UNDERSTANDING INSTITUTIONAL DIVERSITY

UNDERSTANDING INSTITUTIONAL
DIVERSITY

Elinor Ostrom

PRINCETON UNIVERSITY PRESSPRINCETON AND OXFORD

Copyright © 2005 by Princeton University Press
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Understanding institutional diversity / Elinor Ostrom.
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ISBN-13: 978-0-691-12207-6 (cloth : alk. paper)ISBN-10: 0-691-12207-5 (cloth : alk. paper)ISBN-13: 978-0-691-12238-0 (pbk. : alk. paper)ISBN-10: 0-691-12238-5 (pbk. : alk. paper)
1. Diversity in the workplace. 2. Multiculturalism. 3. Interpersonal
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To the fabulous colleagues, students, visiting scholars,
and staff who have made scholarship at the Workshop
over the past thirty years highly productive, challenging,
and rewardingContents

List of Illustrationsxi

Acknowledgments

xiiiPART I: AN OVERVIEW OF THE INSTITUTIONAL ANALYSISAND DEVELOPMENT (IAD) FRAMEWORK1

One
Understanding the Diversity of Structured Human Interactions

3

Diversity: A Core Problem in Understanding Institutions
Is There an Underlying Set of Universal Building Blocks?
Holons: Nested Part-Whole Units of Analysis
Action Arenas as Focal Units of Analysis
Zooming Out to an Overview of the IAD Framework
Viewing Action Arenas as Dependent Variables
Institutional Frameworks, Theories, and Models
The Limited Frame of This Book

4
5
11
13
15
16
27
29

Two
Zooming In and Linking Action Situations
An Action Situation as a Focal Unit of Analysis
Example of a Simple Action Situation
The Basic Working Parts of Action Situations
Linking Action Arenas
Predicting Outcomes
Evaluating Outcomes
Three
Studying Action Situations in the Lab
The Trust Game in the Experimental Laboratory
A Commons Dilemma in the Experimental Laboratory
Structural Changes in the Laboratory
Replications and Extensions of Commons Dilemma
Experiments
Conclusions
Four
Animating Institutional Analysis
Animating Open, Competitive Processes

32
32
35
37
55
64
66
69
70
78
85
93
97
99
100

viii

CONTENTSThe Challenge of Imperfect Information
Assumptions Used in Animating Participants
Variety and Complexity: An Asset or a Liability?
A Focus on Collective Action to Overcome Social Dilemmas
Norms Fostering Collective Action
Emergence and Survival of Norms in Evolutionary Processes
Conclusion

101
103
116
119
121
125
131

PART II: FOCUSING ON RULES

135

Five
A Grammar of Institutions, Sue Crawford and Elinor Ostrom
Parsing Institutional Statements
The Syntax of a Grammar of Institutions
The Syntax Components
Applying the Grammar
Using the Grammar in Empirical Field ResearchSome Next Steps

137
137
139
140
152
171
173

Six
Why Classify Generic Rules?
Solving Babbling Equilibrium Problems
The Policy Analyst’s Need to Understand How to
Reform Situations
Moving beyond Slogan Words to Describe Institutions
Coping with the Immense Diversity by Identifying
Generic Rules
The Role of Rules as Information Transformation
Mechanisms
An Underlying Universality?
Seven
Classifying Rules, Elinor Ostrom and Sue Crawford
The Horizontal Approach: Classifying by the AIM of a Rule
Position Rules
Boundary Rules
Choice Rules
Aggregation Rules
Information Rules
Payoff Rules
Scope Rules
Default Conditions: What Happens if No Rules Exist
Related to Components of an Action Situation?

175
176
180
181
181
184
185
186
187
193
194
200
202
206
207
208
210

CONTENTS

The Vertical Approach: Operational, Collective-Choice,
and Constitutional-Choice Levels of Analysis
Using Rules as Tools to Change Outcomes

ix

214
215

PART III: WORKING WITH RULES

217

Eight
Using Rules as Tools to Cope with the Commons219

Field Research on Common-Pool Resources
What Rules Are Found in Self-Organized Common-Pool
Resource Regimes?
Contemporary Approaches to Resource Policy
Coping with Complexity: A General Problem
Changing Rules as an Adaptive Process
Theoretical Puzzles
Summing Up
Nine
Robust Resource Governance in Polycentric Institutions
Design Principles and Robust Social-Ecological Systems
Threats to Robust Governance of Common-Pool Resources
Modest Coping Methods for Dealing with Threats to
Sustainability
The Advantage and Limits of Polycentric Systems in Copingwith Design and Long-Term Sustainability of Systems
The Capabilities of Polycentric Systems in Coping with
Tragedies of the Commons
Conclusion

221
222
236
242
243
251
253
255
258
271
279
281
283
287

Notes

289

References

307

Index

351

Illustrations

Figures
1.1 The focal level of analysis—an action arena
1.2 A framework for institutional analysis
1.3 Four basic types of goods
2.1 The internal structure of an action situation
2.2 The Snatch Game
2.3 Levels of analysis and outcomes
2.4 Relationships of formal and informal collective-choice arenas
3.1 The Trust Game
3.2 Trust and reciprocity under three experimental conditions
4.1 The relationship between information, action-outcome
linkages, and internal mental models
4.2 The impact of communication, vividness, and salience on
the relationship between information, action-outcome
linkages, and internal mental models
4.3 The Snatch Game with norms
5.1 Authorized relationships: authority to act
5.2 Base game
5.3 Repeated game with shared strategies
5.4 Game with a norm and monitoring
5.5 Equilibrium diagram: game with a norm and monitoring
5.6 Game with a rule
7.1 Rules as exogenous variables directly affecting the elementsof an action situation
7.2 The Snatch Game with minimal property rights

13
15
24
33
36
59
62
71
77
105

108
122
145
156
157
159
160
163
189
213

Tables
2.1 Action sets assigned to positions in several “typical” action
situations
2.2 Results if collective-choice rules specify that a tie voteretains the status quo
2.3 Results if collective-choice rules specify that a tie vote leads
to a change
3.1 Summary results across environments
4.1 Description of various decision strategies

46
47
47
90
115

xii

I L L U S T R AT I O N S

5.1 Game summaries
5.2 Delta parameters and normative concepts used in recent
literature
6.1 Shared strategies, norms, and rules as used in ADICO syntax
and in recent literature
7.1 The AIM component of each type of rule
7.2 Default conditions
7.3 Rules changed to create elementary property rights for
agricultural commodities
8.1 Attributes and conditions used in boundary rules to deﬁne
who is authorized to appropriate from a common-poolresource
8.2 Choice rules used to allocate common-pool resources
8.3 Water allocation rules in Chitwan and Tanahun districts

155
169
178
191
211
212

224
229
232

Acknowledgments

ANY BOOK DRAWING on unpublished and published papers that an author
has drafted and thought about for multiple decades requires many acknowledgments. As I mention in several places in the volume, sections of
this book were started in the 1980s when I was a research scholar at theCenter for Interdisciplinary Research at Bielefeld University in Germany.
I was fortunate enough to visit Bielefeld during 1982 and again in 1988.
The opportunity to express some of my early thinking about nested sets ofrules and action situations with colleagues participating in both Bielefeld
groups is deeply appreciated and helped me immensely. I can remember
the puzzlement of several colleagues when I decided to entitle a lecture
given at Bielefeld in 1982 “The Hidden Structure behind the Structure.”
In some respects, one may think of this book as my effort to uncover that
hidden structure and to answer their many questions.
My major home base throughout this entire period, of course, has been
the Workshop in Political Theory and Policy Analysis at Indiana University, Bloomington. Many earlier draft sections of what is now in this bookhave been presented at our colloquiums, at miniconference sessions, andin various working group meetings (e.g., the CPR Rules Coding Group,
the Evolutionary Theory Working Group) at the Workshop. It has been
an intellectual home for me where one could present radical ideas and getthem taken seriously at the same time as they are being challenged anddebated. The support of the Workshop by Indiana University is deeply
appreciated.
Vincent Ostrom cofounded the Workshop with me more than thirty
years ago, and his ideas permeate all that we do at the Workshop. Noneof us could have achieved what we have done without his inspiration,
criticism, encouragement, and help, and there is no way of really adequately thanking him for years of lively discussions about the theoreticalissues discussed in this book.
Throughout this period, we have been fortunate to receive funding
from the National Science Foundation (SES-8619498; SES-8921888;
SBR-9521918; SES-0083511), the Ford Foundation (950–1160–1), andthe MacArthur Foundation (00–63798-GSS). Work with colleagues in
the Resilience Alliance (supported by the McDonnell Foundation) has
proved important during recent years and has helped to stimulate workon the last sections of the book. I was fortunate to spend a month withcolleagues at the Max Planck Center for the Study of Common Goods in
Bonn, Germany, during 2001. The staff there was very helpful and as-xiv

ACKNOWLEDGMENTS

sisted me in getting a number of notes that had been written in the 1980s
into text form.
In recent years, I have given a number of seminars at different universities on various sections of this book. These include: the Sanford Schoolof Public Policy at Duke University in November 2001; the Departmentof Political Science, University of California, San Diego, in March 2002;
as part of the Walker-Ames Lecture Series at the University of Washington
in April 2002; Kathmandu University in April 2002; the Department of
Economics and the Complex Systems Group at the University of Michigan in November 2002; the Max Planck Institute in Jena, Germany, in
June 2003; Purdue University in October 2004; Cornell University in November 2004; Case Western University in January 2005; and at the Workshop itself on October 18, 2004, as well as in my seminar during the fallsemesters of 2003 and 2004.
During the many years that colleagues and I have been trying to understand institutions, the occasion to write a number of papers and chaptershave enabled coauthors and me to develop initial versions of many of thearguments in this volume. Thus, there are partial sections of earlier papers
that are drawn on in multiple chapters of this volume, including:
Chapters 1 and 2: Elinor Ostrom, “Institutional Rational Choice: An
Assessment of the Institutional Analysis and Development Framework,”in Theories of the Policy Process, ed. Paul A. Sabatier (Boulder, CO: Westview Press, 1999), pp. 35–71; and Elinor Ostrom, “Doing Institutional
Analysis: Digging Deeper Than Markets and Hierarchies,” in the Handbook of New Institutional Economics, ed. Claude Ménard and Mary Shirley (Dordrecht, the Netherlands: Springer, 2005), pp. 797–926.
Chapter 4: Elinor Ostrom, “Collective Action and the Evolution of Social Norms,” Journal of Economic Perspectives 14 (3) (2000): 137–58.
Chapter 5: Sue E. S. Crawford and Elinor Ostrom, “A Grammar of Institutions,” American Political Science Review 89 (3) (September 1995): 582–
600. (Reprinted with the permission of Cambridge University Press.) Reprinted in Polycentric Games and Institutions: Readings from the Workshop in Political Theory and Policy Analysis, ed. Michael McGinnis (AnnArbor: University of Michigan Press, 2000), pp. 114–55. Figures and tables
reprinted with the permission of the University of Michigan Press.
Chapter 8: Elinor Ostrom, “Reformulating the Commons,” in Protecting the Commons: A Framework for Resource Management in theAmericas, ed. Joanna Burger, Elinor Ostrom, Richard B. Norgaard, David
Policansky, and Bernard D. Goldstein (Washington, D.C.: Island Press,
2001), pp. 17–41.
Chapters 8 and 9: Elinor Ostrom, “Coping with Tragedies of the Commons,” Annual Review of Political Science 2 (1999): 493–535. (Reprinted with permission of the Annual Review of Political Science.)ACKNOWLEDGMENTS

xv

Chapter 9: Elinor Ostrom, “Institutional Analysis, Design Principles,
and Threats to Sustainable Community Governance and Management ofCommons,” in Law and the Governance of Renewable Resources: Studies from Northern Europe and Africa, ed. Erling Berge and Nils ChristianStenseth (Oakland, CA: ICS Press, 1998), pp. 27–53.
As I work on this book, I am deeply appreciative of the long conversations that I have had with colleagues about the Institutional Analysis andDevelopment (IAD) framework, which many of us have had a direct rolein creating. The many conversations and memos shared with Larry Kiser
and Sue Crawford are particularly important in affecting my own thinking, and working with Roy Gardner and James Walker to model theseideas using game theory and build experimental games to test our predictions has been invaluable. Roger Parks has repeatedly given me extensive
comments on earlier manuscripts that have challenged me to work still
harder to explain the concepts. Marco Janssen and Mike McGinnis haveboth read and commented extensively on sections of this manuscript.
Working with T. K. Ahn on multiple papers has also added to my understanding. Chuck Myers at Princeton University Press has given me many
useful suggestions and has been extremely helpful through the ﬁnal yearof moving an amorphous manuscript toward being a real book. Cindy
Crumrine, who copyedited this book for Princeton University Press, didan outstanding job. The anonymous reviewers for Princeton University
Press also provided useful advice. Eric Coleman has read through the ﬁnal
draft with a critical and useful eye and developed the index for this book.
Without Patty Lezotte’s excellent editing skills, thoughtful insights, andcheerful help under high stress, I do not know how I could have ﬁnishedthis book.
In addition, there have been a number of people who have given one
or more chapters a very serious read and given me comments. These include: Ryan Adams, Arun Agrawal, Alexander Alexeev, Marty Anderies,
Krister Andersson, Ed Araral, Robert Axelrod, Brian Bartels, Avner Benner, Robert Bish, Juan-Camilo Cardenas, Robert Christensen, DanielCole, Eric Coleman, Meritxell Costeja, Shanon Donnelly, Ginger Elliot,
Ernst Fehr, Susan Fitzpatrick, Bruno Frey, Norman Frohlich, Jessica
Gerrity, Gerd Gigerenzer, Werner Güth, Tobias Haller, Grant Hemphill,
Pamela Jagger, Kenneth Koford, Kudan Kumar, Wai Fung Lam, Joe
Oppenheimer, Mike Radcliffe, Eric Rasmusen, Bo Rothstein, Fritz
Scharpf, John Schiemann, Edella Schlager, Ganesh Shivakoti, Shui YanTang, Ingela Ternstrom, Catherine Tucker, Frank Van Laerhoven,
Tommaso Vitale, James Wilson, Lihau Yang, and Eric Zeemering.

Part I
A N O V E RV I E W O F T H E I N S T I T U T I O N A L
A N A LY S I S A N D D E V E L O P M E N T ( I A D )FRAMEWORKOne
Understanding the Diversity of Structured
Human Interactions

TO UNDERSTAND institutions one needs to know what they are, how andwhy they are crafted and sustained, and what consequences they generate
in diverse settings. Understanding anything is a process of learning what
it does, how and why it works, how to create or modify it, and eventually
how to convey that knowledge to others. Broadly deﬁned, institutions are
the prescriptions that humans use to organize all forms of repetitive andstructured interactions including those within families, neighborhoods,
markets, ﬁrms, sports leagues, churches, private associations, and governments at all scales. Individuals interacting within rule-structured situations face choices regarding the actions and strategies they take, leading
to consequences for themselves and for others.
The opportunities and constraints individuals face in any particularsituation, the information they obtain, the beneﬁts they obtain or are excluded from, and how they reason about the situation are all affected by
the rules or absence of rules that structure the situation. Further, the rules
affecting one situation are themselves crafted by individuals interacting
in deeper-level situations. For example, the rules we use when driving
to work every day were themselves crafted by ofﬁcials acting within thecollective-choice rules used to structure their deliberations and decisions.
If the individuals who are crafting and modifying rules do not understandhow particular combinations of rules affect actions and outcomes in aparticular ecological and cultural environment, rule changes may produce
unexpected and, at times, disastrous outcomes.
Thus, understanding institutions is a serious endeavor. It is an endeavorthat colleagues and I at the Workshop in Political Theory and Policy Analysis have been struggling with for at least three decades.1 After designing
multiple research projects; writing numerous articles; developing ideas in
the classroom; learning from eminent scholars in the ﬁeld, from students,and from colleagues; and making diverse attacks on this problem, it istime to try to put thoughts on this subject together within the covers of abook, even though I am still not fully satisﬁed with my own understanding. Consider this a progress report on a long-term project that will be
continued, I hope, by many others into the future.

4

CHAPTER ONE

Diversity: A Core Problem in Understanding Institutions
A major problem in understanding institutions relates to the diversity ofsituations of contemporary life. As we go about our everyday life, weinteract in a wide diversity of complex situations. Many of us face a morning and evening commute where we expect that others, who are travelingat great speeds, will observe the rules of the road. Our very lives dependon these expectations. Others depend on our own driving behavior conforming in general to locally enforced rules about speeding, changing
lanes, and turn-taking behavior at intersections. Those of us who workin large organizations—universities, research centers, business ﬁrms, government ofﬁces—participate in a variety of team efforts. In order to do
our own work well, we are dependent on others to do their work creatively, energetically, and predictably, and vice versa. Many of us playsports at noontime, in the early evening, or on the weekends. Here again,
we need to learn the basic rules of each of the games we play as well as
ﬁnd colleagues with whom we can repeatedly engage in this activity. During the average week, we will undertake activities in various types of market settings—ranging from buying our everyday food and necessities toinvesting funds in various types of ﬁnancial instruments. And we will
spend some hours each week with family and friends in a variety of activities that may involve worship, helping children with homework, taking
care of our homes and gardens, and a long list of other activities undertaken with family and friends.
Somehow as individuals we implicitly make sense of most of these diverse and complex situations. We do so even today, with all of the newopportunities and risks that were not even conceivable a few generations
ago. We now expect to watch the Olympic games and other international
competitions as they happen, no matter where they are located or where
we are in the world. We have become accustomed to buying bananas,oranges, and kiwi fruit at any time of the year in almost any market weenter around the globe. Not only do millions of us drive to work regularly,
many of us also ﬂy to other parts of the globe on a regular basis, trusting
our lives to the knowledge and skills of pilots to know and utilize themany do’s and don’ts of ﬂying airplanes.
If we are considered to be adults and sane, we are expected to be able
to reason about, learn, and eventually know what to do in many diverse
situations that we confront in today’s world. We know that when we are
shopping in a supermarket that we can take a huge variety of goods off
the shelf and put them in a pushcart. Before we put these same goods inour car, however, we need to line up at a counter and arrange to pay forthem using cash or a credit card (something else that was not so widely
available a few years ago). When we are shopping in an open bazaar inDIVERSITY AND STRUCTURED INTERACTIONS

5

Asia or Africa, however, the do’s and don’ts differ. If we go at the end of
the market day, we may bargain over the price of the fruit that is left on
the stand—something we could never do in a supermarket where fruit
will be refrigerated overnight. If we are in the household goods sectionof the bazaar, vendors would be astounded if we did not make several
counteroffers before we purchased an item. Try that in a furniture store
in a commercial district of a Western country, and you would ﬁnd yourself
politely (or not so politely) told to leave the establishment. Thus, there
are many subtle (and not so subtle) changes from one situation to anothereven though many variables are the same.
These institutional and cultural factors affect our expectations of the
behavior of others and their expectations of our behavior (Allen 2005).
For example, once we learn the technical skills associated with driving acar, driving in Los Angeles—where everyone drives fast but generally follows trafﬁc rules—is quite a different experience from driving in Rome,
Rio, and even in Washington, D.C., where drivers appear to be playing a
blufﬁng game with one another at intersections rather than following trafﬁc rules. When playing racquetball with a colleague, it is usually okay tobe aggressive and to win by using all of one’s skills, but when teaching ayoung family member how to play a ball game, the challenge is how to
let them have fun when they are ﬁrst starting to learn a new skill. Being
too aggressive in this setting—or in many other seemingly competitive
situations—may be counterproductive. A “well-adjusted and productive”
adult adjusts expectations and ways of interacting with others in situations that occur in diverse times and spaces.
Our implicit knowledge of the expected do’s and don’ts in this variety
of situations is extensive. Frequently, we are not even conscious of all of
the rules, norms, and strategies we follow. Nor have the social sciencesdeveloped adequate theoretical tools to help us translate our implicit
knowledge into a consistent explicit theory of complex human behavior.
When taking most university courses in anthropology, economics, geography, organization theory, political science, psychology, or sociology, welearn separate languages that do not help us identify the common workparts of all this buzzing confusion that surrounds our lives. Students frequently complain—and justiﬁably so—that they have a sense of being in a
Tower of Babel. Scholars also see the same problem (V. Ostrom 1997, 156).

Is There an Underlying Set of Universal Building Blocks?
The core questions asked in this book are: Can we dig below the immense
diversity of regularized social interactions in markets, hierarchies, families, sports, legislatures, elections, and other situations to identify universal building blocks used in crafting all such structured situations? If

6

CHAPTER ONE

so, what are the underlying component parts that can be used to build
useful theories of human behavior in the diverse range of situations inwhich humans interact? Can we use the same components to build an
explanation for behavior in a commodity market as we would use to
explain behavior in a university, a religious order, a transportation
system, or an urban public economy? Can we identify the multiple levelsof analysis needed to explain the regularities in human behavior that
we observe? Is there any way that the analyses of local problem solving,such as the efforts of Maine lobster ﬁshers for the last eighty years toregulate their ﬁsheries (see Acheson 1988, 2003; Wilson 1990), can beanalyzed using a similar set of tools as problem solving at a national
level (Gellar 2005; McGinnis forthcoming; Sawyer 2005) or at aninternational level (Gibson, Anderson, et al. 2005; O. Young 1997,
2002)?
My answer to these questions is yes. This answer is, of course, a conjecture and can be challenged. Asserting that there is an underlying unity iseasy. Convincing others of this is more difﬁcult. I welcome exchanges withothers concerning the fundamental building blocks of organized humaninteractions.
Many Components in Many Layers
The diversity of regularized social behavior that we observe at multiple
scales is constructed, I will argue, from universal components organizedin many layers. In other words, whenever interdependent individuals arethought to be acting in an organized fashion, several layers of universal
components create the structure that affects their behavior and the outcomes they achieve. I give a positive answer to these questions based on
years of work with colleagues developing and applying the Institutional
Analysis and Development (IAD) framework.2
Helping others to see the usefulness of developing a multilevel taxonomy of the underlying components of the situations human actors face isthe challenge that I undertake in this volume. Scholars familiar with theworking parts used by mathematical game theorists to describe a game
will not be surprised by the positive answer. To analyze a game, the theorist must answer a series of questions regarding universal components ofa game, including the number of players, what moves they can take, what
outcomes are available, the order of decisions, and how they value moves
and outcomes.
On the other hand, game theorists will be surprised at the extremely
large number of components identiﬁed in this book that create the context
within which a game is played. Further, if one drops the use of a universal,
simpliﬁed model of the individual, the number of options that a theorist

DIVERSITY AND STRUCTURED INTERACTIONS

7

must self-consciously make is even larger than experienced in the past.
While the usefulness of a universal model of rational behavior is challenged in chapter 4, the assumption of a universal framework composedof nested sets of components within components for explaining humanbehavior is retained throughout the book.
Building a Framework
Game-theoretical analysis is drawn on and expanded in this book in several ways. First, I do not conﬁne analysis to those situations that are simple enough to be analyzed as formal games. The core concept of an action
situation (discussed in chapters 2 and 3) can be formalized as a mathematical game to represent many simple and important situations. Many othersigniﬁcant situations—particularly where rules are the object of choice—
are too complex to be modeled as a simple game. (Agent-based models
and simulations of diverse types will provide the modeling tools we needto capture patterns of interaction and outcomes in many of these more
complex settings [Janssen 2003].)
Second, I dig further to develop a consistent method for overtly analyzing the deeper structures that constitute any particular action situation.
For some game theorists, this deeper structure is irrelevant once the structure of a game itself is made explicit. Third, the narrow model of humanbehavior used in game theory is viewed as one end of a continuum ofmodels of human behavior appropriate for institutional analysis. The
three basic assumptions of that model are used as a foundation for specifying the type of assumptions that a theorist needs to make when animating an institutional analysis.
The challenge for institutional theorists—as I discuss in chapter 4—is toknow enough about the structure of a situation to select the appropriate
assumptions about human behavior that ﬁt the type of situation underanalysis. Thus, the approach presented here encompasses contemporary
game theory as one of the theories that is consistent with the IAD framework. Also included, as discussed in chapter 4, are broader theories thatassume individuals are fallible learners trying to do the best they can in
the long term by using norms and heuristics in making their immediate
decisions.
As a scholar committed to understanding underlying universal components of all social systems, I do not introduce complexity lightly. I view
scientiﬁc explanation as requiring just enough variables to enable one to
explain, understand, and predict outcomes in relevant settings. Thus, formany questions of interest to social scientists, one does not need to dig
down through nested layers of rules that are examined in the last half of
this book. One can develop a good analysis of the situation (chapters 2

8

CHAPTER ONE

and 3), decide what assumptions to make about participants (chapter 4),
and predict outcomes. If the predictions are supported empirically, that
may be all that is needed.3
If the predictions are not supported, however, as is the case with muchcontemporary work on social dilemmas and settings involving trust andreciprocity, one has to dig under the surface to begin to understand why.
And if one wants to improve the outcomes achieved over time, one is
faced with the need to understand the deeper structure in the grammar of
institutions discussed in chapter 5 and the types of rules used to create
structure as discussed in chapters 7 and 8. This volume can be viewed aspresenting a series of nested conceptual maps of the explanatory spacethat social scientists can use in trying to understand and explain the diversity of human patterns of behavior. Learning to use a set of conceptual
maps and determining the right amount of detail to use is, however, itself
a skill that takes some time to acquire just as it does with geographic
maps (see Levi 1997b).
Frameworks and Conceptual Maps
For example, if I want to know the quickest route from Providence Bay
to Gore Bay on the Manitoulin Island, where Vincent Ostrom and I spend
summers writing at our cabin on the shores of Lake Huron, I need a very
detailed map of the interior of the island itself. If I want to explain where
the Manitoulin Island is to a colleague—who wants to know where we
spend our summers—I need a less detailed and larger map that shows its
location on the northern shores of Lake Huron, one of the Great Lakes of
the North American continent. If I try to use a map of the entire Western
Hemisphere, however, the Great Lakes are all so small that locating theManitoulin Island itself may be a challenge. I may only be able to point
to the Province of Ontario in Canada, where it is located, or to the entire
set of the Great Lakes. The advantage of a good set of geographic maps
is that after centuries of hard work, multiple levels of detailed maps of
most places are available and are nested in a consistent manner within
one another. Most of us recognize that there is not one optimal map
that can be used for all purposes. Each level of detail is useful for different
purposes.
The “map” that I will elucidate in this volume is a conceptual framework called, as mentioned above, the Institutional Analysis and Development (IAD) framework. The publication of “The Three Worlds of Action:
A Metatheoretical Synthesis of Institutional Approaches” (Kiser and Ostrom 1982) represented the initial published attempt to describe the IAD
framework. Our goal was to help integrate work undertaken by politicalscientists, economists, anthropologists, lawyers, sociologists, psycholo-DIVERSITY AND STRUCTURED INTERACTIONS

9gists, and others interested in how institutions affect the incentives confronting individuals and their resultant behavior.4 During the time since
this publication, the framework has been developed further5 and applied
to analyze a diversity of empirical settings. These include:• the study of land boards in Botswana (Wynne 1989);• the impact of institutions on creating effective monitoring and evaluations
in government development projects (Gordillo and Andersson 2004);• the incentives of operators and state government regarding coal roads in
Kentucky (Oakerson 1981);• the evolution of coffee cooperatives in Cameroon (Walker 1998);• the causes and effects of property-right changes among the Maasai of Kenya(Mwangi 2003);• the performance of housing condominiums in Korea (J. Choe 1992);• the regulation of the phone industry in the United States (Schaaf 1989);• the effect of rules on the outcomes of common-pool resource settings
throughout the world (Oakerson 1992; Blomquist 1992; E. Ostrom 1990,
1992b; Agrawal 1999; Schlager 1994, 2004; Tang 1992; E. Ostrom, Gardner, and Walker 1994; Lam 1998; de Castro 2000; Dolšak 2000; Futemma
2000; Yandle 2001; Gibson, McKean, and Ostrom 2000);• a comparison of nonproﬁt, for proﬁt, and government day-care centers (Bushouse 1999);• the impact of decentralization on forest governance in Bolivia (Andersson
2002, 2004);• the evolution of banking reform in the United States (Polski 2003); and• the effect of incentives on donor and recipient behavior related to international aid (Gibson, Anderson et al. 2005).

Our conﬁdence in the usefulness of the IAD framework has grownsteadily in light of the wide diversity of empirical settings where it hashelped colleagues identify the key variables to undertake a systematic
analysis of the structure of the situations that individuals faced andhow rules, the nature of the events involved, and community affected
these situations over time. What is certainly true is that the number of
speciﬁc variables involved in each of these empirical studies is verylarge. The speciﬁc values of variables involved in any one study (or one
location in a study) differ from the speciﬁc values of variables involved inanother study.
The problem of many variables, and potentially few instances of any
one combination of these variables, has been recognized by other scholars
as one of the perplexing problems haunting systematic empirical testingof social science theories. James Coleman (1964, 516–19) referred to thedevelopment and testing of “sometimes true theories,” by which he meantthat explanations were likely to hold under speciﬁc conditions and not

10

CHAPTER ONE

under others. If a small number of conditions were identiﬁed, sometimes
true theories would not present a major problem for the social sciences.
Rigorous analysis of many important questions, however, does eventually require examining a large number of variables. Viewing macropolitical orders in developed Western societies, for example, Fritz Scharpf(1997, 22) points out that the national institutional settings “known toaffect policy processes can be described as being either unitary or federal,parliamentary or presidential, have two- or multi-party systems in whichinteractions are competitive or consociational, and with pluralist or neocorporatist systems of interest intermediation.” Each one of the ﬁve variables can exist in one or the other “setting” independently of the otherfour variables. And, to make it worse, there may be variables related tothe particular policy area—such as banking, environmental policy, or education—that may also change. “For comparative policy research, thismeans that the potential number of different constellations of situational
and institutional factors will be extremely large—so large, in fact, that it
is rather unlikely that exactly the same factor combination will appear inmany empirical cases” (23). A similar level of complexity exists whenanalyzing factors affecting the performance of city-county consolidation
efforts (Carr and Feiock 2004).
Hammond and Butler (2003) have illustrated this problem clearly intheir critique of the work of some institutional theorists who have madeoverly strong claims for the overarching differences between parliamentary and presidential systems. Presidential systems—according to Burns(1963), Sundquist (1968), and Valenzuela (1993)—are thought to slow, if
not halt, policy change and lead to obstruction, frustration, and deadlock
interspersed with occasional bursts of change when a president faces bothhouses of Congress dominated by his own party. Hammond and Butler
carefully analyze the interaction between rules and the preference proﬁlesthat may exist in ﬁve variations of institutional rules. They conclude “thatconsidering institutional rules alone provides an inadequate guide to the
behavior of any system” (Hammond and Butler 2003, 183).
As Marwell and Oliver (1993, 25) put it, the “predictions that we canvalidly generate must be complex, interactive and conditional.” And, we
can hope that some changes in a component are neutral—or have no impact on outcomes—in at least some settings (as biologists are now learning about in regard to genotypes; see Gavrilets 2003). While verifying
the empirical warrantability of precise predictions has been the guiding
standard for much of the work in political economy, we may have to
be satisﬁed with an understanding of the complexity of structures and acapacity to expect a broad pattern of outcomes from a structure rather
than a precise point prediction. An outcome consistent with a pattern

DIVERSITY AND STRUCTURED INTERACTIONS

11

may be the best veriﬁcation we can achieve in settings of substantial complexity (Crutchﬁeld and Schuster 2003).
Thus, the many relevant variables, the immense number of combinations of these variables that exist, and their organization into multiple
levels of analysis make understanding organized social life a complex endeavor. If every social science discipline or subdiscipline uses a different
language for key terms and focuses on different levels of explanation as
the “proper” way to understand behavior and outcomes, one can understand why discourse may resemble a Tower of Babel rather than a cumulative body of knowledge. This book is devoted to the task of buildingon the efforts of many scholars to develop a conceptual approach that
hopefully has a higher chance of cumulation than many of the separate
paths currently in vogue in contemporary social sciences.

Holons: Nested Part-Whole Units of Analysis
Like good geographic maps, the IAD framework can be presented atscales ranging from exceedingly ﬁne-grained to extremely broad-grained.
Human decision making is the result of many layers of internal processing
starting with the biophysical structure, but with layers upon layers ofcognitive structure on top of the biophysical components (Hofstadter1979). Further, many of the values pursued by individuals are intrinsic
values that may not be represented by external material objects, and theirpresence and strength are important parts of the individual to be examined. Building on top of the single individual are structures composed ofmultiple individuals—families, ﬁrms, industries, nations, and many otherunits—themselves composed of many parts and, in turn, parts of stilllarger structures. What is a whole system at one level is a part of a system
at another level.
Arthur Koestler (1973) refers to such nested subassemblies of partwhole units in complex adaptive systems as holons. “The term holon may
be applied to any stable sub-whole in an organismic or social hierarchy,
which displays rule-governed behaviour and/or structural Gestalt constancy” (291). Christopher Alexander (1964) earlier conceptualized allcomponents of social arrangements as having a pattern and being a unit.
Units have subunits and are themselves parts of larger units that ﬁt together as a pattern. Koestler asserts that a “hierarchically organized wholecannot be ‘reduced’ to its elementary parts; but it can be ‘dissected’ into
its constituent branches on which the holons represent the nodes of thetree, and the lines connecting them the channels of communication, control or transportation, as the case may be” (1973, 291). Thus, much of
the analysis presented in this book will be a form of “dissecting” complex

12

CHAPTER ONE

systems into composite holons that are then dissected further. Explanations occur at multiple levels and different spatial and temporal scales.
Because explanations occur at multiple levels and different spatial andtemporal scales, the relevant theoretical concepts needed to understandphenomena at one level do not necessarily scale up or down. One of thecore puzzles facing the ﬁeld of landscape ecology, for example, is the problem of identifying the scale at which a process or phenomenon occurs.
According to Pickett and Cadenasso (1995, 333), “The basic question
about scale in ecology consists of determining whether a given phenomenon appears or applies across a broad range of scales, or whether it islimited to a narrow range of scales” (see also S. Levin 1992).
The parts used to construct a holon are frequently not descriptive of
the holon they have created. A house is constructed out of ﬂoor joists,
roof beams, lumber, rooﬁng material, nails, and so forth. When one wants
to talk about the house itself, one usually talks about the number ofrooms, the style of the house, the number of stories, rather than the number of nails used in construction—even though a contractor and a hardware salesperson may try to estimate exactly this variable at some point
during construction. When one wants to talk about the street on which
the house is located, one uses terms such as the size of the lots, the widthof the road, the complementarity or lack of complementarity of the building style, and the like. Descriptions of a neighborhood will use still different concepts, as will a description of an urban or rural political jurisdiction in which a neighborhood is located. On the other hand, someconcepts can be used to dissect holons operating at different scales ofanalysis.
Consequently, the institutional analyst faces a major challenge in identifying the appropriate level of analysis relevant to addressing a particular
puzzle and learning an appropriate language for understanding at leastthat focal level and one or two levels above and below that focal level. It
is not only social scientists who face this problem. At a meeting of the
global change scientists held in Bonn in March of 2001, Peter Lemke ofthe World Climate Research Project indicated that the earlier emphasis in
climate research was all on global weather forecasts. This has proved to
be a myth and a delusion. Now they recognize that to do good weather
forecasting, one has to have detailed local models supplemented by global
weather models. Both local and global are needed. They are complementary rather than competitive. Physical scientists are trying now in theirglobal models to integrate some of the more localized conditions, but thatturns out to be very difﬁcult.
Ecologists have struggled with understanding ecological systems composed of communities, modular units within communities, subunits
within these, and attributes of the species in a community (such as diver-DIVERSITY AND STRUCTURED INTERACTIONS

Exogenous Variables

ActionArena

13Interactions
Evaluative
Criteria
Outcomes

Figure 1.1 The focal level of analysis—an action arena.

sity) or of individual species (such as trophic level) (see Tilman 1999;
Tilman, Lehman, and Bristow 1998). Extensive ﬁeld research, analyticalmodeling, and simulations now enable ecologists to make relatively
strong predictions about some of these interactions. “Increasing species
diversity is likely associated with more complex community structure, as
species with unique ecological roles are added. The introduction of new
ecological roles may be stabilizing or destabilizing, depending on how
species function within the community. For example, the addition of athird trophic level to an otherwise stable community with only prey andpredators could potentially destabilize the system” (Ives, Klug, and Goss
2000, 409). Social scientists are slowly gaining greater capabilities forunderstanding multilevel complex systems, but until we develop the appropriate theoretical language for analyzing these systems, we will continue to condemn all complex communities of interacting human organizations as chaotic, as was the dominant view of urban scholars during thelast half-century (see, for example, Hawley and Zimmer 1970).

Action Arenas as Focal Units of Analysis
The focal level for this book is the holon called an action arena in which
two holons—participants and an action situation—interact as they areaffected by exogenous variables (at least at the time of analysis at thislevel) and produce outcomes that in turn affect the participants and theaction situation. Action arenas exist in the home; in the neighborhood;in local, regional, national, and international councils; in ﬁrms and markets; and in the interactions among all of these arenas with others. The
simplest and most aggregated way of representing any of these arenas
when they are the focal level of analysis is shown in ﬁgure 1.1, where
exogenous variables affect the structure of an action arena, generatinginteractions that produce outcomes. Evaluative criteria are used to judge
the performance of the system by examining the patterns of interactions
and outcomes.
Outcomes feed back onto the participants and the situation and may
transform both over time. Over time, outcomes may also slowly affect

14

CHAPTER ONE

some of the exogenous variables. In undertaking an analysis, however,
one treats the exogenous variables as ﬁxed—at least for the purpose of
the analysis. When the interactions yielding outcomes are productive forthose involved, the participants may increase their commitment to maintaining the structure of the situation as it is, so as to continue to receive
positive outcomes. When participants view interactions as unfair or otherwise inappropriate, they may change their strategies even when they arereceiving positive outcomes from the situation (Fehr and Gächter 2000b).
When outcomes are perceived by those involved (or others) as less valued
than other outcomes that might be obtained, some will raise questions
about trying to change the structure of the situations by moving to adifferent level and changing the exogenous variables themselves. Or, if theprocedures were viewed as unfair, motivation to change the structure may
exist (Frey, Benz, and Stutzer 2004).
Similar efforts to identify a core unit of analysis, such as the action
arena, that is contained in many diverse environments have a long history.
Core units of analyses identiﬁed by other scholars include:• collective structures (Allport 1962);• events (Appleyard 1987; Heise 1979);• frames (Goffman 1974);• social action and interaction settings (Burns and Flam 1987);• logic of the situation (Farr 1985; Popper 1961, 1976);• problematic social situations (Raub and Voss 1986);• scripts (Schank and Abelson 1977);• transactions (Commons [1924] 1968); and• units of meaning (Barwise and Perry 1983; Raiffa 1982).

Because the IAD framework is a multitier conceptual map, the simplest
schematic representation of an action arena shown in ﬁgure 1.1 will be
unpacked—and then further unpacked and unpacked throughout the initial chapters of this book. Action arenas include two holons: an action
situation and the participant in that situation (see ﬁgure 1.2). An action
situation can, in turn, be characterized using seven clusters of variables:(1) participants (who may be either single individuals or corporate
actors), (2) positions, (3) potential outcomes, (4) action-outcome linkages, (5) the control that participants exercise, (6) types of information
generated, and (7) the costs and beneﬁts assigned to actions and outcomes(see ﬁgure 2.1 in the next chapter). Thus, an action situation refers to thesocial space where participants with diverse preferences interact, exchange goods and services, solve problems, dominate one another, or ﬁght
(among the many things that individuals do in action arenas). In chapter
2, we will zoom in and unpack the action situation as a focal unit ofanalysis. We will illustrate the working parts of an action situation in

15

DIVERSITY AND STRUCTURED INTERACTIONS

Exogenous Variables
Biophysical/
Material Conditions

Action Arena

Attributes ofCommunity

ActionSituations

Rules

Participants

Interactions
Evaluative
Criteria
Outcomes

Figure 1.2 A framework for institutional analysis. Source: Adapted from E. Ostrom, Gardner, and Walker 1994, 37.

chapter 3, showing how this holon can be operationalized in an experimental laboratory. In chapter 4, we will zoom in to unpack the conceptof a participant and discuss the puzzles and possibilities available to animate the actor. But ﬁrst, let’s use zoom out to examine the variables thatare treated as exogenous when examining an action arena (but may themselves be an outcome of another action arena). Let’s look at a broader
overview of the IAD conceptual map.

Zooming Out to an Overview of the IAD Framework
An institutional analyst can take two additional steps after an effort ismade to understand the initial structure of an action arena leading to aparticular pattern of interactions and outcomes. One step moves outward
and inquires into the exogenous factors that affect the structure of anaction arena. From this vantage point, any particular action arena is now
viewed as a set of dependent variables. The factors affecting the structureof an action arena include three clusters of variables: (1) the rules usedby participants to order their relationships, (2) the attributes of the biophysical world that are acted upon in these arenas, and (3) the structureof the more general community within which any particular arena isplaced (see Kiser and Ostrom 1982). The next section of this chapter
provides a brief introduction to this ﬁrst step (see the left side of ﬁgure1.2). How rules inﬂuence the action arena will then be discussed in much
more depth in chapters 5, 6, and 7.
The second step also moves outward—but to the “other side” of a particular action arena—to look at how action arenas are linked together either
sequentially or simultaneously. This step will be discussed in the last sectionof chapter 2 after discussion of the components of action situations.

16

CHAPTER ONE

Viewing Action Arenas as Dependent Variables
Underlying the way analysts conceptualize action situations and the participants that interact in them are implicit assumptions about the rules participants use to order their relationships, about attributes of the biophysical world, and about the nature of the community within which the arena
occurs. Some analysts are not interested in the role of these underlying
variables and focus only on a particular arena whose structure is given.
On the other hand, institutional analysts may be more interested in one
factor affecting the structure of arenas than they are interested in others.
Anthropologists and sociologists tend to be more interested in how sharedor divisive value systems in a community affect the ways humans organize
their relationships with one another. Environmentalists tend to focus onvarious ways that physical and biological systems interact and create opportunities or constraints on the situations human beings face. Politicalscientists tend to focus on how speciﬁc combinations of rules affect incentives. Rules, the biophysical and material world, and the nature of thecommunity all jointly affect the types of actions that individuals can take,the beneﬁts and costs of these actions and potential outcomes, and thelikely outcomes achieved.
The Concept of Rules
The concept of rules is central to the analysis of institutions (Hodgson
2004a). The term rules, however, is used by scholars to refer to many
concepts with quite diverse meanings. In an important philosophicaltreatment of rules, Max Black (1962) identiﬁed four different usages of
the term in everyday conversations. According to Black, the word rule isused to denote regulations, instructions, precepts, and principles. Whenused in its regulation sense, rules refer to something “laid down by anauthority (a legislature, judge, magistrate, board of directors, university
president, parent) as required of certain persons (or, alternatively, forbidden or permitted)” (115). The example of a rule in the regulation sensethat Black uses is: “The dealer at bridge must bid ﬁrst.” When using rule
in its regulation sense, one can meaningfully refer to activities such as therule “being announced, put into effect, enforced (energetically, strictly,
laxly, invariably, occasionally), disobeyed, broken, rescinded, changed,
revoked, reinstated” (109).
When the term rule is used to denote an instruction, it is closer in meaning to an effective strategy for how to solve a problem. An example ofthis usage is, “In solving quartic equations, ﬁrst eliminate the cubic term”(110). When speaking about a rule in this sense, one would not talk aboutDIVERSITY AND STRUCTURED INTERACTIONS

17

a rule being enforced, rescinded, reinstated, or any of the other activities
relevant to regulation. When rule denotes a precept, the term is being used
as a maxim for prudential or moral behavior. An example would be: “Agood rule is: to put charity ahead of justice” (111). Again, one would not
speak of enforcing, rescinding, or reinstating a rule in the precept sense.
The fourth sense in which the term rule is used in everyday language isto describe a law or principle. An example of this usage is: “Cyclones
rotate clockwise, anticyclones anticlockwise” (113). Principles or physical laws are subject to empirical test, and as such truth values can be
ascribed to them. But physical laws are not put into effect, broken, or
rescinded.
Social scientists employ all four of the uses of the term rule that Black
identiﬁes—and others as well (see discussion in chapter 5). Scholars engaged in institutional analysis frequently use the term to denote a regulation. The deﬁnition of rules used in this book is close to what Black identiﬁed as the regulation sense. Rules can be thought of as the set of
instructions for creating an action situation in a particular environment.
In some ways, rules have an analogous role to that of genes. Genes combine to build a phenotype. Rules combine to build the structure of anaction situation. The property rights that participants hold in diverse settings are a result of the underlying set of rules-in-use (Libecap 1989).
Rules, in the instruction sense, can be thought of as the strategies
adopted by participants within ongoing situations. I will consistently use
the term strategy rather than rule for individual plans of action. Rulesin the precept sense are part of the generally accepted moral fabric of acommunity (Allen 2005). We refer to these cultural prescriptions as
norms. Rules in the principle sense are physical laws.
Until recently, rules have not been a central focus of most of the social
sciences. Even in game theory where “the rules of the game” seem to play
an important role, there has not been much interest in examining whererules come from or how they change. Game-theoretical rules include allphysical laws that constrain a situation as well as rules devised by humans
to structure a situation. The rules of the game—including both physicaland institutional factors—structure the game itself, but have been irrelevant to many game theorists once a game can be unambiguously represented. An inﬂuential contributor to the development of game theory, Anatol Rapoport (1966, 18) stated this distinction very clearly: “Rules areimportant only to the extent that they allow the outcomes resulting fromthe choices of participants to be unambiguously speciﬁed. . . . Any othergame with possibly quite different rules but leading to the same relations
among the choices and the outcomes is considered equivalent to the gamein question. In short, game theory is concerned with rules only to theextent that the rules help deﬁne the choice situation and the outcomes

18

CHAPTER ONE

associated with the choices. Otherwise the rules of games play no part ingame theory.” So long as the game theorist has adequately represented
this focal level of analysis, the theorist interested primarily in ﬁnding thesolution to a game has no need to dig into the rules, attributes of a community, and physical laws that create the structure of the situation. As institutional analysts asked to diagnose why perverse outcomes occur and to
propose ways to improve the outcomes of many action situations, on theother hand, we have to dig below and learn how rules create the set being
analyzed. One cannot improve outcomes without knowing how the structure is itself produced (Eggertsson 2005).
As will be discussed in more depth in chapter 5, rules as used in thisbook are deﬁned to be shared understandings by participants about enforced prescriptions concerning what actions (or outcomes) are required,prohibited, or permitted (Ganz 1971; V. Ostrom 1980; Commons 1968).
All rules are the result of implicit or explicit efforts to achieve order andpredictability among humans by creating classes of persons (positions)
who are then required, permitted, or forbidden to take classes of actions
in relation to required, permitted, or forbidden outcomes or face the likelihood of being monitored and sanctioned in a predictable fashion (V. Ostrom 1991).
Well-understood and enforced rules operate so as to rule out some actions and to rule in others. In a well-ordered human enterprise, somebehaviors are rarely observed because individuals following rules do notnormally engage in that activity in the given setting. It is rare to observe,
for example, that one driver on a public freeway within the United States
will race another driver on that freeway at a speed exceeding one hundred
miles per hour. State highway patrols invest substantial sums in an attempt to enforce highway speeding laws and to rule out excessive speeds
on freeways.
At a racing track, however, one can observe speeds of well over one
hundred miles per hour and drivers directly racing one another in a determined manner. The rules of a racing track rule in some activities that are
ruled out on a freeway. Anyone driving on a freeway will observe a rangeof speeds rather than the single maximum speed mentioned in the speed
limit law. Speed limits illustrate rules that authorize a range of activities
rather than requiring one particular action. Further, enforcement patterns
differ regarding the range of speed in excess of the ofﬁcial upper limit thatwill be tolerated, once observed, before a sanction is issued.
It is also important to recognize that rules need not be written. Nor dothey need to result from formal legal procedures. Institutional rules areoften self-consciously crafted by individuals to change the structure of
repetitive situations that they themselves face in an attempt to improve
the outcomes that they achieve.

DIVERSITY AND STRUCTURED INTERACTIONS

19

ON THE ORIGIN OF RULES

When one is interested in understanding the processes of governance, one
needs to ask where the rules that individuals use in action situations originate. In an open and democratic governance system, many sources existof the rules used by individuals in everyday life. It is not considered illegal
or improper for individuals to self-organize and craft their own rules if
the activities they engage in are legal. In addition to the legislation andregulations of a formal central government, there are apt to be laws
passed by regional and local governments. Within private ﬁrms and voluntary associations, individuals are authorized to adopt many different
rules determining who is a member of the ﬁrm or association, how proﬁts(beneﬁts) are to be shared, and how decisions will be made. Each family
constitutes its own rule-making body.
When individuals genuinely participate in the crafting of multiple layersof rules, some of that crafting will occur using pen and paper. Much ofit, however, will occur as problem-solving individuals interact trying toﬁgure out how to do a better job in the future than they have done in thepast. Colleagues in a work team are crafting their own rules when theymight say to one another, “How about if you do A in the future, and I
will do B, and before we ever make a decision about C again, we both
discuss it and make a joint decision?” In a democratic society, problemsolving individuals do this all the time. They also participate in morestructured decision-making arrangements, including elections to selectlegislators.
WORKING RULES

Thus, when we do a deeper institutional analysis, we attempt ﬁrst to understand the working rules that individuals use in making decisions.
Working rules are the set of rules to which participants would make reference if asked to explain and justify their actions to fellow participants.
While following a rule may become a “social habit,” it is possible to makeparticipants consciously aware of the rules they use to order their relationships. Individuals can consciously decide to adopt a different rule andchange their behavior to conform to such a decision. Over time, behavior
in conformance with a new rule may itself become habitual (see Shimanoff
1980; Toulmin 1974; Harré 1974). The capacity of humans to use complex cognitive systems to order their own behavior at a relatively subconscious level makes it difﬁcult at times for empirical researchers to ascertain
what the working rules for an ongoing action arena may actually be inpractice. It is the task of an institutional analyst, however, to dig undersurface behavior to obtain a good understanding of what rules participants in a situation are following.6

20

CHAPTER ONE

Once we understand the working rules, then, we attempt to understandwhere those rules come from. In an open society governed by a “rule oflaw,” the general legal framework in use will have its source in actions
taken in constitutional, legislative, and administrative settings augmented
by rule-making decisions taken by individuals in many different particularsettings. In other words, the rules-in-form are consistent with the rulesin-use (Sproule-Jones 1993). In a system that is not governed by a “ruleof law,” there may be central laws and considerable effort made to enforce
them, but individuals generally attempt to evade rather than obey the law.
THE PREDICTABILITY OF RULES

Rule following or conforming actions are not as predictable as biologicalor physical behavior explained by scientiﬁc laws. All rules are formulated
in human language. As such, rules share problems of lack of clarity, misunderstanding, and change that typify any language-based phenomenon(V. Ostrom 1980, 1997). Words are always simpler than the phenomenon
to which they refer. In many ofﬁce jobs, for example, the rules require an
employee to work a speciﬁed number of hours per week. If a staff member
is physically at their desk for the required number of hours, is daydreaming about a future vacation or preparing a grocery list for a shopping trip on the way home within the rules? Interpreting rules is morechallenging than writing them down.
The stability of rule-ordered actions is dependent upon the shared
meaning assigned to words used to formulate a set of rules. If no shared
meaning exists when a rule is formulated, confusion will exist about what
actions are required, permitted, or forbidden. Regularities in actions cannot result if those who must repeatedly interpret the meaning of a rule
within action situations arrive at multiple interpretations. Because “rules
are not self-formulating, self-determining, or self-enforcing” (V. Ostrom
1980, 342), it is human agents who formulate them, apply them in particular situations, and attempt to enforce performance consistent with them.
Even if shared meaning exists at the time of the acceptance of a rule,transformations in technology, in shared norms, and in circumstances
more generally change the events to which rules apply. “Applying language to changing conﬁgurations of development increases the ambiguities and threatens the shared criteria of choice with an erosion of theirappropriate meaning” (342).
The stability of rule-ordered relationships is also dependent upon enforcement. According to Commons ([1924] 1968, 138), rules “simply say
what individuals must, must not, may, can, and cannot do, if the authoritative agency that decides disputes brings the collective power of the community to bear upon the said individuals.” Breaking rules is an option that

DIVERSITY AND STRUCTURED INTERACTIONS

21

is always available to participants in an action situation (as contrasted toplayers in a formal game-theoretic model), but associated with breaking
rules is a risk of being monitored and sanctioned. If the risk is low, thepredictability and stability of a situation are reduced. And instability cangrow over time. If one person can cheat without fear of being caught,
others can also cheat with impunity. If the risk of exposure and sanctioning is high, participants can expect that others will make choices fromwithin the set of permitted and required actions. The acceptance of rules
represents a type of Faustian bargain (V. Ostrom 1996). Someone is given
authority to use coercion to increase beneﬁts for others—hopefully, formost others.
The simplifying assumption is frequently made in analytical theories
that individuals in an action situation will take only those actions thatare lawful given the rules that apply. For many purposes, this simplifying
assumption helps the analyst proceed to examine important theoreticalquestions not related to how well the rules are enforced. Highly complicated games, such as football, can indeed be explained with more easebecause of the presence of active and aggressive on-site referees who constantly monitor the behavior of the players and assign penalties for infraction of rules.7 And these monitors face real incentives for monitoring consistently and for applying fair and accepted penalties. Both the fans andthe managers of the relevant sports teams pay a lot of attention to what
the monitors are doing and the fairness of their judgments. In settings
where a heavy investment is not made in monitoring the ongoing actionsof participants, however, considerable difference between predicted andactual behavior can occur as a result of the lack of congruence between
a model of lawful behavior and the illegal actions that individuals frequently take in such situations.
This is not to imply that the only reason individuals follow rules isbecause they are enforced. If individuals voluntarily participate in a situation, they must share some general sense that most of the rules governing
the situation are appropriate. Otherwise, the cost of enforcement within
voluntary activities becomes high enough that it is difﬁcult, if not impossible, to maintain predictability in an ongoing voluntary activity. (One can
expect that it is always difﬁcult to maintain predictability in an ongoing
activity where participants do not have the freedom to enter and leave thesituation.)
WHAT ARE THE IMPORTANT RULES?

What rules are important for institutional analysis? For some institutional
scholars, the important difference among rules has to do with the systemof property rights in use. At a very general level, it is sometimes useful to

22

CHAPTER ONE

know whether the rules related to a situation can be broadly classed asgovernment property, private property, community property, or no property which is an open-access setting (Bromley et al. 1992). Scholars in thelegal pluralist tradition have strongly criticized these categories as not
being precise enough to understand the incentives facing participants andthus are inadequate as a foundation for public policy (Benda-Beckmann
2001). They argue that an analyst needs to learn more about particularproperty rights that specify particular bundles of rights (such as the rightto enter a state park versus the right to hunt deer in the same park) inmuch more detail than those broad categories of rights (Benda-Beckmann
1995, 1997).
A myriad of speciﬁc rules are used in structuring complex action arenas.
Scholars have been trapped into endless cataloging of rules not related toa method of classiﬁcation most useful for theoretical explanations. Butclassiﬁcation is a necessary step in developing a science. Anyone attempting to deﬁne a useful typology of rules must be concerned that theclassiﬁcation is more than a method for imposing superﬁcial order onto
an extremely large set of seemingly disparate rules. The way we havetackled this problem using the IAD framework is to classify rules according to their direct impact on the working parts of an action situation(as will be discussed in chapters 6 and 7).
Biophysical and Material Conditions
While a rule conﬁguration affects all of the elements of an action situation, some of the variables of an action situation (and thus the overall setof incentives facing individuals in a situation) are also affected by attributes of the biophysical and material world being acted upon or transformed. What actions are physically possible, what outcomes can be produced, how actions are linked to outcomes, and what is contained in theactors’ information sets are affected by the world being acted upon in asituation. The same set of rules may yield entirely different types of action
situations depending upon the types of events in the world being acted
upon by participants. These “events” are frequently referred to by political economists as the “goods and services” being produced, consumed,
and allocated in a situation as well as the technology available for theseprocesses.
The attributes of the biophysical and material conditions and theirtransformation are explicitly examined when the analyst self-consciously
asks a series of questions about how the world being acted upon in asituation affects the outcome, action sets, action-outcome linkages, andinformation sets in that situation. The relative importance of the rule conﬁguration and biophysical conditions structuring an action situation var-

DIVERSITY AND STRUCTURED INTERACTIONS

23

ies dramatically across different types of settings. The rule conﬁguration
almost totally constitutes some games, like chess, where physical attributes are relatively unimportant. The relative importance of working
rules to biophysical attributes also varies dramatically within action situations considered to be part of the public sector. Rules deﬁne and constrain
voting behavior inside a legislature more than attributes of the biophysicalworld. Voting can be accomplished by raising hands, by paper ballots, bycalling for the ayes and nays, by marching before an ofﬁcial counter, orby installing computer terminals for each legislator on which votes areregistered. In regard to communication within a legislature, however, attributes of the biophysical world strongly affect the available options. The
principle that only one person can be heard and understood at a time inany one forum strongly affects the capacity of legislators to communicate
effectively with one another (see V. Ostrom 1987).
Considerable academic literature has focused on the effect of attributesof goods on the results obtained within action situations. A key assumption made in the analysis of a competitive market is that the outcomes ofan exchange are highly excludable, easily divisible and transferable, andinternalized by those who participate in the exchange. Markets are predicted to fail as effective decision mechanisms when they are the only
arena available for producing, consuming, or allocating a wide varietyof goods that do not meet the criteria of excludability, divisibility, andtransferability. Market failure means that the incentives facing individuals
in a situation, where the rules are those of a competitive market but the
goods do not have the characteristics of “private goods,” are insufﬁcient
to motivate individuals to produce, allocate, and consume these goods at
an optimal level.
Let us brieﬂy consider two attributes that are frequently used to distinguish among four basic goods and services: exclusion and subtractabilityof use. Exclusion relates to the difﬁculty of restricting those who beneﬁt
from the provision of a good or a service. Subtractability refers to theextent to which one individual’s use subtracts from the availability of agood or service for consumption by others. Both of these two attributes
can range from low to high. When these attributes are dichotomized andarrayed as shown in ﬁgure 1.3, they can be used as the deﬁning attributesof four basic types of goods: toll goods (sometimes referred to as club
goods), private goods, public goods, and common-pool resources. Goods
that are generally considered to be “public goods” yield nonsubtractive
beneﬁts that can be enjoyed jointly by many people who are hard to exclude from obtaining these beneﬁts. Peace is a public good, as my enjoyment of peace does not subtract from the enjoyment of others. Commonpool resources yield beneﬁts where beneﬁciaries are hard to exclude but
each person’s use of a resource system subtracts units of that resource

24

CHAPTER ONE

Subtractability of use

Difficulty of
excludingpotential
beneficiaries

Low

High

Low

Toll goods

Private goods

High

Public goods

Common-pool resources

Figure 1.3 Four basic types of goods. Source: Adapted from V. Ostrom and
E. Ostrom 1977, 12.

from a ﬁnite total amount available for harvesting (E. Ostrom, Gardner,
and Walker 1994; Aggarwal and Dupont 1999). When a ﬁsher harvests
a ton of ﬁsh, those ﬁsh are not available to any other ﬁsher.
EXCLUDABILITY AND THE FREE-RIDER PROBLEM

When the beneﬁts of a good are available to a group, whether or not
members of the group contribute to the provision of the good, that good
is characterized by problems of excludability. Where exclusion is costly,
those wishing to provide a good or service face a potential free-rider orcollective-action problem (Olson 1965). Individuals who gain from themaintenance of an irrigation system, for example, may not wish to contribute labor or taxes to maintenance activities, hoping that others willbear the burden. This is not to say that all individuals will free-ride whenever they can. A strong incentive exists to be a free-rider in all situations
where potential beneﬁciaries cannot easily be excluded for failing to contribute to the provision of a good or service.
When it is costly to exclude individuals from enjoying beneﬁts from an
investment, private, proﬁt-seeking entrepreneurs, who must recoup theirinvestments through quid pro quo exchanges, have few incentives to provide such services on their own initiative. Excludability problems can thus
lead to the problem of free-riding, which in turn leads to underinvestment
in capital and its maintenance.
Public sector provision of common-pool resources or infrastructure
facilities raises additional problems in determining preferences and organizing ﬁnances. When exclusion is low cost to the supplier, preferences
are revealed as a result of many quid pro quo transactions. Producers
learn about preferences through the consumers’ willingness to pay forvarious goods offered for sale. Where exclusion is difﬁcult, designingmechanisms that honestly reﬂect beneﬁciaries’ preferences and their will-

DIVERSITY AND STRUCTURED INTERACTIONS

25

ingness to pay is challenging, regardless of whether the providing unit isorganized in the public or the private sphere. In very small groups, thoseaffected are usually able to discuss their preferences and constraints on aface-to-face basis and to reach a rough consensus. In larger groups, decisions about infrastructure are apt to be made through mechanisms suchas voting or the delegation of authority to public ofﬁcials. The extensive
literature on voting systems demonstrates how difﬁcult it is to translate
individual preferences into collective choices that adequately reﬂect individual views (Arrow 1951; Monroe forthcoming).
Another attribute of some goods with excludability problems is thatonce they are provided, consumers may have no choice whatsoever as towhether they will consume. An example is the public spraying of insects.
If an individual does not want this public service to be provided, there
are even stronger incentives not to comply with a general tax levy. Thus,
compliance with a broad ﬁnancing instrument may, in turn, depend upon
the legitimacy of the public-choice mechanism used to make provision
decisions.
SUBTRACTABILITY

Goods and facilities can generate a ﬂow of services that range from being
fully subtractable upon consumption by one user to another extreme
where consumption by one does not subtract from the ﬂow of services
available to others. The withdrawal of a quantity of water from an irrigation canal by one farmer means that there is that much less water foranyone else to use. Most agricultural uses of water are fully subtractive,
whereas many other uses of water—such as for power generation or navigation—are not. Most of the water that passes through a turbine to generate power, for instance, can be used again downstream. When the use of
a ﬂow of services by one individual subtracts from what is available toothers and when the ﬂow is scarce relative to demand, users will be
tempted to try to obtain as much as they can of the ﬂow for fear that it
will not be available later.
Effective rules are required if scarce, fully subtractive service ﬂows areto be allocated in a productive way (E. Ostrom, Gardner, and Walker
1994). Charging prices for subtractive services obviously constitutes one
such allocation mechanism. Sometimes, however, it is not feasible to priceservices. In these instances, some individuals will be able to grab considerably more of the subtractive services than others, thereby leading to noneconomic uses of the ﬂow and high levels of conﬂict among users.
Allocation rules also affect the incentives of users to maintain a system.
Farmers located at the tail end of an irrigation system that lacks effective
allocation rules have little motivation to contribute to the maintenance

26

CHAPTER ONE

of that system because they only occasionally receive their share of water.
Similarly, farmers located at the head end of such a system are not motivated to provide maintenance services voluntarily because they will receive disproportionate shares of the water whether or not the system iswell-maintained (E. Ostrom 1996).
ADDITIONAL ATTRIBUTESIn addition to exclusion and subtractability, the structure of action situations is also affected by a diversity of other attributes that affect how rules
combine with physical and material conditions to generate positive or
negative incentives. The number of attributes that may affect the structureof a situation is extraordinarily large, and I do not want even to start alist in this volume. The crucial point for the institutional analyst is thatrules that help produce incentives leading to productive outcomes in one
setting may fail drastically when the biophysical world differs. As our
extensive studies of common-pool resources have shown, for example,
effective rules depend on the size of the resource; the mobility of its resource units (e.g., water, wildlife, or trees); the presence of storage in thesystem; the amount and distribution of rainfall, soils, slope, and elevation;and many other factors (see E. Ostrom, Gardner, and Walker 1994).
These additional attributes are slowly being integrated into a body ofcoherent theory about the impact of physical and material conditions on
the structure of the situations that individuals face and their resulting
incentives and behavior. Analysts diagnosing policy problems need to besensitive to the very large difference among settings and the need to tailor
rules to diverse combinations of attributes rather than some assumed uniformity across all situations in a particular sector within a country.
Attributes of the Community
A third set of variables that affect the structure of an action arena relate
to the concept of the community within which any focal action arena islocated. The concept of community is again one that has many deﬁnitions
and meanings across and within the social sciences. Given the breadth ofwhat I already plan to tackle, I do not plan to focus in detail on how
various attributes of community affect the structure of situations within
a community (see Agrawal and Gibson 2001 for an excellent overall review of the concept of community). The attributes of a community thatare important in affecting action arenas include: the values of behavior
generally accepted in the community; the level of common understandingthat potential participants share (or do not share) about the structure of

DIVERSITY AND STRUCTURED INTERACTIONS

27

particular types of action arenas; the extent of homogeneity in the preferences of those living in a community; the size and composition of therelevant community; and the extent of inequality of basic assets among
those affected.
The term culture is frequently applied to the values shared within acommunity. Culture affects the mental models that participants in a situation may share. Cultures evolve over time faster than our underlying
genetic endowment can evolve. Cultures have in turn affected how
the human brain itself has evolved (Boyd and Richerson 1985; Richerson
and Boyd 2002). The history of experience with governance institutions
at multiple levels affects the way local participants understand, implement, modify, or ignore rules written by external ofﬁcials (Medard andGeheb 2001).
For example, when all participants share a common set of values andinteract with one another in a multiplex set of arrangements within asmall community, the probabilities of their developing adequate rules andnorms to govern repetitive relationships are much greater (Taylor 1987).
The importance of building a reputation for keeping one’s word is important in such a community, and the cost of developing monitoring andsanctioning mechanisms is relatively low. If the participants in a situation
come from many different cultures, speak different languages, and are
distrustful of one another, the costs of devising and sustaining effective
rules are substantially increased.
Whether individuals use a written vernacular language to express theirideas, develop common understanding, share learning, and explain thefoundation of their social order is also a crucial variable of relevance toinstitutional analysis (V. Ostrom 1997). Without a written vernacular language, individuals face considerably more difﬁculties in accumulating
their own learning in a usable form to transmit from one generation to
the next.

Institutional Frameworks, Theories, and Models
So far in this chapter, I have provided a brief overview of the IAD framework without telling the reader what I mean by a framework. The terms—
framework, theory, and model—are all used almost interchangeably by
diverse social scientists. This leads to considerable confusion as to what
they mean.8 Frequently, what one scholar calls a framework others call amodel or a theory.9 In this book, I will use these concepts to mean a nested
set of theoretical concepts—which range from the most general to themost detailed types of assumptions made by the analyst. Analyses con-

28

CHAPTER ONE

ducted at each level provide different degrees of speciﬁcity related to aparticular problem (Schlager 1999).
The development and use of a general framework helps to identify theelements (and the relationships among these elements) that one needs toconsider for institutional analysis. Frameworks organize diagnostic andprescriptive inquiry. They provide the most general set of variables thatshould be used to analyze all types of settings relevant for the framework.
Frameworks provide a metatheoretic language that is necessary to talkabout theories and that can be used to compare theories. They attempt
to identify the universal elements that any relevant theory would need toinclude. Many differences in surface reality can result from the way thesevariables combine with or interact with one another. Thus, the elements
contained in a framework help the analyst generate the questions thatneed to be addressed when ﬁrst conducting an analysis.
The development and use of theories enable the analyst to specify which
components of a framework are relevant for certain kinds of questions
and to make broad working assumptions about these elements. Thus,
theories focus on parts of a framework and make speciﬁc assumptions
that are necessary for an analyst to diagnose a phenomenon, explain its
processes, and predict outcomes. To conduct empirical research, a scholar
needs to select one or more theories to use in generating predictions about
expected patterns of relationships. Several theories are usually compatible
with any framework. Empirical research should narrow the range of applicable theories over time by showing the superiority of the remaining
theories to explain data. Microeconomic theory, game theory, transaction
cost theory, social choice theory, public choice, constitutional and covenantal theory, and theories of public goods and common-pool resources
are all compatible with the IAD framework discussed in this book.
The development and use of models make precise assumptions about alimited set of parameters and variables. Logic, mathematics, game theorymodels, experimentation and simulation, and other means are used to
explore the consequences of these assumptions systematically on a limited
set of outcomes. Multiple models are compatible with most theories. Inan effort to understand the strategic structure of the games that irrigatorsplay in differently organized irrigation systems, for example, Weissing
and Ostrom (1991a, 1991b) developed four families of models to explore
the likely consequences of different institutional and physical combinations relevant to understanding how successful farmer organizations arranged for monitoring and sanctioning activities. These models enabled
us to analyze in a precise manner a subpart of the theory of commonpool resources and thus also one combination of the components of the
IAD framework. Models are extensively used in contemporary policy

DIVERSITY AND STRUCTURED INTERACTIONS

29

analysis by ofﬁcials working with the World Bank, national governments,
as well as state and local governments.
For policy makers and scholars interested in issues related to how different governance systems enable individuals to solve problems democratically by modifying rules at various levels, the IAD framework helps toorganize diagnostic, analytical, and prescriptive capabilities. It is similarin structure and intent to the “Actor-Centered Institutionalism” framework developed by Renate Mayntz and Fritz Scharpf (1995) and applied
to several national policy settings by Fritz Scharpf (1997). It also aids inthe accumulation of knowledge from empirical studies and in the assessment of past efforts at reforms.
Without the capacity to undertake systematic, comparative institutional assessments, recommendations of reform may be based on naive
ideas about which kinds of institutions are “good” or “bad” and not
on an analysis of performance. Some policy analysts tend to recommend
private property as a way of solving any and all problems involving overuse of a resource. While private property works effectively in some environments, it is naive to presume it will work well in all (see Tietenberg
2002; Rose 2002). One needs a common framework and family of theories in order to address questions of reforms and transitions. Particularmodels then help the analyst to deduce speciﬁc predictions about likely
outcomes of highly simpliﬁed structures. Models are useful in policy analysis when they are well-tailored to the particular problem at hand. Models
are used inappropriately when applied to the study of problematic situations that do not closely ﬁt the assumptions of the model (see E. Ostrom
1990 for a critique of the overreliance on open access models of commonpool resources regardless of whether users had created their own rules tocope with overharvesting or not).

The Limited Frame of This Book
Several times in the past, I have participated with colleagues in efforts tooutline a book that examined how rule conﬁgurations, attributes ofgoods, and attributes of the community all affected the structure of action
situations, individual choices, outcomes, and the evaluation of outcomes.10 Each time, the projected volume mushroomed in size and overcame our capacity to organize it. Thus in this book, I have tried to focus
primarily on how rules affect the structure of action situations instead oftrying to work out the details of the entire framework. The focus on thecomponents of institutions in this volume should not be interpreted tomean that I feel that institutions are the only factor affecting outcomes inall action situations.

30

CHAPTER ONE

Institutions are only one of a large number of elements that affect behavior in any particular situation at a particular time and place. No single
cause exists for human behavior. To live, one needs oxygen, water, andnutrition. All are key parts of the explanation of life. Life itself operates
at multiple levels. Genes underlie phenotypic structures in a manner thatis broadly analogous to the way that rules underlie action situations. But
neither genes nor rules fully determine behavior of the phenotypes thatthey help to create. Selection processes on genes operate largely at theindividual level, but rules—as well as other cultural “memes”—are likely
to be selected at multiple levels (see Hammerstein 2003). When one steps
back, however, for all of the complexity and multiple levels, there is alarge amount of similarity of underlying factors. In the biological world,
it is somewhat amazing that there is only a small proportion of the genesthat differ between an elephant and a mouse. As we develop the logic of
institutions further, we will see that many situations that have the surface
appearance of being vastly different have similar underlying parts. Thus,
our task is to identify the working parts, the grammar, the alphabet ofthe phenotype of human social behavior as well as the underlying factorsof rules, biophysical laws, and community.
Thus, the focus of this book reﬂects my sense that the concept of institutions, the diversity of institutions and their resilience, and the question of
how institutions structure action situations require major attention. This
volume is, thus, an effort to take an in-depth look at one major part ofwhat is needed to develop fuller theories of social organization. In thisvolume, I will try to articulate in more detail than has been possible before
what I think the components of institutions are and how they can be used
to generate explanations of human behavior in diverse situations. The
focus on institutions should not be interpreted, however, as a position
that rules are always the most important factor affecting interactions andoutcomes. In the midst of a hurricane, rules may diminish greatly in theirimportance in affecting individual behavior.
This volume should be thought of as part of a general effort to understand institutions so as to provide a better formulation for improving theirperformance. Our book Rules, Games, and Common-Pool Resources(E. Ostrom, Gardner, and Walker 1994) provides one focused applicationof the IAD framework. Michael McGinnis has recently edited three volumes (1999a, 1999b, 2000) that present earlier elucidations of the IAD
framework and empirical studies that are closely related. Kenneth Bickers
and John Williams (2001) and Michael McGinnis and John Williams(2001) clearly elucidate important aspects of the general approach.
I am writing this book from the perspective of a policy analyst. Without
the careful development of a rigorous and empirically veriﬁable set oftheories of social organization, we cannot do a very good job of ﬁxing

DIVERSITY AND STRUCTURED INTERACTIONS

31

problems through institutional change. And, if we cannot link the theoretical results into a coherent overall approach, we cannot cumulate knowledge. All too often, major policy initiatives lead to counterintentional results. We need to understand institutions in order to improve theirperformance over time (North 2005).
As I demonstrate in chapter 8, however, the option of optimal design
is not available to mere mortals. The number of combinations of speciﬁcrules that are used to create action situations is far larger than any set
that analysts could ever analyze even with space-age computer assistance.
This impossibility does not, however, leave me discouraged or hopeless.
It does, however, lead me to have great respect for robust institutions thathave generated substantial beneﬁts over long periods of time (see Shepsle
1989; E. Ostrom 1990). None have been designed in one single step.
Rather, accrued learning and knowledge have led those with good information about participants, strategies, ecological conditions, and changes
in technology and economic relationships over time to craft sustainableinstitutions, even though no one will ever know if they are optimal. Thus,in chapter 9, I dig into the process of learning, adaptation, and evolution
as processes that enable polycentric institutional arrangements to utilize
very general design principles in the dynamic processes of trying to improve human welfare over time. It is also necessary to discuss the threatsthat can destroy the resilience of complex social systems.

TwoZooming In and Linking Action Situations

An Action Situation as a Focal Unit of Analysis
Whenever two or more individuals are faced with a set of potential actions
that jointly produce outcomes, these individuals can be said to be “in”
an action situation. Typical action situations include:• buyers and sellers exchanging goods in a market;• legislators making legislative decisions about future laws;• powerful politicians bargaining over the allocation of public support;• users of a common-pool resource withdrawing resource units (such as ﬁsh,water, or timber);• heads of state negotiating an international treaty.

The structure of all of these situations—and many more—can be described and analyzed by using a common set of variables. These are: (1)the set of participants, (2) the positions to be ﬁlled by participants, (3) the
potential outcomes, (4) the set of allowable actions and the function thatmaps actions into realized outcomes, (5) the control that an individual
has in regard to this function, (6) the information available to participants
about actions and outcomes and their linkages, and (7) the costs and beneﬁts—which serve as incentives and deterrents—assigned to actions andoutcomes. The internal structure of an action situation can be represented
as shown in ﬁgure 2.1. In addition to the internal structure, whether asituation will occur once, a known and ﬁnite number of times, or indeﬁnitely affects the strategies of individuals.
The number of participants and positions in a situation may vary, butthere must always be participants in positions for one to talk about anaction situation. Similarly, there must be potential actions that participants can take. The set of available actions represents the means thatparticipants have to achieve particular outcomes in that situation. Information about the situation may vary, but all participants must have accessto some common information about the situation for an analyst to evenstate that the participants are in the same situation. The costs and beneﬁts
assigned to actions and outcomes can be thought of as the external incentives and deterrents in a situation. How these affect the choice of participants regarding speciﬁc actions, and thus the cumulation of results, de-33

L I N K I N G A C T I O N S I T U AT I O N S

Exogenous Variables
PARTICIPANTS

INFORMATION
about

CONTROLoverassigned toassigned to

ACTIONSPOTENTIALOUTCOMES

Linked to

POSITIONSNET COSTS
AND BENEFITS
assigned toGiven implicit models used by actors

Given the theory used by an analyst

Observed Interactions and Outcomes Predicted Interactions and Outcomes
Evaluative Criteria

Evaluative Criteria

Empirical Analysis

Theoretical Analysis

Figure 2.1 The internal structure of an action situation.

pends also on the initial resources and valuation patterns held by theparticipants. In some action situations, the standing of participants isgrossly unequal, allowing some to have substantial power over others andthe relative beneﬁts they can achieve.
When doing analysis at a focal level of analysis, these working parts
are the “givens” of a situation. For the purposes of analyzing the likely
human behavior and outcomes within a particular structure, one assumes
that the structure of the situation is ﬁxed for the short run. Within aparticular situation, individuals can attempt to choose only in light of
their beliefs about the opportunities and constraints of that situation. In
an open society, individuals may be able eventually to affect the structureof action situations in which they repeatedly ﬁnd themselves by changing
the rule conﬁgurations affecting the structure of these situations. To do so,they move to deeper analytical levels (collective-choice or constitutionalchoice action situations) where the outcomes generated are changes in

34

CHAPTER TWO

the rules of other action situations. In a closed society, individuals at an
operational level may have little opportunity to change rules at any leveland may ﬁnd themselves in highly exploitative situations. We discuss shifting to a higher level in the last half of this chapter.
The working parts of an action situation are both necessary and sufﬁcient to describe the structure of an action situation. They are similar to
the elements identiﬁed by game theorists to construct formal game models(see Gardner 2003; Gintis 2000b). A formal description of a game is thus
one way of describing a subset of all action situations and will soon be
used to illustrate the concept of a simple action situation.1
The mathematical tools of game theory are powerful and enlightening.
They can only be used, however, to elucidate the structure of relatively
simple action situations. The full game tree for a highly structured game,such as chess, cannot be fully articulated. In chess, for example, the ﬁrst
player can open with any of twenty actions and the second player can
respond with twenty as well. Thus, after the ﬁrst two moves, there are
already four hundred branches to specify if one wanted to try to represent
chess as a formal game. “It has been estimated that the total number ofpossible moves in chess is on the order of 10120, or a ‘one’ with 120 zeros
after it. . . . A supercomputer a thousand times faster than your PC, making a billion calculations a second, would need approximately 3 × 10103
years to check out all of these moves” (Dixit and Skeath 1999, 66).
In addition to using the elements of an action situation to analyze aformal game, the elements can also be used to describe the structure ofmore complex action situations in a nonmathematical form. One strategy
is to represent key parts of complex chains of action situations as a gamewithout trying to represent the entire structure as one game (McGinnis
forthcoming). The basic elements of an action situation have also beenused in empirical studies of complex action situations in the ﬁeld.
Scholars associated with the Workshop in Political Theory and Policy
Analysis have developed three large databases that measured as many
aspects as we could regarding the structure of action situations facing
appropriators from common-pool resources: inshore ﬁsheries (seeSchlager 1990, 1994, 2004; Schlager and Ostrom 1992, 1993); irrigation
systems (see Tang 1992; Lam 1998); and forests (see Gibson, McKean,and Ostrom 2000; Moran and Ostrom 2005; Poteete and Ostrom 2004).
These efforts to measure the structure and the realized outcomes of various property regimes as they relate to diverse common-pool resources led
us to realize both how difﬁcult it is to measure these concepts in ﬁeld
settings as well as the substantial scientiﬁc beneﬁts achieved by so doing.
Speciﬁc sets of questions on our coding forms were our way of operationalizing the basic working parts of operational-level action situations.

L I N K I N G A C T I O N S I T U AT I O N S

35

A carefully crafted case study is another method for analyzing more
complex action situations and their linkages (Yandle 2001). Analyticalnarratives are an important technique for examining the structure of complex action-outcome linkages and their consequences (Bates et al. 1998,
2000). So long as theorists use a consistent language to describe theirstructure, much can be learned from single case studies over time or comparative case studies of action situations that are not presented in formal
language (see Theesfeld 2004; Yandle and Dewees 2003).
Computer programs have been written as ways of representing the actions of players. An IBM team, for example, put years of effort into a
chess-playing program—Deep Blue—to try to beat a world champion
chess player. The IBM team succeeded in beating Gary Kasparov, thethen world champion chess player in 1997, only after many years of trying.2 Agent-based computational models are currently used by some analysts to examine a variety of action situations that are too complex to
be analyzed completely using mathematical models (see Axelrod 1997;
Tesfatsion 2002; Janssen 2003; Janssen and Ostrom forthcoming a; Parker et al. 2003; Hodgson and Knudsen 2004). The method of institutional
analysis described here can thus be implemented using a wide variety ofanalytical tools.

Example of a Simple Action Situation
Before turning to a discussion of the individual working parts of a situation, let us illustrate the concept of an action situation using the tools ofgame theory to examine a simple game—the Snatch Game.3 The Snatch
Game characterizes situations where the individuals involved do not sharenorms or rules. In other words, it is a game in a “state of nature.” In thisnormless and ruleless environment, let us assume there are two farming
households who are fully self-reliant. Each produces a different commodity—say, chickens and potatoes. Meals composed of all chicken or all
potatoes are not as ﬂavorful or healthy as meals composed of bothchicken and potatoes. Both households would thus beneﬁt from ﬁnding
a way of exchanging some of the chickens and potatoes they produce.
The structure of a Snatch Game in a state of nature is shown in ﬁgure
2.2. In this game, Household 1 and Household 2 both have ten comparable units of the commodity they produce. Household 1 has ten chickens
and Household 2 has ten sacks of potatoes (each sack of potatoes is considered comparable to one chicken). Both would be better off if they could
exchange half of their own commodities for half of those grown by theother farming household. Say, each household valued their own commodity as one. If they could exchange ﬁve units of their own production for

36

CHAPTER TWO(15, 15)(5, 20)Snatch(10, 10)

Exchange
Refuse Offer
Household 2(10, 10)Do Not Offer

Offer
Household 1

Figure 2.2 The Snatch Game. Source: Based on STEAL in Plott and Meyer
1975, 70.

ﬁve of the other farming household, the marginal value of the second
commodity would give them twice as much value—or a total of ﬁfteen
value units each. The problem is how to accomplish an exchange when
there are no well-deﬁned property rights.
Household 1 could either offer or not offer to exchange ﬁve units withHousehold 2. If Household 2 gets an offer, then it has three choices.
The ﬁrst is to refuse to exchange. The outcome here would be for bothhouseholds to retain their original ten units. Second, Household 2 canagree to an exchange and both households would be better off with ﬁfteen value units. A third alternative—especially if Household 2 had several young, strong sons—would be to snatch the ﬁve commodities offeredby Household 1 and keep their own. This leaves Household 1 with ﬁveunits. Household 2 then has ﬁfteen commodity units valued at twenty
units of value.
Let us assume that both households value only the goods they ﬁnallyreceive. If Household 2 has the physical capability of snatching the goods
once they were brought out in this “lawless” situation, no exchange
would take place. Given that Household 2 has not developed strong
norms against snatching, which affect the value of the outcomes and arealso known to Household 1, Household 1 would predict that Household
2 will snatch the goods. And without internal norms against using physical force to take possession of goods, Household 2 would deﬁnitely snatch
Household 1’s goods, if given the opportunity. Given this certainty,

L I N K I N G A C T I O N S I T U AT I O N S

37Household 1 would never offer an exchange in the ﬁrst place. The predicted equilibrium of this game is an inefﬁcient outcome—no exchange.
Readers familiar with game theory will recognize that the Snatch Gameis one example of a very broad class of games that has the structure of aone-shot, sequential social dilemma. Social dilemmas are ubiquitous in
economic, political, and social life. They occur whenever the private returns to each participant are greater than their share of a joint return nomatter what other participants do. If the structure of a one-shot social
dilemma game is not changed, and individuals pursue their own immediate, objective outcomes as the only values taken into account, individuals
will not achieve outcomes that could leave everyone better off. Asymmetric social dilemmas, similar to the Snatch Game, are sometimes called: the“Trust Game,” or the “Investment Game,” or even the “Peasant Dictator
Game” (Berg, Dickhaut, and McCabe 1995). We will describe ﬁndings
from a series of experiments on these games in chapter 3.
The pervasiveness of social dilemmas has repeatedly been recognizedin the great books of political philosophy. Hobbes described such a setting
as a “war of all against all.” Rousseau used a stag hunt to illustrate theproblem of a group needing to all work together to hunt a large animal
but facing the temptation to break up into separate groups when small
animals appeared on the scene that were easy to catch. A small group
could catch a rabbit, but ruined the chance for the group to obtain alarge animal. Many important books of the last several decades have been
devoted to an analysis of simultaneous or sequential social dilemmas(Barry and Hardin 1982; Taylor 1987; Schelling 1978). We shall often
use social dilemma situations as examples throughout this book, since
understanding how individuals act within social dilemma situations constitutes one of the major puzzles facing all contemporary social science
disciplines.
We shall return to a discussion of the Snatch Game at different junctures throughout the book—not because we think that all of the interesting action situations are illustrated by simple, two-player games. Rather,simple situations can help us understand the concept of an action situation itself. And these simple situations also illustrate that a large body ofimportant situations—social dilemmas—are at the heart of our discourse
about institutions and the diversity of institutions.

The Basic Working Parts of Action Situations
Let us now turn to the elements of an action situation so we can begin tounderstand these fundamental elements of all interactive situations.

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CHAPTER TWO

ParticipantsParticipants in an action situation are decision-making entities assigned
to a position and capable of selecting actions from a set of alternatives
made available at nodes in a decision process. The participants in action
situations can also be corporate actors—nations, states in a federal system, private corporations, NGOs, and so forth. Whenever participants
are organizations, one treats them “in” the situation as if they were asingle individual but one that is linked to a series of additional situations
within their own organization. For some purposes, one may ignore thelinked situations—especially when the interests of the organization, andthus the strategy it will follow, are very clear and unlikely to change due
to an internal challenge. Alternatively, one may self-consciously examine
the linked structure (see McGinnis and Williams 1989). Several attributesof participants are relevant when representing and analyzing speciﬁc situations. These include (1) the number of participants, (2) their status asindividuals or as a team or composite actor, (3) and various individual
attributes, such as age, education, gender, and experience.
THE NUMBER OF PARTICIPANTS

Interdependent action situations require at least two participants—suchas the two households in the Snatch Game—where the actions of eachaffect the outcomes for both. Adding a third participant changes the structure of the situation substantially.4 The dividing line between major typesof games, for example, is between two-person and N-person games,
where N is deﬁned as any number greater than two. The speciﬁc numberof participants is frequently overtly speciﬁed in real-world settings (or informal theories about these settings) such as legislatures, juries, and most
sports. Some descriptions of a situation, however, specify the number ofparticipants in a looser fashion such as a small or large group, or faceto-face relationships versus impersonal relationships. Since many othercomponents of an action situation are affected by the number of participants, this is a particularly important variable in the analysis of any action
situation.
THE INDIVIDUAL OR TEAM STATUS OF PARTICIPANTS

Participants in many action situations are individual persons, or they may
represent a team or composite actor, such as the households in the Snatch
Game. Under speciﬁc conditions, a group of individuals may be considered as one participant (a team) in a particular action situation. Let us
address the conditions that are necessary to treat a group as a participant
when they participate individually in at least one other linked situation.

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39Drawing on the work of Fritz Scharpf (1997), we need ﬁrst to distinguish between sets of individuals who share many similar characteristics
and whose aggregate behavior may be predictable from knowledge acquired about a sample of individuals. In electoral politics, one can discuss“the urban voter,” or “veterans,” or “the labor vote.” These are shorthand terms for a class of individuals who share characteristics that areperceived to be very important in affecting their individual preferences
related to some events. These are meaningful concepts. They help the analyst make sense out of many events when a large number of individuals
act independently, and it is useful to gain a realistic expectation about
their likely actions. They are not, however, acting as a team.
As Scharpf, and Granovetter (1978) before him acknowledge, when
individuals who share some key attributes also differ in the strength ofsome other key variable, aggregate behavior may be characterized by frequency-dependent behavior with strong threshold effects. This has beenused in the explanations of street protests or revolutionary actions (Kuran
1989). For example, those who feel the most strongly against a policy
may be willing to stage protest marches even if others do not join them.
If the number of individuals who have strong views is not sufﬁciently
large, however, other individuals may not be willing to engage in protest
actions. On the other hand, depending on the distribution of preferences,
it is possible for a strong initial showing to trigger more individuals,
whose actions in turn trigger still others, to participate. Such processes
can still be explained primarily at the individual level.
For an institutional analyst to consider a set of individuals to be a “composite” actor, one must assume that the individuals intend to participate
in a collective action. One needs to assume that “the individuals involved
intend to create a joint product or to achieve a common purpose”(Scharpf 1997, 54). Such composite actors—such as a household—will,of course, vary in regard to the type of internal decision-making mechanisms that they will use. Some will depend on very widespread preferencesof their members—which Scharpf calls “collective actors.” “Corporate
actors,” on the other hand, are not so dependent on the preferences of
their members and beneﬁciaries, and activities “are carried out by staff
members whose own private preferences are supposed to be neutralizedby employment contracts” (54).
A fully organized market with well-deﬁned property rights, for example, may include buyers and sellers who are organized as ﬁrms as well as
individual participants. Firms are composed of many individuals. Eachﬁrm in a market is treated as if it were a single participant, but this is a“shorthand” way of viewing the lattice of internal action situations withina particular ﬁrm that leads to external decisions to be taken in market
settings.

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CHAPTER TWO

Whether an analyst treats separate individuals as participants or as
members of a collective or corporate organization depends upon the concerns and questions of the analyst. The game of bridge, for example, may
be represented as having either four participants or two teams, each composed of two individuals. If the analyst is interested in examining communication behavior during bidding, bridge is best represented as involving
four participants. For the purpose of explaining how moves lead to a ﬁnal
score, the game may best be represented as involving two teams.
ATTRIBUTES OF PARTICIPANTS

Participants bring a diversity of ascribed or acquired characteristics to any
situation. These characteristics may not inﬂuence their actions in somesituations, while having a major impact on others. Whether drivers passing one another on a busy highway are both of the same social or ethnic
background is unlikely to affect their actions. A young, inexperienced
driver of any background, however, may approach passing another carwith great hesitation and not pass when most adult drivers would do so.
The same two adults might hesitate a long time before extending trust
and reciprocity to each other if facing an opportunity to enter a long-term
contract in a community that has recently faced racial or ethnic conﬂict.
Even the simple Snatch Game is likely to have different outcomes depending on the attributes of Households 1 and 2. The outcomes of many
situations depend on the knowledge and skills of the parties. Two chessmasters will play a chess tournament differently than two young children
just learning the game.
Attributes of participants are also affected by the rules structuring anaction situation. The A in the ADICO syntax presented in chapter 5 identiﬁes the attributes of participants as an important element of all rules.
Individuals with some ascribed or acquired attributes, such as ethnic
background, gender, or education may be enjoined from participation.
Positions
Another element of an action situation is the set of positions or “anonymous slots” into and out of which participants move. Examples of positions include players, voters, judges, buyers, sellers, legislators, police ofﬁcers, and so forth. Positions and participants are separate elements in asituation even though they may not be clearly so identiﬁed in practice. In
many formal games, the distinction between a participant and the position that a participant holds is blurred. In the Snatch Game, for example,
there are two participants and two positions. The positions are simply
labeled as Household 1 and Household 2. The household in the ﬁrst posi-

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41tion has the opportunity to make the ﬁrst move. While in this instance,
given the prediction of no exchange, holding the ﬁrst position does notgive a special advantage. Frequently, however, ﬁrst mover advantages
exist and may give one participant substantial power over the other.
The number of positions is frequently fewer than the number of participants. In legislative committee situations, for example, there are frequently only two positions—chair and member—while there may be ﬁve
to twenty-ﬁve or more participants. When game theorists identify the elements of a game, they rarely mention positions in their deﬁnition (seeRapoport 1966; Gintis 2000b). They then tend to name positions in the
text describing a game, as I did above, using terms such as Player 1 andPlayer 2, Principal and Agent, or Agenda Setter and Member.
Depending on the structure of the situation, a participant may simultaneously occupy more than one position. All participants will occupy
whatever is the most inclusive position in a situation—member, citizen,
employee, and the like. In a private ﬁrm, additional positions such as
foreman, division manager, or president will be occupied by some participants while they continue to occupy the most inclusive position—thatof employee.
Positions are thus the connecting link between participants and actions.
In some situations, any participant in any position may be authorized
to take any of the allowable actions in that situation. However, in most“organized” situations, the capability to take particular actions is assigned to a speciﬁc position and not to all positions. The nature of a position assigned to participants in an action situation deﬁnes the “standing”
of the participant in that situation. The standing of a position is the setof authorized actions and limits on actions that the holder of the position
can take at particular choice sets in the situation. Those who hold theposition of a member of a legislative committee are authorized to debate
issues and vote on them. The member who holds the position of chair canusually develop the agenda for the order of how issues will be brought
before the committee or even whether a proposal will even be discussed.
Determining this agenda frequently determines which issue will win in a
ﬁnal vote (Plott and Levine 1978; Weingast 1989).
Action situations vary substantially in the degree to which participants
control their own entry into or exit from a position. A defendant in acriminal trial does not control his or her movement into or out of thisposition. Participants in many formal social dilemma games are treatedas if they have no choice regarding entry and exits. When exit is feasible,
however, the outcomes of social dilemma situations are likely to be different (Orbell and Dawes 1993). Orbell, Schwarz-Shea, and Simmons(1984) have shown that cooperators, when given the opportunity to exit
after playing with a defector, choose the exit option more than defectors.

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CHAPTER TWO

A participant in a legislature could not be a member of the legislature
against his or her will, but could lose this position involuntarily. In somehierarchical situations covered by civil service systems, individuals haveto compete vigorously for positions by passing examinations, but once
appointed, they may hold their positions for life, subject to their taking
legal actions.
Potential Outcomes
In the analysis of formal games, the standard practice is to report theoutcomes of joint decisions as the analyst assumes they are ranked inutility to the participants in particular positions. In the Snatch Game, thevalue of ten is assigned to each of the households for keeping their own
chickens and potatoes. A utility value of ﬁfteen is designated for a successful exchange of half of the chickens for half of the potatoes, and the utility
value of twenty to Household 2 for keeping its own commodities andsnatching ﬁve from Household 1. When the purpose of analyzing a situation is focused entirely on understanding the result of a particular structure and the analyst is certain about the ordinal ranking of participants’values over outcomes, then this abbreviated process of representing outcomes in utility space is an effective means of analyzing a situation.
When the analyst wants to understand how rules or attributes of thebiophysical world or the community change an action situation, greater
precision is obtained by separately analyzing the biophysical outcomes
and then the value assigned by participants to outcomes. In the Snatch
Game, the physical outcomes are the actual bundles of commodities thatexist at each end point. If no offer is made or an offer is rejected, bothhouseholds end up with ten units of commodities. If Household 2 successfully snatches Household 1’s commodities offered for sale, it ends up withﬁfteen commodity units and Household 1 is left with ﬁve units of theirown commodity. If they reach a successful exchange, each household now
has ﬁve sacks of potatoes and ﬁve chickens, which is assigned a utility
valuation of ﬁfteen units.
As discussed below, external beneﬁts and costs are frequently assigned
to outcomes by payoff rules. If there were a market where commodities
were exchanged at known prices, one could assign a monetary value tothe commodities. If there were taxes imposed on the exchange of commodities (a sales tax), one could represent the outcomes in a monetary
unit representing the market price minus the tax. If one wanted to examine the proﬁtability of growing chickens as contrasted to potatoes or othercrops, one would represent the outcomes in terms of the monetary valueof the realized sales value minus the monetary value of the inputs (land,
labor, and other variable inputs).

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43

Combining biophysical outcomes, external payoffs, and participants’internal valuation into one measure is useful for making decisions in astatic setting. It does not, however, enable an analyst to identify how speciﬁc rules might affect the structure of a situation. The set of physicallypossible actions and resulting transformations remains the same if payoff
rules are the only rules to be changed affecting a situation. What is affected by a change in payoff rules is the net level of beneﬁts or costs assigned to a particular path of actions and physical results. Thus, to examine the effect of rules in a careful and systematic manner, one needs toconsider the underlying physical transformations separately from the material rewards assigned a chain of actions and results.
One example of the essential difference is between the amount of goods
produced during a particular time period and the net ﬁnancial sums assigned to participants in positions (workers, managers, owners, etc.) forthat time period. It is also important to consider the internal valuation
placed by a participant on the rewards and costs assigned to physicalresults. Thus, in the approach taken in this volume, there are three components to what individuals value as outcomes: (1) the physical results obtained as a result of a chain of actions by participants, (2) the material
rewards or costs assigned to actions and results by payoff rules, and (3)the valuation placed on the combination of the ﬁrst and second components by the participants. The valuation placed on exchanging potatoes
for chickens would be quite different—perhaps even negative—for a vegetarian household, as contrasted to one that enjoyed eating chicken.
The state variables affected by a situation, however measured, are what
the participants in an action situation are thought of as affecting. A frequent assumption is made that the participants self-consciously decide toaffect particular results or to leave them in as they were in the statusquo. Analysts can also include “unintended outcomes” within the set of
potential outcomes included in an action situation. Polluters, for example,
frequently do not know the full range of physical results generated as aresult of their actions.
THE OPPORTUNITY INVOLVED IN AN ACTION SITUATION

The “opportunity” involved in an action situation can be deﬁned as therange of the values of the outcome variables potentially affected in a situation (von Wright 1966, 124; Commons [1924] 1968, 67). If one variable
is affected, the opportunity existing in a situation is the difference between
the highest and lowest achievable value on that outcome variable. Whenthat variable is dichotomous, its presence or absence is the full range ofvariation. The range on a quantiﬁed variable may be small or great. The
level of opportunity in a market exchange, for example, is the difference

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CHAPTER TWO

between the lowest price offered by a potential buyer and the highest priceoffered by a potential seller. When that range is severely constrained, themarket does not offer much opportunity for affecting the price that abuyer obtains or a seller must pay. When the range is large, the market
offers considerable opportunity for affecting the price (Commons [1924]
1968, 66–67). When multiple state variables are included as potential
outcomes, the concept of opportunity becomes more difﬁcult to measure
but involves a cumulative score of the range of all the state variables affected using concepts such as net beneﬁts or utility.
THE STATUS QUO OUTCOME

The status quo is a concept used to describe the relationship between thevalues of the end-state variables compared to the values of the initial-statevariables. If none of the values on any of the state variables has changed,
one concludes that the outcome remains the “status quo.” In the Snatch
Game, the status quo exists under two conditions: (1) if the ﬁrst player
does not make an offer or (2) if the ﬁrst player does make an offer andthe second player refuses it. When one conceptualizes the outcomes of alegislative session as the values of a set of policy variables, the status quo
is always included in the set of potential outcomes. Unless there is a minimum winning coalition within those authorized to change particular policy variables, the status quo policy will continue in effect.
As discussed in some detail in the last half of this chapter, the outcomeof one action situation may include the possibility or necessity of proceeding to another situation. Thus, the outcome of a committee hearing in
the U.S. House of Representatives may be the successful reporting out of
proposed legislation. Combined with this success, however, is the necessity to take action on the ﬂoor of the House, in a committee of the Senate,on the ﬂoor of the Senate, and potentially in a combined Senate-House
committee that reports back to both houses that again reconsiders thelegislation one more time (Shepsle and Weingast 1984).
If one action situation is not reached until after particular outcomes areachieved in other action situations, the ﬁrst situation can be considered a“contingent action situation” (Coleman 1973, 64). The internal behaviorof participants in most organizations can be analyzed as a complex seriesof differentially structured action situations linked by a set of procedural
rules. When analyzing the “actions” of one organization in a multiorganizational setting, many of these internal situations will not be separately
analyzed and some overall method for determining the most likely action
to be taken by a particular type of organization in a speciﬁc type of action
situation will be substituted for the full chain of linked situations. In work

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45on two-level games, McGinnis and Williams (1989) analyze international
bargaining situations where they examine both the interaction of nations
in a bargaining situation and then the internal structure of relationships
within the respective governments.5
Actions
Participants assigned to a position in an action situation must choose
from a set of actions at any particular stage in a decision process. An
action can be thought of as a selection of a setting or a value on a control
variable (e.g., a dial or switch) that a participant hopes will affect an
outcome variable. In game theory, the set of actions available to a participant at a speciﬁc sequence in a game—a decision point—is the available
moves. The speciﬁc action selected by a participant from the set of authorized actions is called a choice. In the Snatch Game, the ﬁrst player has achoice between two moves (offer or not offer) and the second player achoice among three moves (refuse, snatch, or exchange). A complete speciﬁcation of the moves to be taken in all possible contingencies in a oneshot or repeated game is called a strategy.
The types of variables included within the concept of a set of authorized
actions are vast. Examples of typical action sets available to participants
who hold positions in different types of situations are arrayed in table2.1. The term “action” thus includes both overt acts as well as the choice
not to act in some situations or “forbearances” (von Wright 1966). Bothvoting for one or another candidate and abstaining from voting are included in a voter’s action set, for example. Both can be conceptualized asa setting on a control variable.
Action-Outcome Linkages
A setting on a control variable is considered “linked” to a state variable
when it is possible to use that setting to cause the state variable (1) to comeinto being, (2) to disappear, or (3) to change in degree. A light switch, forexample, is a control variable with two positions—on and off. It is linked
to some source of light that shines or does not shine. By turning the switch
to off, one can make the light disappear. By turning the switch to on, one
can produce light. In this situation, the control variable does not change
unless a human actor takes a positive action to change it. Since the statevariable may also change as a result of some physical process (such as thelight bulb burning out), the actor can be said to have some effect on theoutcome by knowing the linkage and choosing whether to change the

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CHAPTER TWO

TABLE 2.1
Action sets assigned to positions in several “typical” action situations
Position to which action set is assigned

Authorized actions included in action set

A voter in an election

Vote for Candidate i, Candidate j, or
abstain from voting

A seller in a competitive market

Sell up to X quantity of good Y at thegoing price; do not sell any of good Y at
the going price

A member of a legislature

Submit an amendment to add provisions
to an already submitted bill; submit
an amendment to delete or change
provisions to an already submitted
bill; submit no amendments

A chair of a committee

Order the votes on an original bill (B), an
amendment (A), and the status quo (Q)in any of the three following ways:(1) A vs. B and then the winner vs. Q(2) A vs. Q and then the winner vs. B(3) B vs. Q and then the winner vs. A

setting (take a positive action) or to retain the old setting (to forbear fromtaking a positive action).
A state variable may be linked to many control variables. One might
think of a situation in which three switches jointly control a light—atleast two of them must be in the “on” position for the light to appear. A
person authorized to set one of the switches to on or off can potentiallyaffect whether the light is on or off, but cannot totally control the presenceor absence of light. If only one other switch is turned on, a person assigned
to one of the other two switches can either make the light appear (byturning their switch on) or can produce darkness (by turning their switchoff). If one person’s switch is already off, the other person can allow darkness to continue by refraining from changing his or her control switch. It
is in this sense that a “nonaction” may affect an outcome variable. Since
all three light switches are linked to the light, choices to change the switch
position or leave it the same are both considered to be an action.
To give some political content to these concepts of action and transformation functions, let us analyze the situation facing the chair of a ﬁveperson county council located in a state that authorizes local options regarding the legality of alcohol being sold within the county boundaries.
The other four members of the council are divided equally on the issue.
On a tie vote, a chair can vote in favor of a law, vote against a law, or
abstain. What effect his or her positive action (voting for or against) or47

L I N K I N G A C T I O N S I T U AT I O N STABLE 2.2
Results if collective-choice rules specify that a tie vote retains the status quo
Selling liquor in
the county is

Legislation
proposes

Result with tie

Act of chair

Final outcome

IllegalIllegalIllegal(Forbearing to let a state remain absent)

AbstainIllegalIllegalLegal
Illegal(Acting to make a state come into being)

Vote Yes

Legal

Legal
Illegal(Acting to make a state disappear)

Legal

Vote Yes

Illegal

Legal
Legal
(Forbearing to let a state continue)

Legal

Abstain

Legal

TABLE 2.3
Results if collective-choice rules specify that a tie vote leads to a change
Selling liquor in
the county is

Legislation
proposes

Result with tie

Act of chair

Final outcome

IllegalLegal
Legal(Acting to keep a state from occurring)

Vote No

IllegalIllegalLegal
Legal(Forbearing to let a state come into being)

Abstain

Legal

Legal
Illegal(Forbearing to let a state disappear)

IllegalAbstainIllegalLegal
IllegalIllegal(Acting to prevent a state from disappearing)

Vote No

Legal

forbearance (abstaining) has depends on the rules governing what happens if there is a tie vote, as shown in tables 2.2 and 2.3.
Any speciﬁc situation would not have all eight elementary modes ofaction and forbearance present at once. Which subsets of the eight would
be included in an action situation obviously depend on the collectivechoice rules of a particular situation regarding what happens with a tie
vote. Actions designed to produce a certain state or an outcome are frequently called production functions. Various combinations of materials,
time, and effort are used to produce a commodity. Transformation functions are not all as determinate as those presented above. Stochastic transformation functions relate actions to outcomes in a probabilistic manner.
Thus, the concepts of certainty, risk, and uncertainty are relevant to considerations of these linkages.

48

CHAPTER TWOCERTAINTY, RISK, AND UNCERTAINTY

In action situations that are formally analyzed, the linkage of actions tooutcomes is usually presumed to be knowable. In such situations, one
could represent the chain of actions and results as a tree, as in the Snatch
Game. When the full tree or matrix is presumed to be known to the participants, analysts refer to three types of linkage between actions and outcomes: certainty, risk, and uncertainty.
In a certain linkage, every available action is linked directly with one
and only one outcome. The perfectly competitive market, as represented
in neoclassical economic theory, links actions to outcomes in a certain
manner. Neither sellers nor buyers in a perfectly competitive market haveany control over price. The price at which alternative quantities of a product can be bought and sold is determined by market forces, and thus certain, in the short run. Offers to buy or sell at the market price lead to one
and only one outcome.
The analytical world of certainty is vast and includes much of the formal theory in economics and management science. Efforts to ﬁnd maxima
and minima of functions, feasible regions, production possibility frontiers, and so forth, are all modes of analysis appropriate for situations
characterized by certainty. Outside of formally organized large-scale markets, few interactive situations are likely to have one-to-one relationships
between actions and outcomes.
Action-outcome linkages that are considered to be risky or uncertain
involve one-to-many relationships between actions and outcomes, like
both games presented above. In a situation characterized by risk, the objective probabilities’ relationships between each action and set of outcomes can be known (F. Knight 1921; Cashdan 1990). A classic situationof this type is the urn ﬁlled with a known number of red and black balls
where the individual must decide upon actions that can lead to one or
another outcome depending upon the color of the next ball drawn fromthe urn. A roulette wheel is an instrument of risk. Insurance ﬁrms face aworld characterized by risk when they calculate premiums to be charged
for different types of insurance policies. Once data about the distributionof past events are available, probabilities can be assigned to different distributions of future events. When the probability that particular actions
will lead to particular outcomes is known, a probability matrix can be
developed that indicates the probability of each outcome occurring given
particular combinations of states of affairs and actions.
An essential indeterminacy of social interactions remains in the context
of many action situations. This indeterminacy is called uncertainty (F.
Knight 1921). When institutional arrangements leave open wide avenues
for choice, and each individual’s outcome is dependent upon the actions

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49taken by others, uncertainty will characterize the resulting decision situation. Uncertainty characterizes a situation in which the probabilities of
speciﬁc actions leading to outcomes are unknowable. The set of actions
and the set of outcomes are still assumed to be ﬁnite and knowable. The
linkages between actions and outcomes are also presumed to be knowable. Most formal games are characterized by uncertainty, as are mostsituations existing in the political realm. The potential decision of theindividual is taken into account in the decision making of the “other.”
Each individual must decide in the presence of at least one “strategicother” whose decision to act will be contingent upon expectations about
the individual’s action (Coleman 1973, 42). For situations that can be
represented as formal games, the Nash equilibrium and its reﬁnements
are a method for predicting the likely action-outcome linkages assumingthat all participants view the situation with common knowledge about its
structure and the rationality of the other players. Anthropologists andecologists have delved into a number of fascinating questions related tothe presence of substantial risk in the ecology in which humans ﬁnd themselves (see Kaplan, Hill, and Hurtado 1990; Low 1990). Hawkes, O’Connell, and Blurton Jones (2001), for example, explain the frequently observed relationship that hunted meat from large animals is widely shared,
while food obtained from subsistence agriculture is not as related to thedifference in the risk of obtaining food from these two activities (see also
Kameda, Takezawa, and Hastie 2003 for an excellent synthesis of thedebate over explanation).
The distinction presented here among certainty, risk, and uncertainty
focuses entirely on structural aspects of the situation (linkage patterns)
and not on the level of information that an individual has about the situation. Theorists sometimes blur this distinction. This blurring leads to considerable confusion when they treat the uncertainty in the situation andthe lack of information by an individual as the same phenomena. The
separation of situational and cognitive aspects of uncertainty draws heavily on the earlier work of F. Knight (1921) and Buchanan and di Pierro(1980), who stress the need to distinguish between the structure of thesituation and the cognition of the individual. H. Simon (1955) and Williamson (1975) also distinguish between situational complexity and uncertainty on the one hand and cognitive limitations on the other.
Control
The extent of control over the linkage of the action to outcomes that a
participant has varies from absolute to almost none. An individual has
total control (omnipotence) over an outcome variable, which we may calloi in a situation if for each value oi potentially affected within that situa-

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CHAPTER TWO

tion there is an action ai , such that the conditional probability of oi given
ai equals one. For two-dimensional outcomes, an individual has total control if for each combination of outcome variable values oioj there is an ai,
such that the conditional probability of oioj given ai equals one (Coleman
1973, 61).
An individual has partial control over a state variable if the conditional
probability of a change in the value oi of the state variable given an action
ai available to the individual is greater than zero and less than one. Partial
control can, thus, vary from an extremely small chance of affecting an
outcome to a high probability of affecting the outcome. A participant canbe said to be impotent with respect to an outcome when he or she has nocontrol over the values of a state variable (see von Wright 1966, 129–31
for a discussion of control that is similar to that of Coleman).
The “power” of an individual in a situation is the value of the opportunity (the range in the outcomes afforded by the situation) times the extent
of control. Thus, an individual can have a small degree of power, eventhough the individual has absolute control if the amount of opportunity
in a situation is small. The amount of power may also be small when theopportunity is large, but the individual has only a small degree of control.
Action situations may involve differential distributions of control and opportunity to different individuals in the situation. Consequently, individuals may differ in the amount of power they have in the situation. Conceptsof opportunity, control, and power are thus deﬁned as situation-dependent. A single individual may have dramatically differing levels of control,
opportunity, and power in the different action situations in which they
participate. An executive may have more power in regard to those issues
where the executive must initiate action than when the executive can only
stop action.
Information about the Action Situation
Participants in an action situation may have access to complete or incomplete information. Almost all formal representations of action situations
assume that participants have access to complete information by whichis meant that each participant could know the number of other participants, the positions, the outcomes, the actions available, how actions arelinked to outcomes (and thus the certainty, risk, or uncertainty of thelinkage), the information available to other players, and the payoffs ofthe same. In other words, complete information is an assumption thateach participant could know the full structure of an action situation as
deﬁned here.
When participants are assumed to have access to complete information,a further distinction is made in formal theory between perfect and imper-

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51fect information. When a participant has access to perfect information,
they cannot only know all of their own past actions, they can also know
the actions of all other players before they make any move. In otherwords, they can know the exact decision node at which they are making
a choice. Household 2 in the Snatch Game has perfect information. Under
imperfect but complete information, the individual is assumed to haveaccess to knowledge of the full structure of the situation, but may nothave access to knowledge about all the moves that other participants havetaken prior to a particular move. The participants could know all thepossible nodes at which he or she could be, but is unable to distinguishthe exact node for the current move.
When information is less than complete, the question of who knows
what at what juncture becomes very important. With incomplete information, how much any one individual contributes to a joint undertaking isoften difﬁcult for others to judge. When joint outcomes depend on multiple actors contributing inputs that are costly and difﬁcult to measure,incentives exist for individuals to behave opportunistically (Williamson
1975). Opportunism—deceitful behavior intended to improve one’s own
welfare at the expense of others—may take many forms, from inconsequential, perhaps unconscious, shirking to a carefully calculated effort todefraud others with whom one is engaged in ongoing relationships. The
opportunism of individuals who may say one thing and do something else
further compounds the problem of incomplete information.
In many work situations, a boss cannot know exactly what employees
are doing and how this adds to or takes away from the accomplishmentof a joint output. Developing a contract that motivates them to be highly
productive has long been recognized as a challenge (Barnard 1938). In
the contemporary economics literature, the problem of a principal negotiating with an agent—when the agent’s actions cannot be fully known
to the principal—is known as the Principal-Agent problem (Fudenberg,
Holmström, and Milgrom 1990; Laffont and Martimort 2002). Asymmetric information problems also occur when individuals try to develop
a contract to share risk. An insurance ﬁrm can never know the intentions
and behavior of those it insures. Thus, all insurance situations involve a
moral hazard problem (Arrow 1963).
When action situations are being analyzed in a less formal manner,
analysts are more apt to assume that participants have access to incomplete information about the full set of actions available to them, the fullset of outcomes, and about how actions are linked to outcomes. This
assumption is frequently linked to fundamental assumptions made about
the limitations of human cognitive abilities in a model of the individual.
However, the usefulness of an assumption of complete or incomplete information also relates to the complexity of the action situation being mod-

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eled and the language structure used, the channels of communication, andthe type of communication normally assumed to be present in the action
situation.
Costs and Beneﬁts
The discussion of outcomes, and of action-outcome linkages presented
above, relies on the relationships between control variables and state variables. In addition to the physical actions and outcomes that are involvedin a situation, rewards and/or sanctions may be distributed to participantsin positions dependent upon the path taken to achieve a particular outcome. Costs and beneﬁts are cumulative. External rewards or sanctions
assigned to outcomes include the rewards (ﬁnancial returns) or sanctions(taxes or ﬁnes) assigned to actions taken along a path to an outcome.
For simplicity, it is frequently assumed in theoretical models that acts
are costly and outcomes are beneﬁcial. Participants are then viewed asweighing the costs of an action against the beneﬁts of an outcome. Actions
may, however, have associated beneﬁts, and outcomes may be “bads”
instead of “goods.” In the Snatch Game, for example, one of the outcomesis very bad for Household 1 and very good for Household 2. All actions
are represented in ﬁgure 2.2 as having zero costs. If the place where
Households 1 and 2 meet is located some distance away from each of
their farms, the cost of traveling to this location could be included in apayoff table. Thus, the ﬁnal external value assigned to an outcome for aparticipant in a position is the net value of the path taken to an outcome
given the choices made by the participant and those of others. The external value to a seller in a fully developed market setting is the proﬁt resulting from the sale of a quantity of goods minus the cost of buying or
producing those goods.
In conducting an institutional analysis, a distinction needs to be madebetween a physical outcome, an external reward or sanction, and the valuation that a participant assigns to the physical transformation and external rewards and/or sanctions. So long as the physical linkage between
actions and outcomes remains the same, the outcome remains the same.
A ﬁrm following a routine production process generates, on average, thesame quantity of a product per unit of time. If the price of the commodity
increases or decreases substantially, or a new tax is imposed on the saleof the commodity, the net proﬁts obtained differ substantially. The external or extrinsic values are the set of “reward” variables affected by thepath of actions and outcomes. Examples of extrinsic rewards include theﬁnancial returns assigned a worker in a Principal-Agent contract. The
reward may be assigned strictly on action variables (e.g., how many hours
the worker clocks in), strictly on outcome variables (e.g., how much of a

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53

particular ﬁnal or intermediate product is produced), or on some combination of action and outcome variables (a wage plus a production bonus).
Extrinsic beneﬁts and costs are frequently assigned through the operationof a rule system and thus do not rely only on biophysical processes.
If the primary interest is to predict what will happen in a particularsituation, and not how to change the situation, the only value that a researcher needs to use is the net value assigned by participants to theachievement of an outcome. This value is referred to as utility in economics and game theory. Utility is a summary measure of all the net values tothe individuals of all the beneﬁts and costs of arriving at a particularoutcome. Game theorists use utility values in representing an outcome
and only rarely decompose this into component costs and beneﬁts. Some
individuals may pay attention only to the objective, extrinsic valuation
assigned to an action-outcome link. For simplicity, many analysts assume
that subjective utility is positively associated with the net value of theexternal rewards. In economics, theorists normally assume that utility ismonotonically associated with proﬁts, for example. As discussed extensively in chapter 4, this assumption is reasonable to make in many but not
all situations. Individuals may assign either a positive or negative intrinsic
value to actions or outcomes. Participants in the same situation may noteven assign the same internal valuation to the objective outcome (seeSchiemann 2002).
The intrinsic valuation attached to an external reward or sanction is
the internal value that individuals associate with the components of theobjective transformations and rewards (Frey 1997a; Deci 1975). Joy,
shame, regret, and guilt are all forms of intrinsic values. If the person
evaluates an action as being improper, they may assign a negative intrinsic
value. If the person is proud of an action, they may assign it a positive
intrinsic value. Measuring intrinsic valuation is extremely challenging. In
fact, dissecting the holon referred to as “the participant” in this framework is among the more challenging tasks an institutional analyst faces.
Thus, all of chapter 4 is devoted to a beginning elucidation of this process.
The Number of Times the Action Situation Will Be Repeated
In addition to the internal components of an action situation, it is also
important to know whether the situation is a one-shot or a repeated interaction. Analysts usually agree, for example, that the outcomes that individuals will obtain in a social dilemma game like a Prisoner’s Dilemmaor the Snatch Game will depend, among other factors, on whether theparticipants are engaged in a one-time encounter or over an indeﬁnitely
long sequence of plays. The predicted equilibrium in a single-play Snatch

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CHAPTER TWO

Game (assuming that both players do not know one another and value
only the objective outcomes) is that neither participant would trust theother when in the ﬁrst position. Thus, both players are worse off thanthey could be if they exchanged commodities. If this game were repeateda ﬁnite number of times, most game theorists would predict the sameequilibrium as for a single-round game due to backward induction. The
last play of this game is similar to the single-round game. Given this,analysts predict that participants will not cooperate on this last round.
Once the participants have decided not to cooperate on the last round,they would also decide not to cooperate on the second-to-the-last round,and so forth, back to the ﬁrst round.
When participants in a social dilemma game are placed into an indeﬁnite series of rounds, however, the disadvantage of continued lack of cooperation can lead them to adopt a conditional cooperative strategy solong as other participants also cooperate. The well-known folk theoremof game theory establishes that full cooperation is one of the feasible equilibria that participants in an inﬁnitely repeated (or even an indeﬁnitely
repeated) situation may achieve if they use one of several conditional cooperative strategies (Kreps et al. 1982). It is, however, only one of manyequilibria. Thus, participants face a challenging coordination problem inreaching this outcome.
One of the most famous and useful conditional strategies is “Tit forTat,” where players in a repeated, symmetric social dilemma cooperateon their ﬁrst round and then take whatever action their counterpart took
in the last round (Axelrod 1984). If there are only two participants in a
repeated, symmetric social dilemma situation, they can monitor and punish one another through future actions. Thus, whenever one of the participants tries to take advantage of the other participants, the second participant can directly punish the ﬁrst participant on the next round. Once agroup is larger than two, simple strategies or heuristics like Tit for Tat aremuch harder to implement since an error by one person may set the entire
group into a never-ending series of retributions.
The possibility of participating in a repeated symmetric situation, then,
may be enough to lead small groups of individuals to seek out mutuallyadvantageous strategies when they might not otherwise do so. For asymmetric social dilemmas, however, such as the Snatch Game, there is no
single, simple heuristic—like Tit for Tat—that can be successfully employed to reach the higher joint outcome. This is due to the fact that theﬁrst player can simply stay with the status quo, and what the second
player would do is irrelevant. The second player does not have an easy
method for “punishing” the ﬁrst player in later rounds as there may be
no later rounds. Scholars are working on a set of heuristics that are pos-

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55

ited to help individuals cope with asymmetric, social dilemma situations
where trust and reciprocity are so very important (see Rieskamp andGigerenzer 2003).

Linking Action Arenas
In addition to analysis that digs deeper into the exogenous factors affecting action arenas, an important development in institutional analysis is
the examination of linked arenas. Whereas the concept of a “single” arena
may include large numbers of participants and complex chains of action,
most of social reality is composed of multiple arenas linked sequentiallyor simultaneously (see Shubik 1986). Rarely do action situations exist
entirely independently of other situations. Political scientists frequently
study processes within a legislature where decisions are ﬁrst made in acommittee, then in one house, then in a second house, then in conference
committees, then by the chief executive. Decisions inside one unit of a
private ﬁrm must also go through a sequence of action situations before
they are ﬁnal. Given the importance of repeated interactions to the development of a reputation for reciprocity and the importance of reciprocity
for achieving higher levels of outcome over time, individuals have a strong
motivation to link situations so as to utilize the capabilities achieved
through gaining a reputation in one situation that helps to provide credibility in others.
Action situations are linked in two different and important ways. The
ﬁrst kind is an organizational linkage. Within larger organizations, what
happens in the purchasing department affects what happens in the production and sales departments and vice versa. For some purposes, it isuseful to isolate a single situation in an organization to try to understandwhy particular kinds of outcomes are achieved in that situation without
embedding the situation of interest in a larger lattice of related situations.
Further, interesting and important institutional arrangements for coordinating complex chains of actions among large numbers of actors involve
multiple organizations competing with one another according to a set ofrules. Markets are the most frequently studied institutional arrangementsthat achieve coordination by relying primarily on rule-governed, competitive relationships among organizations. Rule-governed competition
among two or more political parties is considered by many analysts to be
an important requisite for a democratic polity. Less studied, but potentially as important a means for achieving responsiveness and efﬁciency inproducing public goods and services are arrangements that allow rule-

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CHAPTER TWO

ordered competition among two or more potential producers of public
goods and services (see McGinnis 1999b).
A more fundamental form of linking is shifting levels of analysis fromone situation to a deeper rule-changing situation. All action situations
where individuals engage in the provision, production, distribution, appropriation, assignment, or consumption of goods and services are classiﬁed as operational situations. Operational situations are themselves affected by the biophysical world, the rules affecting the structure of theoperational situation, and the attributes of the community in which thesituation was embedded. If operational situations were all that is analyzed
in this book, we would not need to go further than the presentation of
the Institutional Analysis and Development (IAD) framework for a single
level of analysis. In this case, the rules analyzed are presumed to be ﬁxed
and the linkage between a situation at one level (where rules are assumed
to be ﬁxed and unchanging) and another level (where rules are themselves
the outcomes produced) is unnecessary.
We do, however, want to understand how operational situations arelinked to a deeper situation where the rules of the operational situation
are made or modiﬁed. Not only are there two levels of analysis; one candig deeper into where the rules come from in a collective-choice situation
to examine constitutional-choice situations. For practical reasons, westop the digging at a metaconstitutional level that is relevant when individuals are in the process of constituting or reconstituting ongoing relationships. A metaconstitutional level occurs when participants examine
the consequences of diverse rules for who is to represent them in a constitutional process.
In this chapter, we will focus ﬁrst on linked action situations where
the outcomes from one situation are eventually fed into a series of othersituations, but where rules themselves remain relatively constant. Thenwe will address the deeper question of how to examine the linkage between one situation and its immediate deeper-level situation where therules of the ﬁrst situation may be changed.
Organizational Linkages
Behavior within any particular situation may depend upon expected outcomes in another situation. As mentioned above, a potential outcome of
one situation may be to enter a second situation. Some social processes
may need to be thought of as composed of a series of linked situations.
The outcomes of any one situation become inputs into the next situation.
The intermediate outcomes of an early situation may not have much value
unless the full series of linked situations is completed. Thus, getting a bill

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57

passed by the House of Representatives can be viewed as an intermediatestep in ﬁnally getting a bill passed into law. The full set of situations within
the Senate and the signature by the president are necessary before a bill
becomes a law. The intermediate outcome is a necessary step for the ﬁnal
outcome, but may not have much assigned worth.
An organization is composed of one or more (usually more) action situations linked together by prescriptions specifying how outcomes from one
situation become inputs into others. Organizations may be thought of asa tree or a lattice with situations at each node. A particular set of rules
structures the situation at each node. A general set of rules partially structures all internal situations and speciﬁes the paths that may be chosenfrom one situation to the next. Thus, a tournament is one form of organization that prescribes how players will proceed through the tournamenttree. Many bureaucratic organizations have a similar general structure tothat of tournaments.
Where one draws the boundaries on the analysis of linked situations
depends on the questions of interest to the analyst. In regard to collectivechoice action situations, one may want to understand why some kinds ofcandidates are selected in elections and not others. If this is the only question, one can focus on primary elections and the relationships among
diverse supporters and the policy position of candidates in a “candidate
selection game” (see Downs 1957 for the classic analysis of this situation).
Other situations are only potentially linked together. Thus, many commercial transactions occur within organizations or across market situations. Most of these occur without any reference to a court. However,invoking court proceedings is a very important method available to participants in these transactions to constrain the action set of other participants to those that have been agreed to. If it is extremely costly to use
court proceedings, then one would predict that the action sets of participants would be more likely to include some actions that are illegal or areoutside the bounds of a contract. Thus, the relative ease and availabilityof such action situations as courts to serve as monitors, sanctioners, andto provide remedies is extremely important in understanding the behaviorof actors within any particular situation.
Actions and outcomes that occur in one location may also stimulate
reactions by others located in another situation located elsewhere. Firms
that are highly proﬁtable are frequently used as models by others as to
how to organize themselves for success. Farm households who innovate
and are successful or common-property arrangements that increase theirjoint yield are frequently copied by others. These connections are like aripple across the landscape rather than strongly linked situations.

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CHAPTER TWO

Multiple Levels of Analysis
Besides multiple and nested action arenas at any one level of analysis,
nesting of arenas also occurs across several levels of analysis. All rules arenested in another set of rules that deﬁne how the ﬁrst set of rules can bechanged. The nesting of rules within rules at several levels is similar to
the nesting of computer languages at several levels. What can be done ata higher level will depend on the capabilities and limits of the rules atthat level and at a deeper level. Whenever one addresses questions about
institutional change, as contrasted to ongoing actions within institutional
constraints, it is necessary to recognize that:
1. Changes in the rules used to order action at one level occur within a currently “ﬁxed” set of rules at a deeper level.
2. Changes in deeper-level rules usually are more difﬁcult and more costly
to accomplish, thus increasing the stability of mutual expectations among individuals interacting according to the deeper set of rules.

As mentioned earlier in the chapter, it is useful to distinguish three levelsof rules that cumulatively affect the actions taken and outcomes obtainedin any setting (Kiser and Ostrom 1982).6 Operational rules directly affectday-to-day decisions made by the participants in any setting. These canchange relatively rapidly—from day to day. Collective-choice rules affectoperational activities and results through their effects in determining who
is eligible to be a participant and the speciﬁc rules to be used in changing
operational rules. These change at a much slower pace. Constitutionalchoice rules ﬁrst affect collective-choice activities by determining who iseligible to be a participant and the rules to be used in crafting the setof collective-choice rules that, in turn, affect the set of operational rules(Brennan and Buchanan 1985; Buchanan and Tullock 1962). Constitutional-choice rules change at the slowest pace. One can even think about a“metaconstitutional” level underlying all the others that is not frequently
analyzed. One can think of the linkages among these rules and related
level of analysis as shown in ﬁgure 2.3.7
For most practical applications, three levels are enough.8 No theoreticaljustiﬁcation exists for three and only three levels (Diermeier and Krehbiel
2003). For the purposes of formal theory, we may need to assume as longa series of layers as is needed until we hit rock bottom—the biophysicalworld. Very deep layering—even inﬁnite layering—turns out to be neededin many aspects of formal theory. Game theorists, for example, have had
to assume that the common understanding needed for one to assume that
there is a game is nested inﬁnitely. “Information is common knowledge if
it is known to all players, each player knows that all of them know it,
and each of them knows that all of them know that all of them know it,59

L I N K I N G A C T I O N S I T U AT I O N S

Individuals' Actions Taken that Directly AffectState Variables in the WorldOPERATIONAL SITUATIONS(Provision, Production, Distribution, Appropriation, Assignment, Consumption)

Biophysical World

Operational
Rules-in-Use

Community

Individuals' Actions Taken that Directly AffectRules that Affect Operational Situations
COLLECTIVE-CHOICE SITUATIONS(Prescribing, Invoking, Monitoring, Applying, Enforcing)

Biophysical World

Collective Choice
Rules-in-Use

Community

Individuals' Actions Taken that Directly AffectRules that Affect Collective-Choice Situations
CONSTITUTIONAL SITUATIONS(Prescribing, Invoking, Monitoring, Applying, Enforcing)

Biophysical World

Constitutional
Rules-in-Use

Community

Individuals' Actions Taken that Directly AffectRules that Affect Constitutional Situations
METACONSTITUTIONAL SITUATIONS(Prescribing, Invoking, Monitoring, Applying, Enforcing)

Biophysical World

Community

Figure 2.3 Levels of analysis and outcomes. Source: E. Ostrom 1999, 60.

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and so forth ad inﬁnitum” (Rasmusen 1989, 50). Thus, one can always
assume that there are even more primitive rules underlying those that
one is analyzing at any one level—thus our positing of a very general
metaconstitutional level—until one gets to the constraints of a biophysicalworld. At that point, the only distinction that can be made among actors
is their strictly physical strength, as Hobbes long ago recognized. For mostinstitutional analyses, three nested levels are sufﬁcient.
At an operational tier, participants interact in light of the internal andexternal incentives they face to generate outcomes directly in the world.
Examples of operational-level situations include:• Families deciding to move or not move into an urban neighborhood depending on the proportion of neighbors who share attributes (such as wealth,race, ethnicity) with them in the potential future neighborhood as comparedto their present neighborhood (see H. Young 1998, 62–65; Schelling 1978).
• Workers hired by a boss to undertake a complex task who have private information about their level of effort and face incentives to keep effort at a minimum (for examples from a developed economy perspective, see Miller 2001;
and from a developing economy perspective, see Ensminger 2001).
• Harvesters from a common-pool resource deciding how much, when, andwith what technology to appropriate resource units (Gibson, McKean, andOstrom 2000).

The participants in collective-choice games may be the same participants as in linked operational-choice games (as when all ﬁrms in an industry agree upon a particular industry standard that they will all use in
manufacturing goods). Or, participants in collective-choice games may
differ from those in operational games. They may, for example, be legislative representatives selected in electoral games (themselves part of the collective-choice level of action) to be the agents of a set of principals—
the citizens engaged in a wide diversity of operational games that will beaffected over time by legislative policies. Examples of collective-choice(policy choice) situations include:• devising allocation schemes that can be used by NASA in allocating uses ofspace stations (Plott and Porter 1996);• elected representatives in diversely structured legislatures devising policies based on popular preference distributions (Hammond and Butler
2003);• citizens in diversely structured European countries making electoral decisions in light of the information generated by their political systems (Benzand Stutzer 2004).

Policy decisions affect the structure of arenas where individuals are making operational decisions and thus impacting directly on a biophysicalworld. The situation could as well be at a constitutional tier, where deci-

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61sions are made about who is eligible to participate in policymaking andthe rules that will be used to undertake policymaking.
Participants in the third level can again either be participants in theother two levels or not. And, participants in constitutional choices may
not recognize that they are making a constitutional rule—they may be
simply trying to ﬁx a problem with the way that they have been making
policy choices over the last several years. Examples of constitutionalchoice arenas are:• a group of groundwater producers meeting to discuss the formation of apublic district in order to develop regulations on the amount of water withdrawn from the basin (Blomquist 1992);• representatives from regions within a country debating the design or modiﬁcation of a national constitution (V. Ostrom 1987; Reynolds 2002; Dougherty 2001);• representatives from countries in regions that are developing strategies tocreate new multicountry unions for some purposes and possibly even tochange their borders (O’Leary, Lustick, and Callaghy 2001).

At each level of analysis there may be one or more arenas in which thetypes of decisions made at that level will occur. In the collective-choice,constitutional, and metaconstitutional situations, activities involve prescribing, invoking, monitoring, applying, and enforcing rules (Lasswell
and Kaplan 1950; Oakerson 1994).
The concept of an “arena” as described earlier does not necessarily
imply a formal setting, but can include such formal settings as legislaturesand courts. Policy making (or governance) regarding the rules that will
be used to regulate operational-level choices is usually carried out in one
or more collective-choice arenas, as shown in ﬁgure 2.4. Constitutional
arenas are frequently formal arenas, but these may occur within speciallycalled conventions, within formal courts, or within a legislature thatchanges a basic rule about making collective-choice rules.9
When a theorist chooses to analyze a situation at any particular level,
the theorist must assume that the institutional rules at that level are temporarily ﬁxed for the purpose of analysis. These rules form a part of the structure of the situation rather than the solution to the game created by thatstructure. When the purpose of analysis is to understand the origin of therules at one level, knowing the structure of the situation at the next level isessential for that enterprise. The equilibria achieved at one level are thus
supported by equilibria that have been achieved at deeper levels.10 Understanding the role of these nested levels does not, however, require that theanalyst specify the full supporting infrastructure in elucidating how individuals are expected to behave at one level.11 Thus, assuming that there are
multiple levels where decisions are made that affect actions at other levels
actually greatly simpliﬁes analysis rather than complicating it.

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National, regional, and/or local
formal collective-choice arenas
Legislatures
Regulatory agencies
CourtsFormal third-party monitoringand enforcement activities

Operationalrules-in-use

Self-organized collective-choice
arenasInformal gatherings
Appropriation teams
Private associations

Informal third-party monitoringand enforcement activities

Figure 2.4 Relationships of formal and informal collective-choice arenas. Source:
Adapted from E. Ostrom 1990, 53.

Level-Shifting Strategies
An individual engages in “level shifting” whenever he or she begins tocontemplate how to change any of the constraints on an operational situation (or, on a collective-choice situation) that are potentially under thecontrol of the participants in that situation. A groundwater pumper, forexample, attempts to shift levels when he or she states to another pumper,“Hey, this race of ours to withdraw water is going to destroy the basin
and leave us all worse off than we could be. Why don’t we change ourrules so as to avoid such a disaster?”Any participant can shift levels of analysis in his or her own thinking
at any point in time while engaged in action at a different level. Before aresource user suggests a rule to another user, the ﬁrst has already analyzed
some of the consequences of adopting a revised set of rules. Shifting levelsof analysis can be accomplished by any actor operating independently ofothers. That actor simply contemplates the opportunities and constraints
that might be available at a different level for solving some of the problems occurring at a current level.
When the individual estimates that substantial beneﬁts are likely tooccur if others agree to an actual shift of levels of action and to change
rules, the individual may then be willing to invest resources to try to convince others that they should agree on a shift and consider the constraints
that are currently in effect. Shifting levels of action does not mean deciding to change the rules by making the shift. Shifting enables only those
who shift to contemplate overtly a different set of rules (or other constraints that may potentially be under their control, such as making acapital investment in their joint enterprise.) One possible result of a shiftof levels may be to keep the status quo rules.

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63

In ﬁeld settings, it is hard to tell where one situation starts and anotherstops. Life continues in what appears to be a seamless web as individuals
move from home to market to work (action situations typically characterized by reciprocity, by exchange, or by team problem solving or command). Further, within arenas, choices of actions within a set of rules ascontrasted to choices among future rules are frequently made without
recognizing that the level of action has shifted. So, when a “boss” says toan “employee,” “How about changing the way we do X?” and the two
discuss options and jointly agree upon a better way, they have shiftedfrom taking actions within previously established rules to making decisions about the rules structuring future actions. In other words, using IAD
language, they have shifted to a collective-choice arena.
The costs of shifting levels of decision making vary dramatically fromone setting to another. In some settings the same individuals are involvedin constitutional, collective-choice, and operational situations. Shifting
levels may be accomplished at low costs. A group of resource users discussing common problems at an operational level may somewhat naturally turn to a discussion of what could be done to solve any of theircurrent problems potentially including overuse, underinvestment, lack of
information, and the like. Part of this discourse may be focused on enforcing their current rules more effectively. Another part of their discussion
may include reference to alternative rules that might reassign their own
rights and duties. After such a discussion, they may agree to stay with
their current rules or seriously evaluate the likely effect of changing rules
and agreeing on a change.
Formal procedures—including petitions, court hearings, legislation, and/or referenda—may be required to shift levels of action in many situations.
Bureaucratic ofﬁcials may control access to an arena in which rules andother constraints could be changed. Judicial procedures may be used forthis purpose. The transaction costs of shifting levels and transforming an
ongoing situation may be very high. In such cases, participants at one levelmay continue to rely for long periods on rules that produce suboptimal
outcomes at that level because the expected costs of changing rules arehigher than the beneﬁts they could derive from a better set of rules. Alternatively, they may devise their own de facto rules that they enforce themselves
since they cannot turn to authorities to enforce them. Developing de facto
rules outside formal channels may be less costly than trying to use the formal channels available to participants in some political systems.
It is through shifting levels of action that participants may be able toself-consciously design rules in their efforts to change patterns of undesirable interactions and outcomes at operational or collective-choice levels.
The lack of a self-conscious examination of this strategy leads somescholars to presume that individuals facing social dilemma situations haveno way out but continuing suboptimal outcomes or relying on “external

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actors” to change the constraints they are facing. Garrett Hardin (1968),
for example, asks readers to picture a set of herders trapped in a “tragedyof the commons” that they could not themselves overcome. In an open,
self-organizing society with considerable opportunities for individuals
to devise their own rules for the public and private enterprises they establish, shifting levels of analysis is, however, something that participants dofrequently.
Once this possibility is seriously considered, however, it raises the problem of how to analyze rules themselves rather than simply taking them as
unchangeable constraints. This is the major challenge that will be addressed in chapters 5 to 9 of this volume. If rules are to be used as thetools of a self-governing society, the diversity of tools themselves must be
examined. This is a nontrivial problem, especially given the importanceof the biophysical world and the broader communities in which rules are
only one factor affecting structure.

Predicting Outcomes
Depending upon the analytical structure of a situation and the particularassumptions about the actor used, the analyst makes strong or weak inferences about results. In tightly constrained, one-shot action situations
under conditions of complete information, where participants are motivated to select particular strategies or chains of actions that jointly leadto stable equilibria, an analyst can frequently make strong inferences andspeciﬁc predictions about likely patterns of behavior and outcomes. Game
theorists draw on solution concepts, such as the Nash equilibrium, topredict outcomes.
Many ﬁeld situations, however, do not facilitate making clear predictions. Instead of completely independent decision making, individuals
may be embedded in communities where unobserved norms of fairness
and conservation may change the structure of a situation dramatically.
Within these situations, participants may adopt a broader range of strategies than expected using an assumption of a narrow self-interest. Further,individuals may change their strategies over time as they learn about theresults of past actions. The institutional analyst examining these more
open, less-constrained situations must make weaker inferences and predict the patterns of outcomes that are relatively more or less likely toresult from a particular type of situation. In an experimental laboratory,
for example, giving subjects in many types of social dilemmas an opportunity to communicate on a face-to-face basis is likely to substantiallychange the strategies chosen by subjects to ones that are not predicted bynoncooperative theory (see E. Ostrom, Gardner, and Walker 1994 andcites contained therein). This is not, however, a determinate process. The

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65variation in outcomes achieved is relatively large in groups that engage inface-to-face communication, as discussed in chapter 3.
In ﬁeld settings, one can assume that providing arenas where at least
some individuals engage in face-to-face discussions will usually change
the outcomes achieved. The important role of communication is achieving
a common understanding of the problems jointly faced. Discourse frequently generates ideas concerning various ways of coping more effectively with these problems. Further, communication helps participants tolearn what norms individuals share or do not share and whether sufﬁcienttrust exists that individuals can adopt plans of actions that depend on
trustworthy behavior (see E. Ostrom 1998, 2001). Even large communities whose forests are degraded can potentially turn a bad situation
around through their own efforts. Varughese (2000) documents how acommunity of over twenty-ﬁve hundred people organized its own forest
association and created subcommittees so as to involve more members in
face-to-face discussion and decisions. This community has devised many
innovative strategies for improving forest conditions while trying to keep
the costs on community members relatively low. Many historical factors,
as well as the current structure of the situation, affect the likelihood oforganization and communication. Thus, no determinate predictions canbe made.
Some situations within any one of these levels may be simple enoughthat one can generate a clear and empirically supported prediction about
likely behavior and outcomes—as one can, for example, in a highly competitive market producing goods characterized by low costs of exclusionand subtractability. Here, one can rely on well-tested results from priortheoretical and empirical work. It is usually much more difﬁcult to predict
results when one is analyzing a collective-choice or constitutional-choicelevel situation as it impacts on operational-level settings. The process ofchanging the property rights of inshore ﬁsheries in New Zealand by thenational government involved substantial conﬂict and reorganization
throughout its early history (Yandle and Dewees 2003). From an effort
to create strictly private transferable rights, the system has evolved into
a complex but workable system. Comanagement institutions have been
crafted to complement individual property rights—something not predicted by anyone when the initial institutional change was initiated (ibid.).
The problem of predicting outcomes is especially challenging when new
and unfamiliar collective- or constitutional-choice rules are selected.12
When new and unanalyzed situations are created by the process of changing parts of a rule conﬁguration, institutional analysis needs to proceed
to undertake a deeper analysis of how participants view the new rules,
how they come to understand them, how they will be monitored andenforced, and what types of individual actions and collective outcomesare produced. This is frequently a difﬁcult and complex theoretical and

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empirical task. As Mantzavinos, North, and Shariq (2004, 75) stress, the“greatest challenge for the social sciences is to explain change—or more
speciﬁcally, social, political, economic, and organizational change.”

Evaluating Outcomes
In addition to predicting outcomes, the institutional analyst may also evaluate the outcomes that are being achieved as well as the likely set of outcomes that could be achieved under alternative institutional arrangements. Participants in action situations and those observing thesesituations use evaluative criteria that are applied to the outcomes as wellas the processes of achieving outcomes. The number of potential evaluative criteria is large. In this chapter, we can only brieﬂy focus on (1) economic efﬁciency; (2) equity; (3) adaptability, resilience, and robustness;(4) accountability; and (5) conformance to general morality.
Economic Efﬁciency
Economic efﬁciency is measured by the magnitude of the change in the
ﬂow of net beneﬁts associated with an allocation or reallocation of resources. The concept of efﬁciency plays a central role in studies estimating
the beneﬁts and costs or rates of return to investments that are often used
to determine the economic feasibility or desirability of public policies.
When considering alternative institutional arrangements, therefore, it isimportant to consider how revisions in the rules affecting participants willalter behavior and, hence, the allocation of resources.
EquityAssessing equity is undertaken in two ways: (1) on the basis of the equality
between individuals’ contributions to an effort and the beneﬁts they derive and (2) on the basis of differential abilities to pay. The concept ofequity that underlies an exchange economy holds that those who beneﬁt
from a service should bear the burden of ﬁnancing that service. This iscalled ﬁscal equivalence. Perceptions of ﬁscal equivalence or a lack thereof
can affect the willingness of individuals to contribute toward the development and maintenance of public facilities.
Outcomes that tend to redistribute resources to poorer individuals are
considered to improve equity from a redistributional equity perspective.
Thus, in some cases, efﬁciency criteria would urge that scarce resources beused where they produce the greatest net beneﬁt, while equity goals would
urge an effort to beneﬁt particularly needy groups. Likewise, redistributional
criteria may differ in rankings from those of achieving ﬁscal equivalence.

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67

Adaptability, Resilience, and Robustness
Another criterion that can be applied to repeated situations is how behavior changes over time. Do individuals learn from experience within an
action situation? Do they adapt to new circumstances as they arise or
become rigid in their responses over time? The concept of resilience thatoriginally was developed in ecology has now been applied to social systems. Resilience is deﬁned as the amount of disruption needed to transform a system from stability domain (characterized by a conﬁguration ofmutually reinforcing processes and structures) to another (Holling 1973;
Gunderson and Holling 2001). The concept of robustness tends to be
used more in engineering, while resilience was initially deﬁned related toecological systems. Robustness refers to the maintenance of a system’s
performance even when it is subject to external, unpredictable disturbances (Carlson and Doyle 2002; Anderies, Janssen, and Ostrom 2004).
Accountability
When evaluating collective-choice or constitutional-choice levels, one can
ask whether ofﬁcials are accountable to citizens concerning the policies
and rules chosen. Without accountability, actors can engage in the various
opportunistic, strategic behaviors. Concern for accountability may not
conﬂict with efﬁciency and equity goals. Indeed, achieving efﬁciency requires that information about the preferences of citizens be available todecision makers, as does achieving accountability. Institutional arrangements that effectively aggregate this information assist in realizing efﬁciency at the same time that they serve to increase accountability and topromote the achievement of redistributional objectives.
Conformance to General Morality
In addition to accountability, one may wish to evaluate the level of generalmorality fostered by a particular set of institutional arrangements. Are
those who are able to cheat and go undetected able to obtain very highpayoffs? Are those who keep promises more likely to be rewarded andadvanced in their careers? How do those who repeatedly interact within
a set of institutional arrangements learn to relate to one another over thelong term? Are the procedures fair? (Frey, Benz, and Stutzer 2004)
The Need for Trade-offs
Trade-offs are often necessary in using performance criteria as a basis
for selecting from alternative institutional arrangements. Some criteria
are more important when evaluating patterns of actions and outcomes at

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a collective-choice or constitutional-choice level than at an operational
level. The trade-off issue arises most explicitly in considering alternative methods of funding public projects at a collective-choice level. Evaluating how institutional arrangements compare across overall criteria isquite a challenge. Analytical examination of the likely trade-offs between
intermediate costs is valuable in attempting to understand comparative
institutional performance (see E. Ostrom, Schroeder, and Wynne 1993,
chap. 5).
In this chapter, we have focused ﬁrst on a core analytical unit of the IAD
framework—the action situation. This is where the institutional analyst
focuses on explaining results that affect the daily lives of participants aswell as the resources affected by participants. Fortunately, the same components—participants, positions, actions linked to outcomes, control, information, and costs and beneﬁts—characterize collective-choice as wellas constitutional-choice arenas.
Given the importance of this theoretical concept, we will devote one
more chapter to illustrate the working parts and outcomes of differently
structured action situations. Chapter 3 moves into the experimental lab,
where the researcher must self-consciously create an action situation designed to enable the testing of theoretical predictions. Chapter 3 will help
the reader really understand the working components of this core unit ofanalysis.
Chapter 4 will then be devoted to the other component of an action
arena—the participants. The reader will be well aware by the end of chapter 3 that the primary model of individual behavior used extensively ingame theory, economics, and institutional analysis yields predictions thatare not supported in many experiments discussed in chapter 3 and theliterature cited therein. Thus, we face a substantial challenge in animating
institutional analyses.
Chapters 5 through 9 then turn to the question of how we can analyze
the rules used as tools to change the structure of action situations. Unfortunately, the language used by social scientists to discuss the rules, norms,
and strategies used by participants in situations is extremely confusing.
Thus, considerable effort has to be expended in chapters 5, 6, and 7 todevelop a consistent, theoretical language to deﬁne and then use theseterms in a coherent and cumulative manner. Once this is accomplished,
we will illustrate the usefulness of the concepts that we have carefully
deﬁned with extensive examples from empirical research.

ThreeStudying Action Situations in the Lab

READERS OF PAST descriptions of the working components of the Institutional Analysis and Development (IAD) framework have frequently urged
me to provide some examples of an action situation to help them makethis abstract concept more meaningful. Studying action situations in anexperimental laboratory turns out to be an excellent way to understandhow the components of an action situation and changes in these components, even small changes, can make a difference in behavior and outcomes. Experiments provide very good examples of how action situations
work. In the laboratory, the researcher carefully establishes the speciﬁccomponents of an action situation to be studied.
While it is never possible to establish a perfect experiment, extensive
methodological work has been undertaken since the pathbreaking workof Vernon Smith (1982). Smith challenged social scientists to exhibit greatcare in the design of experiments by having the payoffs closely tied to the
incentives of the decision environment. He further urged researchers toensure that the assumed structure was clear to the subjects by pretesting
instructions extensively. Smith helped to establish a tradition of paying
subjects well and closely related to the theoretical payoff structure being
investigated, instead of having the subjects participate in experiments aspart of required entry-level courses. Further, researchers have now developed a real commitment to share experimental instructions and their datawith one another and to encourage replications in order to ensure that
there was nothing “unique” about one implementation of an experiment.
Still further, experimental studies are now being used to “test bed” competing strategies for implementing new public policies (see Plott and Porter 1996).
In this chapter, I will provide a brief overview of two action situations
that have been studied extensively in experimental laboratories in many
countries. The ﬁrst is the Trust Game, similar to the Snatch Game discussed in chapter 2. The second is a Commons Dilemma, which we will
discuss again in later chapters. I do so for three reasons: (1) to allow thereader who is not familiar with the concept of an action situation or agame to see how this concept has been operationalized; (2) to illustrate
how small changes in the structure of an action situation can lead to big
differences in outcomes; and (3) to illustrate how the ﬁndings from experi-

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ments challenge the presumption that all participants use the same internal form of rationality to make decisions in all settings. In light of theseﬁndings, in chapter 4 we will examine the puzzle facing institutional analysts of how to model individual behavior.
Both of the experiments discussed in this chapter are social dilemmas.
In all social dilemmas, individuals face short-term incentives that, if followed, would lead them all to be worse off than feasibly attainable outcomes. The ﬁrst action situation to be discussed is the Trust Game. Subjects in a laboratory experiment are assigned cash or tokens that havevarying values to be converted into cash immediately after an experimentis concluded. The Trust Game has been studied extensively (E. Ostrom
and Walker 2003). Behavior in the laboratory is not fully consistent withwhat is predicted by noncooperative game theory when monetary payoffs
are assumed to be monotonically related to the internal utility values thata subject assigns to outcomes.
The second type of experiment is of appropriation (harvesting) from acommon-pool resource—a Commons Dilemma. Many of the experiments
described in the second section of this chapter have been conducted bycolleagues at Indiana University as well as being replicated by scholars atother universities. Common-pool resource situations will help the reader
think about more complex N-person situations that are closely related to
speciﬁc ﬁeld settings including inshore ﬁsheries, irrigation systems, forest
resources, and groundwater basins. While behavior in the laboratory is
broadly consistent with noncooperative, game-theoretic predictions in abaseline common-pool resource experiment, interactions and outcomesare not consistent with this prediction when face-to-face communicationis allowed or when subjects can use costly punishment against one another. There is, thus, a lot to learn about operationalizing action situations
from experimental work. Given the multiplicity of ﬁndings that are notconsistent with conventional game-theoretical analysis, we will be setting
the stage for an extended discussion of “animating institutional analysis”
in chapter 4.

The Trust Game in the Experimental Laboratory
In an elegantly simple, two-person game, Berg, Dickhaut, and McCabe(1995) designed a game that enables researchers to focus in clearly on
factors that affect the likelihood that an individual will take a costly action because she places trust in a second individual. As we discussed inchapter 2, unless the ﬁrst household in the Snatch Game trusted that thesecond household would exchange goods rather than snatch Household
1’s goods, no positive transaction would occur. There are many such situ-

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71

Figure 3.1 The Trust Game.

ations that individuals face in all aspects of life. The most simple baseline
game is composed of the following elements (see ﬁgure 3.1):
1. Participants. Two subjects play the game.
2. Positions. The two positions are the Investor and the Trustee.
3. Actions. The Investor is given X at the beginning of the game. The Investor then decides to keep all of X, to give T to the Trustee and to keep X − T, or
to give all of it (T = X) to the Trustee. The Trustee then decides how much of
the funds—Y—to return to the Investor.
4. Outcomes. The outcomes are the size of the funds allocated to the twoplayers in light of the decisions they have made.
5. Action-outcome linkages. The amount invested in the Trustee earns a rateof return (supplied by the experimenter) of 1 + r.
6. Information. Both players are told the full tree of possibilities and that
their own identity will remain anonymous to the other player and to the experimenter (double-blind).
7. Potential payoffs. The payoffs are affected by the rate of return (1 + r ). r
has frequently been operationalized as 2, so that the amount that the Investor
sends to the Trustee is tripled. The payoff to the Investor is (X − T ) + Y. The
payoff for the Trustee is (1 + r ) T − Y, assuming the Investor sent something inthe ﬁrst place or zero otherwise.1 T can then be used as a measure of trust and(1 + r ) T − Y for T > 0 as a measure of trustworthiness. The original payoff ratesof Berg and colleagues were X = $10.00, r = 2. Thus, if the Investor allocated allof the funds to the Trustee, the Trustee had $30.00 to keep or share. Beyond
the objective pecuniary payoffs, it is also possible that the individual assigns autility to the objective, external payoffs that increases or decreases the value ofthe objective payoff to the individual or assigns utility to the objective values
that the other player receives.

A self-interested Trustee wanting to maximize pecuniary returns would
return zero to the Investor. If the Investor expects this, no funds will be

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invested in the ﬁrst place. Similar to the predicted outcome for the Snatch
Game, the predicted outcome using noncooperative game theory and assuming that individuals attempt to maximize monetary returns is zero
invested. The baseline game is barren of many of the social factors thatare thought to affect trust. The players are strangers and do not even see
one another. There is no way that they can establish a link to one anotherthrough promise-giving or the like. The Trust Game is similar in structure
to a sequential Prisoner’s Dilemma. The baseline game represents a situation requiring trust in about as pristine a form as one can imagine.
Using a one-shot decision setting with double-blind experimental procedures to ensure complete anonymity, Berg, Dickhaut, and McCabe
found that 30 of 32 subjects in the position of Investor sent money to theTrustee ($5.16 on average). Of the 30 subjects in the position of Trustee,
18 returned more than $1.00 ($4.66 on average) and one-third of themsent more funds to the Investor than they received. On average, those
Investors who sent $5.00 or more received an average return in excess of
the amount they sent. It was those Investors sending less than $5.00 who
received a negative net-average return. In other words, on average, those
Investors who trusted their counterpart the most were the ones who left
the game with more wealth than those who were less trusting.
This experimental design of the Trust Game is simple, but captures theessence of trusting and reciprocal behavior so effectively that it has been
replicated and extended in many diverse settings and countries (see Cook
and Cooper 2003). The initial Berg, Dickhaut, and McCabe experimentcan be thought of as examining the level of trust and reciprocity among
relatively equal players. All of the subjects were students at the Universityof Minnesota. Which subjects were chosen as the Investor or the Trustee
was determined randomly.
Ernst Fehr and colleagues at the University of Zurich have undertakena related set of experiments that shifted the framing and structure of thesituation from one involving participants who related to one another ina horizontal manner to one that self-consciously involved hierarchy involving either “employers or buyers” and “workers or sellers” in a vertical relationship (see Fehr, Kirchsteiger, and Riedl 1993; Fehr and Falk
1999; Fehr and Gächter 1998). In one of the Swiss experiments (Fehr,
Kirchsteiger, and Riedl 1993), the experimenters randomly assigned subjects to two groups placed in separate rooms without capacity to see ortalk directly with one another (as did Berg and colleagues). For all roundsof the experiment, one set of subjects was told they were “Workers” andwould be paid a wage if employed, and another set of subjects was told
they were “Employers.” There were always more Workers than Employers so that not all of them would be “hired.” All were informed about the
speciﬁc wages that could be offered and the range of effort that a Worker

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73

could expend at a monetary cost. The Employer’s income depended on
the effort expended by their Workers.
Employers made the ﬁrst move in each of the twelve periods of an experimental session by making contract offers of wage rates. The size ofthe payoff to the Employer depended on the effort that a Worker expended once he was hired with a speciﬁc wage. Since there were more
Workers than Employers, however, several Workers would remain unemployed and receive only a set fee for showing up at the experiment. Thus
the Workers were at an initial disadvantage and might expect to receive
a minimal wage offer.
In the second stage, the experimental Workers who had accepted awage offer in a period made a decision regarding the level of effort theywould return to their Employers, ranging from a minimum of 0.1 to a
maximum of 1.0 with a reduction in their payoffs dependent on the levelof effort chosen. Any level of effort above 0.1 would be a “return gift”
back to the Employer, at a cost to the Employer that was much less thanthe beneﬁt that the Employer would receive. For a wage of 60, for example, increasing effort by 0.1 would increase the cost to the Worker by 2
units at most, but would increase the return to the Employer by 6 units.
The anonymity of all subjects was assured by the experimenters.
The game-theoretical prediction for this experiment is similar to thatfor the Berg, Dickhaut, and McCabe setting. The Employer should offera minimum wage since the person in this position can expect that theWorker will return minimal effort (Fehr, Kirchsteiger, and Riedl 1993,
443). On the other hand, subjects in both positions lose potential income
if they follow the theoretically “rational” thing to do (as is the case forInvestors and Trustees in the Berg, Dickhaut, and McCabe experiment).
Fehr and colleagues repeatedly found, however, that subjects in the position of Employer offered substantially more than the theoretical minimum
expected and that Workers reciprocated by allocating higher than minimal costly effort. In this and a replication in a more challenging experiment, where a double auction determined wages (Fehr and Falk 1999),
average effort was a positive function of the level of wage offered and far
above the predicted level.
Kenneth Koford (2003) has replicated both the original Berg, Dickhaut,and McCabe horizontal experiments as well as some of the vertical (Employer-Worker) experiments run by Fehr and colleagues, with students
recruited from Soﬁa University and Varna Economics University, both in
Bulgaria. This enabled Koford to directly examine the impact of the relative horizontal or vertical standing of the two positions in the experiment.
In the horizontal experiment, 44 out of 47 of the subjects in the positionof Investor sent some money, which was quite similar to the 30 out of 32

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Investors in Berg, Dickhaut, and McCabe’s experiment. The distributionof amounts sent was also quite similar.
In the vertical experiment, Koford found a different pattern to that ofFehr and Falk (1999). The effort levels of the subjects in the position ofWorker were much lower: “16 of the 41 effort levels were the minimum,.1. The mean effort is just .368” (Koford 2003, 17). Further, the wage
level was not associated with the level of effort. Thus, in the horizontal
design, Koford found the same level of trust and reciprocity as the U.S.
and Swiss experiments, but not in the vertical designs.
Koford explains the intriguing difference in ﬁndings by drawing on thebusiness and cultural traditions and history in Bulgaria and the Balkancountries more generally. Given the long history of exploitative Ottoman
and then communist rulers, government provided no basic security ortrust for a people. Folks had to trust others in similar circumstances.
Those in business in Bulgaria report extensive fraud and difﬁculties withtrusting others, however (see Koford and Miller 1995). Students are more
likely to engage in a general trust relationship with one another.
It seems that under speciﬁc conditions Bulgarian students trust as much as
American students, and very likely more. This may be due to the focus upon“students,” who feel that they should share a principle of solidarity in Bulgaria.
Then, when they are divided into Workers and Employers, this solidarity disappears. Discussion with students suggested that if the horizontal experimentwere run with the other group being signiﬁcantly different—say, ordinary
Workers, or a different ethnic group . . . the level of trust would be considerably
lower. (Koford 2003, 21)

The horizontal Trust Game has also been replicated by Buchan, Croson, and Johnson (1999) in another cross-country design intended to assess the impact of the socioeconomic setting of participants on behavior.
They conducted the Trust Game with 188 subjects from China, Japan,
Korea, and the United States. They ﬁnd no pure country effects in termsof the amount sent by subjects in the role of the Investor (on average, 67
percent of the endowment) or in the amount returned (on average, 31
percent of the amount received).
In addition to examining country effects, the researchers examined several other contextual variables including: the cultural beliefs of the participant (based on questionnaire data where questions relate to an individual’s attitude toward group versus individual outcomes), social distance(manipulated experimentally), and communication (manipulated experimentally). Subjects who showed a greater orientation toward group outcomes sent more funds as both an Investor and as Trustee. The opportunity to communicate information about one’s self and learn something
about the other person with whom a subject was paired also had a positiveS T U D Y I N G A C T I O N S I T U AT I O N S I N T H E L A B

75

effect on amounts sent and returned. Buchan and colleagues found that“trusters prosper.” In other words, “subjects who sent above averageamounts to their partners, took home greater wealth than did subjects
who sent only average amounts or less” (Buchan, Croson, and Johnson
1999, 22).2
In a follow-up study to examine the impact of enabling the Trustee tobuild a reputation, Dickhaut et al. (1997) added a publicly announced
second round to the basic structure of the base experiment. Both participants played in both rounds. According to standard, noncooperative
game theory using backward induction, this second round should makeno difference to the behavior of individuals in the ﬁrst round. Dickaut
and colleagues wanted to ascertain if Trustees would act even more trustworthy than those who had participated in the ﬁrst study in order toassure Investors that they could be trusted.
In the ﬁrst round, they found that 10 of the 23 Investors sent the maximum sum ($10.00) to the Trustee and that none of them sent zero. Trustees exhibited higher levels of reciprocity in the ﬁrst round than had been
exhibited in the baseline study. Twenty of them returned more than theircounterpart had sent to them, leading to a positive-sum outcome for all
involved. The ﬁndings for the second and ﬁnal round, however, followeda different pattern. Nineteen out of 20 of the Investors who had received
positive returns in round one made a positive investment again in round
two, and all three of the Investors who had received a negative return inthe ﬁrst round sent zero to the Trustee in the second round. The big difference was that only 7 of the 19 Investors received a positive net return inround two. The reciprocity that had been exhibited in round one wassubstantially reduced in the second, and known-to-be ﬁnal, round.
Güth, Ockenfels, and Wendel (1997) simpliﬁed the experimental conditions so that Investors had similar dichotomous options as Household 1
in the Snatch Game—to trust or not trust the Trustee. The Trustee could
then either “exploit” the Investor by choosing an option that paid theTrustee a high payoff and gave the Investor very little, or the Trustee could
reciprocate the trust by choosing an option that led to lower but equal
payoffs for both. Güth and colleagues found that the majority of the subjects in the ﬁrst position (21 out of 28) extended trust, but that theirtrusting action was not reciprocated by a majority of those in the second
position. On the other hand, in a replication of this experimental structure, Kirchler, Fehr, and Evans (1996) had results very close to those ofBerg, Dickhaut, and McCabe with high levels of trust and reciprocity
exhibited (see also Dickhaut et al. 1997).3 Güth, Ockenfels, and Wendel(1997) also experimented with a further modiﬁcation of the Trust Game.
In this experiment, they had subjects experience the game once and receive
the payoffs from that round. Then they asked the subjects to bid for the

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role of Investor and Trustee. Very low levels of trust were extended whenplayed by those who bid for the Investor position (and low levels of reciprocity were extended by those who were trusted).
In an experiment that explores the impact of changes in the informationexchanged as well as threatened sanctions on outcomes of a Trust Game,
Fehr and Rockenbach (2003) obtained some fascinating results (see also
Fehr, Gächter, and Kirchsteiger 1996 and Fehr and Gächter forthcoming
for related experiments). In one series of experiments—which they callthe “trust condition”—both the Investor and the Trustee receive tenmoney units (MUs). As in the above games, the Investor may either sendzero or some number between 1 and 10 MUs to the Trustee. Whateveramount is sent is tripled. At the time of sending funds, the Investor has
to indicate a “desired back-transfer” that can range from zero to the fullamount of the tripled transfer. The information about the amount sent
and the desired back-transfer is given to the Trustee, who then decides
how much, if any, to send back. The only difference between this experiment and the Berg, Dickhaut, and McCabe experiment is the information
given to the Trustee about the amount the investor expects back.
In a second condition—which they call an “incentive condition”—theInvestor is given a choice between indicating whether he or she has chosen
a ﬁning option that will deduct four MUs from the Trustee if the Investor
does not receive at least as much as the speciﬁed desired level. The Investor
can overtly waive this opportunity to have these funds deducted if the
returned level is not as desired. If chosen by the Investor, the deduction issubtracted from the Trustee’s payoff, and the Investor receives whateverfunds the Trustee decides to send back without contributing to the deduction. Fehr and Rockenbach explored this incentive option in order to examine the proposition that a threatened sanction would increase the levelof funds returned by the Trustee.
They found that Trustees sent substantial returns in all conditions. Like
Berg and colleagues, they found that the amount of funds returned werepositively associated with the amount of money invested in the ﬁrst place.
Nineteen of the 24 Trustees in the trust condition (79 percent) paid backmore than zero, and 19 of the 45 subjects in the Trustee position in the
incentive condition (42 percent) paid back more than zero (Fehr andRockenbach 2003, 138). As shown in ﬁgure 3.2, the highest return occurred when the Investor refrained from imposing a ﬁne in the incentive
condition. The lowest level of return occurred when the Investor indicateda high desired back-transfer and a ﬁne for not meeting this level. Fehr andRockenbach explained both ﬁndings as the result of what they refer to as“strong reciprocity.” “First, refraining from the threat of ﬁning, although
the threat is available, could itself be perceived as a fair act, which induces

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77

Figure 3.2 Trust and reciprocity under three experimental conditions. Source:
Fehr and Rockenbach 2003, 138, and data provided by Ernst Fehr.

the trustees to increase their cooperation. Second, attempts to use thesanction to enforce an unfair distribution of income may be perceived as
hostile acts, inducing the trustees to reduce cooperation” (2003, 139–
40). Thus, the threat of a ﬁne, which is frequently thought to be a major
technique for solving problems related to trust, backﬁred. Instead of enhancing the level of reciprocity shown by Trustees, an externally established ﬁne that is endogenously used as a threat by the Investor reduces
reciprocity rather than increasing it. This is consistent with the theoreticaland experimental work of Bruno Frey (1994, 1997a) on external sanctions crowding out reciprocity. We will return to the question of how
sanctions are arrived at, and their impact, when we discuss sanctions
again in the context of the common-pool resource experiments as well as
in later sections of this book.
The ﬁndings from these rich experimental studies of the Trust Gameare consistent with empirical studies of trust in other settings (Gambetta
1988; Rothstein 2005; Delhey and Newton 2003). They also provide important insights for an institutional analyst. First of all, it is relatively
easy to see how Berg, Dickhaut, and McCabe initially created an action
situation in an experimental lab. We can also observe how small changes

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in these working parts—including: (1) the relative standing of the participants, (2) the number of rounds, (3) the range of actions authorized, (4)the beneﬁts and costs of actions and outcomes, (5) the diverse ways ofchoosing subjects to hold a position (e.g., random assignment or an auction), or (6) the diverse sanctioning mechanisms—affected interactions
and outcomes. Hopefully, the concept of an action situation is now a little
clearer for the reader.
Many readers, however, will be surprised at the high levels of trust
exhibited in the ﬁrst experiment and its replications in multiple cultures.
The game-theoretic prediction for this game is very stark and clear. Because the Trustee is expected by a rational player not to return anything
to an Investor, the Investor is not expected to trust an unknown stranger
by sending any funds. Like all social dilemmas, this prediction leaves all
participants worse off than they could have achieved. The multiple
replications in which a substantial level of trust is exhibited raises serious
questions about the universal validity of relying entirely on the rational
choice model of the individual (a topic to be explored in some depth inchapter 4).4
Still further questions are raised about the capability of the classic
model of self-interested rationality to explain the adverse impact of externally imposed threats of sanctions on the willingness of Trustees to reciprocate Trust. When faced with a threat of sanction, if the Trustee did not
return the level of funds speciﬁed by the Investor, many Trustees did not
return the funds speciﬁed—thus, levels of reciprocity were reduced by theexternally imposed threat of a sanction. This empirical result challenges
not only the model of the individual that is widely used but also the standard recommendation that external sanctions are the best way to solve
social dilemmas that lie at the heart of many types of basic economic,
social, and political problems. We will discuss the core difference in outcomes achieved between externally imposed sanctions and sanctions
agreed upon by those involved in the next section of this chapter.

A Commons Dilemma in the Experimental Laboratory
The Trust Game characterizes a wide diversity of settings where one person has to trust someone else and that person has to reciprocate in orderfor both to be better off. This game seems too simple for some scholars.
“All one needs to do is create a form of contract law,” they say, “in orderfor the Investor to bind the Trustee to a contract so as to return the investment.” Then they argue, you would not have to depend on reciprocity
but rather on the legal system. One of the expected beneﬁts of creatinga relevant legal structure is to use sanctions against those who are not

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79trustworthy. As we have seen, however, from the experiments using sanctions, the way that legal structures are implemented can make a big difference. Externally imposed sanctions can lead to resentment rather thanguilt and adversely affect the willingness to reciprocate trust.
Creating contract law or property rights is more difﬁcult than it appearson the surface (as we will discuss in chapter 8, and as many advisers
have learned in Eastern Europe and the former Soviet Union—see, forexample, de Soto 2000). Many contracts in Western economies are undertaken based on trust and reciprocity (Arrow 1974) rather than drawing
on the legal system. Further, the Fehr and Rockenbach experiments demonstrate that a sanctioning system imposed without agreement of the participants may reduce reciprocity rather than increase it. Hopefully, thereader can by now appreciate both how to operationalize an action situation as well as learning about the substantial ﬁndings of experimental
research on trust. Further theoretical developments on trust have now
been achieved as a result of this extensive research (see Bacharach andGambetta 2001; Ahn et al. 2003; McCabe and Smith 2003; E. Ostrom
and Walker 2003).
We will now look at a second illustration of an action situation in thelaboratory that is inherently far more complex than the Trust Game—a common-pool resource situation. When the users of a common-poolresource are tempted to overuse the resource, the structure of the situation
is a social dilemma. Social dilemmas are pervasive in social life, andproposed solutions to these dilemmas have occupied all great politicalphilosophers including Aristotle, Hobbes, and Hume. There are manydifferently structured social dilemmas, but they all are characterized
by a situation where everyone is tempted to take one action but all will
be better off if all (or most of them) take another action. Studying how
subjects behave in this type of social dilemma helps us understand moregeneral questions of relevance across the social sciences than simply thestudy of natural resources. We will also return to discuss the commonpool resource setting in chapters 8 and 9 to examine how actual users ofthese kinds of resources create their own rules to deal with the problemsof overuse.
The Deﬁnition of a Common-Pool Resource
A common-pool resource, such as a lake, an ocean, an irrigation system,
a ﬁshing ground, a forest, the Internet, or the stratosphere, is a natural or
man-made resource from which it is difﬁcult to exclude or limit users once
the resource is provided by nature or produced by humans (E. Ostrom,
Gardner, and Walker 1994). One person’s consumption of resource units,
such as water, ﬁsh, or trees, removes those units from what is available

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to others. Thus, the trees or ﬁsh harvested by one user are no longer available for others. As discussed in chapter 1, the difﬁculty of excluding beneﬁciaries is a characteristic that common-pool resources share with public
goods. Further, the subtractability of resource units from a common-poolresource is an attribute shared with private goods. In the rest of this chapter, I will focus primarily on renewable natural resources as exemplars ofcommon-pool resources, but the theoretical arguments are relevant to
man-made common-pool resources, such as the Internet, as well.
When the resource units produced by a common-pool resource have ahigh value and institutional rules do not restrict the way resource units
are appropriated (an open-access situation), individuals face strong incentives to appropriate more and more resource units leading eventually tocongestion, overuse, and even the destruction of the resource itself. Because of the difﬁculty of excluding beneﬁciaries, the free-rider problem isa potential threat to efforts to reduce appropriation and improve the longterm outcomes achieved from the use of a common-pool resource. If someindividuals reduce their appropriation levels, the beneﬁts they generate
are shared with others whether the others also cut back on their appropriation or not. Some individuals may free-ride on the costly actions of others
unless ways are found to reduce free-riding as an attractive strategy. When
free-riding is a major problem, those who would willingly reduce theirown appropriations if others did are unwilling to make a sacriﬁce for the
beneﬁt of a large number of free riders.
Consequently, one of the important problems facing the joint users ofa common-pool resource is known as the “Commons Dilemma,” given
the potential incentives in all jointly used common-pool resources for individuals to appropriate more resource units when acting independently
than they would if they could ﬁnd some way of coordinating their appropriation activities. Joint users of a common-pool resource often face manyother problems including assignment problems, technological externality
problems, provision problems, and maintenance problems (E. Ostrom,
Gardner, and Walker 1994). And, the speciﬁc character of each of these
problems differs substantially from one resource to the next. In thischapter, I focus on the problem of controlling appropriation since this iswhat most policy analysts associate with “the tragedy of the commons”(G. Hardin 1968; Dietz, Ostrom, and Stern 2003).
A Baseline Appropriation Situation
We need to start with a static, baseline situation that is as simple as
we can specify it without losing crucial aspects of the problems that
real appropriators face in the ﬁeld. This static, baseline situation is composed of:

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81

1. Participants. A set of n symmetric subjects who do not have any outside
relationships with one another.
2. Positions. No differentiation exists in the positions these subjects holdrelevant to the common-pool resource. In other words, there is only one position of appropriator.
3. Actions. Appropriators must decide how to allocate tokens assigned tothem in each time period. Basically, one can think of these appropriators as
being “endowed” with a total set of assets, e, which they are free to allocate inany proportion during each time period to two activities. In a ﬁeld setting one
can think that every day, each appropriator must decide between spending timetrying to harvest resource units from the common-pool resource or using theirtime to earn money in an outside option, such as working in a local factory. To
simplify the problem, we posit that all appropriators have the same endowment(just as we all have only twenty-four hours per day), and face the same outside
opportunity. Thus, they have to decide how much of their endowment to devote
each round to appropriation from the common-pool resource or in gaining
returns from an outside option.
4. Outcomes. The actions they take affect the amount of resource units thatcan be appropriated from the common-pool resource or returns earned fromthe outside option.
5. Action-outcome linkages. The function maps the actions of all of the appropriators given the biophysical structure of the resource itself onto outcomes.
While these functions are frequently stochastic in ﬁeld settings and affected bymany variables in addition to the actions of individuals, we will consider only
determinant functions of appropriation actions in the baseline setting. The
wage function simply multiplies the amount of time allocated to it by whateveris the standard wage. The resource function is a concave function, F, which
depends on the number of assets, xi, which is a fraction of e allocated to appropriation. Initially, the sum of all of the individuals’ actions, ∑xi, generates better
outcomes than the safe investment in wage labor. If the appropriators decideto allocate a sufﬁciently large number of their available assets, the outcome
they receive is less than their best alternative. In other words, allocating too
many assets to the common-pool resource is counterproductive (see Gordon
1954; Scott 1955).
6. Information. As an initial information condition (because of the instructions carefully given to all subjects), we assume that appropriators know the
shape of the function linking actions to outcomes and know that they are symmetric in assets and opportunities. Information about outcomes is generated
after each decision round is completed. Appropriators may not communicate
with one another. It is assumed that each appropriator will assume that all otherappropriators are rational actors and will adopt the “best response” to theirown actions. The best response function should lead all appropriators to overharvest from the resource.

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7. Potential payoffs. Payoff functions specify the value of the wage rate andthe value of the resource units obtained from the common-pool resource. Speciﬁcally, the payoff to an appropriator is given by:we
if xi = 0w (e − xi ) + (xi / ∑xi ) F (∑xi ) if xi > 0.

(1)

Basically, if appropriators put all of the assets into their outside option,
they receive a certain monetary return equal to the amount of their endowment times an unchanging rate of return (w).5 If appropriators put
some of their endowed assets into the outside option and some into thecommon-pool resource, they get part of their return from the outside option and the rest from their proportional investment in the common-poolresource times the total output of the common-pool resource as determined by function F.
Predicted Outcomes for a Commons Dilemmain the Laboratory
In a series of laboratory experiments conducted at Indiana University, we
thought it crucial to examine behavior in an appropriation situation witha nonlinear transformation (action-outcome) function and a sufﬁcientnumber of players that knowledge of outcomes did not automatically provide information about each player’s actions. In this chapter, I can only
brieﬂy discuss the results of these experiments. All procedures and speciﬁcations are thoroughly documented in E. Ostrom, Gardner, and Walker
1994 and in journal articles cited therein.
In the baseline experiments, we utilized the following equation for thetransformation function, F, measured in units of output (outcome units):
23 (∑xi ) − 25 (∑xi )2 .

(2)

Instead of asking subjects to pretend they were ﬁshing or harvesting timber, the situation was described as involving a choice between investingin either of two markets having the structure as speciﬁed above. All eightsubjects were assigned either 10 or 25 tokens as their endowment in eachround of play. Their outside opportunity was valued at $.05 per token.
They earned $.01 on each outcome unit they received from investing tokens in the common-pool resource. Subjects were informed that theywould participate in an experiment that would last no more than twohours. The number of rounds in each experiment varied between twenty
and thirty rounds. In addition to being told the payoff function speciﬁ-

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83

cally, subjects were provided with look-up tables that eased their task ofdetermining outcomes depending on their own and others’ decisions.
With these speciﬁcations, the predicted outcome for a ﬁnitely repeatedgame where subjects are not discounting the future and each subject isassumed to be maximizing monetary returns is for each subject to invest
8 tokens in the common-pool resource for a total of 64 tokens. By design,
the prediction is the same for both endowment levels. At this level ofinvestment, they would each earn $.66 per round in the 10-token experiments and $.70 per round in the 25-token experiments (players were paid
one-half of their computer returns in the 25-token experiments to keep
the payoffs roughly similar). The players could, however, earn considerably more if the total number of tokens invested was 36 tokens (rather
than 64 tokens) in the common-pool resource. This optimal level of investment would earn each subject $.91 per round in the 10-token experiment and $.83 per round in the 25-token experiment. The baseline experiment is an example of a commons dilemma in which the game-theoretic,
predicted outcome involves substantial overuse of a common-pool resource, while a much better outcome could be reached if subjects were tolower their joint use.
Behavior in a Sparse Experimental N-Person, Repeated
Commons Dilemma
Subjects interacting in baseline experiments substantially overinvested as
predicted. Subjects in the 10-token experiments achieved, on average, 37
percent of the maximum earnings from the common-pool experimentavailable to them, while subjects in the 25-token experiments received −3
percent (E. Ostrom, Gardner, and Walker 1994, 116). At the individuallevel, however, subjects rarely invested 8 tokens, which is the predictedlevel of investment at equilibrium. Instead, all experiments generated an
unpredicted and strong pulsing pattern in which individuals appear toincrease their investments in the common-pool resource until there is astrong reduction in yield, at which time they tend to reduce their investments leading to an increase in yields. The pattern is repeated over time.
At an aggregate level, behavior approximates the predicted Nash equilibrium in the 10-token experiments. Outcomes are far lower than predictedin the early rounds of the 25-token experiment and only begin to approach the predicted level in later rounds. No game-theoretical explanation yet exists for the pulsing pattern or the substantial difference between
the 10-token and the 25-token experiments.
In response to postexperiment questions, subjects explained that they
were using several heuristics. One of the heuristics they used was to invest
more in the common-pool resource whenever the rate of return on the

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previous round was above $.05 (what they could earn in their next bestalternative) and less if the return was below $.05. In the 10-token experiment, some players invested all 10 of their tokens whenever the rate of
return in the prior round was above $.05. With such heuristic strategies,
equilibrium is never reached at the individual level. Each player is revising
his or her response to the results obtained in the most recent round, creating considerable turbulence in the outcomes jointly reached.
Replication in Agent-based Models
An extremely interesting follow-up study was undertaken by Peter Deadman (1997) in which artiﬁcial agents were programmed to use a varietyof heuristics similar to those used by the human subjects in these commonpool resource (CPR) experiments and to interact in a simulated environment that exactly replicated the baseline experiments. Deadman foundthat the speciﬁc results obtained in any series of runs depended on theparticular heuristic (or mix of heuristics) programmed, but the artiﬁcial
agents did consistently produce the same kind of pulsing returns and theconsistent difference between 10-token and 25-token environments was
also observed. Deadman describes his results:
As in CPR experiments, the group performance for the simulation follows an
oscillating pattern in which high performance leads to over investment in the
CPR and the resultant drop in performance causes a reduction in group-wide
investment in the CPR. . . . Still more interesting is the observation that thesimulations perform similarly to subjects in laboratory experiments in terms of
average performance over time. At the ten token endowment, the simulations
perform near the Nash equilibria over time. At the 25 token endowment, thesimulations perform near zero percent of optimum over time. (Deadman 1997,
175–76)

Jager and Janssen (2003) also developed a multiagent model using theconsumat framework derived from social psychology (Jager, Janssen, andViek 2001). They thought that they could replicate the data from thebaseline commons dilemma experiments described above with an assumption that individual subjects differed in regard to their Social Value Orientations (SVOs). In their ﬁrst series of simulations, they were indeed able,
as Deadman had done independently, to explain the aggregated patternof appropriation behavior. Jager and Janssen were not as successful inreplicating individual appropriation decisions (from the experimental
data) as they had been in simulating aggregate outcomes. Jager and Janssen (2003) suggested that the cognitive processes that subjects use appear
to be important in behavior. Those with low aspiration levels may lock

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85

into a habitual response too soon. Further, those agents “conforming to
the Homo psychologius have a better performance than the Homo economicus in approximating the empirical data” (98).

Structural Changes in the Laboratory
In addition to the baseline experiments, we and many other researchers
have explored how changes in the structure of the action situation affectoutcomes. These changes are operationalized in the set of instructions
given to subjects and in the procedures adopted within the experiment.
The ﬁrst structural change is related to the information component ofan action situation. Instead of forbidding communication among subjects,
as in the baseline experiments, subjects are now authorized to communicate with one another on a face-to-face basis in a group setting before
returning to their own enclosed terminals to make their private decisions.
This introduction of an opportunity for “cheap talk” in a social dilemma,
where agreements are not enforced by an external authority, is viewed
within the context of noncooperative game theory as irrelevant. The sameoutcome is predicted as in the baseline experiment.
In a second set of experiments, colleagues also explored whether cheap
talk would enable individuals who acquired heterogeneous assets toachieve better outcomes than predicted by theory. In a third series of experiments, the payoff component was changed to allow subjects to sanction one another at a cost to themselves. Since using this option produces
a beneﬁt for all at a cost to the individual, the game-theoretic predictionis that no one will choose the costly sanctioning option. Fourth, the authority rule was changed to allow subjects to covenant with one anotherto determine their investment levels and to adopt a sanctioning system
if they wished. Again, the predicted outcome is the same. In all four ofthese appropriation experiments, however, subjects demonstrate their
willingness and ability to search out and adopt better outcomes than thosepredicted.
Face-to-Face Communication
In the repeated communication experiments, subjects ﬁrst made tenrounds of decisions in the context of the baseline appropriation situation
described above. After the tenth round, subjects listened to an announcement that told them they would have an open group discussion before
each of the next rounds of the experiment. The subjects left their terminals

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and sat in a circle facing one another. After each discussion period, they
returned to their terminals to enter their anonymous decisions. Subjects
used face-to-face communication to discuss together what strategy would
gain them the best outcomes and to agree on what everyone should invest
in the subsequent rounds. After each decision round, they learned what
their aggregate investments had been, but not the decisions of individual
players. Thus, they learned whether total investments were greater than
the total investments they had earlier agreed upon. While in many rounds,
subjects did exactly as they had promised one another they would do,some defections did occur. If promises were not kept, subjects used thisinformation about the aggregate investment levels to castigate the unknown participant who had not kept to the agreement.
This opportunity for repeated face-to-face communication was extremely successful in increasing joint returns. In the 10-token experiments, subjects obtained close to 100 percent of the maximum available
returns. There were only 19 instances out of 368 total opportunities (5percent) where a subject invested more in the common-pool resource
than agreed upon (E. Ostrom, Gardner, and Walker 1994, 154). In the
25-token experiments, subjects also improved their overall performance.
The temptation to defect, however, was greater in the 25-token experiments. Subjects in the 25-token baseline experiments had received total
returns that were slightly below zero, while in the communication experiments, they obtained on average 62 percent of the maximum available
returns (with considerable variation across experiments). The defection
rate was 13 percent. Our conclusion in completing an analysis of theseexperiments was:Communication discussions went well beyond discovering what investments
would generate maximum yields. A striking aspect of the discussion rounds
was how rapidly subjects, who had not had an opportunity to establish a welldeﬁned community with strong internal norms, were able to devise their own
agreements and verbal punishments for those who broke those agreements. . . .
In many cases, statements like “some scumbucket is investing more than weagreed upon” were a sufﬁcient reproach to change defectors’ behavior. (E. Ostrom, Gardner, and Walker 1994, 160)

That subjects had internalized norms regarding the importance of keeping
promises is evidenced by several of their behaviors. Simply promising to
cut back on their investments in the common-pool resource led most subjects to change their investment pattern. Secondly, subjects were indignant
about evidence of investment levels higher than that promised and expressed their anger openly. Third, those who broke their promise tended
to revert to the promised level after hearing the verbal tongue-lashing of

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87their colleagues (see A. Simon and Gorgura 2003 for an intensive analysisof the recorded transcripts). The ﬁndings from these initial communication experiments are consistent with a large number of studies of the impact of face-to-face communication on the capacity of subjects to solve avariety of social dilemma problems (see Bohnet and Frey 1998; E. Ostrom
and Walker 1991; Sally 1995 and literature cited therein).
Communication among Heterogeneous Players
Steven Hackett, Edella Schlager, and James Walker (1994) conducted aseries of commons dilemma experiments where they explored whether
communication could ameliorate the problems identiﬁed in ﬁeld and experimental settings related to heterogeneity among appropriators (R. Hardin 1982; R. Johnson and Libecap 1982; Libecap and Wiggins 1984; Isaac
and Walker 1988a, 1988b; Wiggins and Libecap 1987; Hackett 1992).
The task of agreeing to and sustaining agreements for efﬁcient appropriation from a common-pool resource is more difﬁcult for heterogeneous
appropriators because of the distributional conﬂict associated with alternative sharing rules. In heterogeneous settings, all appropriators may bemade better off by adopting a new rule; some will beneﬁt more than others, depending upon the sharing rule chosen. Consequently, appropriators
may fail to cooperate on the adoption of a sharing rule because they cannot agree upon what would constitute a fair distribution of beneﬁts produced by cooperating.
In order to address appropriator heterogeneity, the Hackett, Schlager,
and Walker experimental design allows for two levels of input endowments. One subset of appropriators has large endowments of tokens (24);the other appropriators have small token endowments (8). Group allocations to invest in the commons at the asymmetric Nash equilibrium are
greater than optimal, but not all potential returns dissipated.
In order for communication to enhance joint payoffs to a heterogeneous set of subjects, they must agree on (1) the target level of group
allocations to the common-pool resource, and (2) a rule for allocating thetarget input allocation across appropriators, and they must create (3) thenecessary “social capital” to attenuate cheating, since agreements are
nonbinding. The existence of heterogeneity in endowments and in historic
allocation levels has no effect on the ﬁrst problem, but is likely to elicit
disagreement over the second problem. Disagreements then impair thebuilding of social capital—the third problem.
Subjects knew with certainty the total number of decision makers in
the group, their own token endowment and that of the others, the total
number of tokens in the group, the transformation function, and the num-

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ber of decision rounds in the current treatment condition. After eachround, subjects were shown a display that recorded their payoffs in eachmarket for that round, total group token investments, and a total of theircumulative proﬁts for the experiment, but not the allocations made byspeciﬁc other players.
Subjects participated in two (consecutive) ten-round sequences of theasymmetric game. In the ﬁrst ten rounds, subjects were not allowed tocommunicate, but face-to-face communication was allowed during
the second set of ten interactions. Prior to each ten-round treatment sequence, four subjects were assigned the “large” token endowment, while
the other four subjects were assigned the “small” endowment. Two different mechanisms were used for assigning these endowments: random andauction (based on Güth 1988). In the ﬁrst method, large endowments
were assigned randomly prior to the ten decision rounds without communication, and again prior to the ten decision rounds with communication.
A multiple-unit ascending price auction was used as the alternative mechanism for assigning endowment positions because of its demand-revealing
characteristics. In particular, the price paid for the large endowment position should theoretically correspond with the maximum value placed on
this position by the subject with the fourth highest valuation.
The opportunity to communicate led to a noticeable change in thepattern of allocations. With the allocation rules agreed upon in communication rounds, subjects concentrated near the optimal allocation of 56
tokens in total. In the random-assignment and communication condition,individual allocations of 8 tokens represented the modal response (67percent). In the auction and communication condition, however, Hackett,
Schlager, and Walker (1994) observed a spread of allocations clustered
between 6 to 10 tokens. For both treatment conditions with no communication, they observe a level of rent accrual relatively close to that predictedby the Nash equilibrium (49 percent). Thus, even in an environment of
extreme heterogeneity in subject endowments, communication remains a
powerful mechanism for promoting coordination, resulting in rents very
close to those observed in the homogeneous decision setting discussed
above.
Three follow-up experimental designs were conducted by Pamela
Schmitt, Kurtis Swope, and James Walker (2000). They used the samebaseline design as ﬁrst described above with three variations in regard to
the information component of the situation. In all of their protocols forthe communication aspects of the experiment (rounds eleven throughtwenty-ﬁve), six out of the eight players were invited to communicate with
one another in one location. In their ﬁrst protocol, two of the remaining“players” were computerized decision makers whose decisions were each

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89

the result of a random draw of a number between four and twelve. The
other six, who could communicate with one another, were informed
about the constraints on the random draw for “Players 7 and 8.” In theirsecond protocol, two of the players were real subjects who had been separated from the other six players. These real players did not face a constraint on their decisions. In the third protocol, the two separated players
were constrained to invest between 4 and 12 tokens in the common-poolresource. As in the baseline experiments, subjects were informed about
the aggregate investment of all eight players after each round.
In all three protocols, subjects substantially improved their overall efﬁciency in the communication rounds as contrasted to the noncommunication rounds. Limiting who could communicate to six out of the eightplayers did, however, make a difference. The six communicating subjects
were never certain if the announced aggregated investment level reﬂected
higher investments by the two “noncommunicating” players or whether
some of the communicating groups did not follow their agreement. This
uncertainty affected the capacity of the communicating group to keep totheir own agreements. They could always blame the outsiders for any
major overinvestment.
Major differences in interactions and outcomes occurred across thethree protocols. The outsiders in Protocol 2 were the least constrained intheir decisions. The members of the communicating group had the most
difﬁculty in reaching agreements and following them in Protocol 2. The
six communicating subjects in Protocol 2 had a “scapegoat” they could
blame for high investment levels in rounds eleven through twenty-ﬁve. As
shown in table 3.1, the subjects in Protocol 2 were less likely to come toan agreement in the ﬁrst place, had a much higher deviation rate and sizeof deviation when they did agree, and obtained lower payoffs than in theearlier Ostrom, Gardner, and Walker experiments (discussed above andlabeled as OGW in table 3.1) or in Protocols 1 or 3. The problem ofimperfect monitoring was less severe in Protocols 1 and 3. Some subjects
among the communicating group were able to deviate without raisingsuspicion of cheating in these protocols, but they made only small increases over what the group had promised each other.
What this series of experiments found does have considerable implications for those trying to achieve an agreement in the ﬁeld not to overharvest from a common-pool resource. The results provide evidence that communication is less likely to be effective in preventing overharvesting in
Commons Dilemmas “in which a subset of appropriators either cannot
or will not participate in collective action” (Schmitt, Swope, and Walker
2000, 852). The lack of commitment by an outside group is not only asource of additional investment but also gives “insiders” a scapegoat to

8.84
1.21
68.5%
5.95
8.13
1.57
1.71
64%
15.3%
2.75

9.08
1.11

8.71
1.24
73%
5.65
1.54
95%
13%
3.3

2
−9.4%

Random6

Group

Protocol 1(5 experiments)

8
−2%

OGW

Source : Schmitt, Swope, and Walker 2000, 849.

Number of subjects
Average rents
Rounds 1–10
Average investment in market 2
Average payoffs
Average rents
Average investment in market 2
Average payoffs
Rounds 11–25
Agreement rate
Deviation rate
Average deviation size

Decision environment
25 tokens
Repeated communication

TABLE 3.1
Summary results across environments

Outside

31.1%
50%
7.83

9.50
1.17
6.1%
9.00
9.90
1.27
1.30

2
−12.3%

8.76
1.20

6

Group

Protocol 2(3 experiments)

28.5%

2

Outside

97.8%
15.9%
2.52

7.23
1.36
80.4%
5.61
8.28
1.54
1.70

8.40
1.37

6

Group

Protocol 3(3 experiments)

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91

blame if their own harvests are higher than agreed upon. “The problem
becomes more severe when outsiders have less constraints on their overall
appropriation behavior and their ability to behave strategically” (852).
Sanctioning Experiments
Participants in ﬁeld settings are frequently able to communicate with one
another on a face-to-face basis, at least from time to time, either in formally constituted meetings or at social gatherings. In many ﬁeld settings,
where the resource has been sustained over a long time, participants havealso devised a variety of formal or informal ways of monitoring and sanctioning one another if rules are broken (discussed in chapters 8 and 9).
Engaging in costly monitoring and sanctioning behavior is, however, notconsistent with the theory of norm-free, complete rationality (Elster
1989a, 40–41). Thus, it was important to ascertain whether subjects in acontrolled setting would actually pay funds from their own earnings inorder to sanction the less cooperative behavior of other participants. The
short answer to this question is yes, they will.
All sanctioning experiments used the 25-token design since appropriation levels had been much higher in this design. Subjects played ten roundsof the baseline game modiﬁed so that the individual contributions ineach round were reported as well as the total outcomes. Subjects werethen told that in the subsequent rounds they would have an opportunity
to pay a fee in order to impose a ﬁne on the payoffs received by anotherplayer. The fees ranged in diverse experiments from $.05 to $.20 andthe ﬁnes from $.10 to $.80. In brief, the ﬁnding from this series of experiments was that much more voluntary sanctioning occurs than the zerolevel predicted.
Subjects react both to the initial cost of sanctioning and to the fee-toﬁne relationships. They sanction more when the cost of sanctioning is lessand when the ratio of the ﬁne to the fee is higher. Sanctioning is primarily
directed at those who invested more in the common-pool resource. A few
sanctions, however, appear to be a form of “blind revenge.” These wereﬁnes made by subjects who had themselves been ﬁned by unknown others
for their high levels of investment. In these few cases, the sanctioners
picked on those whose investments were lower than others, and thus weresuspected of being the ones who had previously sanctioned them (E. Ostrom, Walker, and Gardner 1992).
In this set of experiments, subjects were able to increase their returns
from the common-pool resource modestly to 39 percent of maximum,
but when the costs of fees and ﬁnes were subtracted from the total, thesegains were wiped out. When subjects were given a single opportunity to92

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communicate prior to the implementation of sanctioning capabilities,
they were able to gain an average of 85 percent of the maximum payoffs(69 percent when the costs of the fees and ﬁnes were subtracted).
Covenanting Experiments
In self-organized ﬁeld settings, participants rarely impose sanctions on
one another that have been devised exogenously as was done in the above
sanctioning experiments and in the trust experiments, where the experimenters assigned one position the authority to sanction the holder of theother position. In the ﬁeld, sanctions are much more likely to emerge
from an endogenous process of crafting their own rules, including thepunishments that should be imposed if these rules are broken. Spendingtime and effort in a linked collective-choice situation designing rules creates a public good for all of those involved. Crafting rules for an operational situation is thus a second-level dilemma that theorists have argued
is no more likely to be solved than the original commons dilemma.
Noncooperative game theory predicts that participants will not undertake such efforts. This is the foundation for the repeated recommendationthat rules must be imposed on participants by external authorities whothen assume ofﬁcial responsibility for monitoring and enforcing theserules and are paid a salary for their work. Since self-organized rules arefound in many local common-pool resource situations, it appears thatparticipants frequently do design their own rules contrary to the theoretical prediction. Few scholars are able to witness these processes, however,in the ﬁeld.
In order to observe what happens in these settings, subjects experienced
with baseline and sanctioning experiments were recalled and given an
opportunity to have a “convention” in the laboratory. In a face-to-face
discussion, subjects could decide whether or not they would like to haveaccess to a sanctioning mechanism like the one described above, howmuch the ﬁnes and fees should be, and on the joint investment strategy
that they would like to adopt. All of the subjects in these groups wereendowed with 25 tokens in every round. Four out of six experimentalgroups adopted a covenant in which they speciﬁed the number of tokens
they would invest and the level of ﬁnes to be imposed. The ﬁnes determined by the participants ranged in size from $.10 to $1.00 (E. Ostrom,
Walker, and Gardner 1992).
The groups that crafted their own agreements were able to achieve an
average of 93 percent of the maximum in the periods after theiragreement. And, the defection rate for these experiments was only 4 percent. The two groups that did not agree to their own covenant did notfare as well. They averaged 56 percent of the maximum available returns

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and faced a defection rate of 42 percent (E. Ostrom, Walker, and Gardner
1992). Consequently, those subjects who used an opportunity to covenantwith one another to agree on a joint strategy and choose their own levelof ﬁnes received very close to optimal results based entirely on their own
promises and their own willingness to monitor and sanction one anotherwhen it was occasionally necessary (see Frohlich, Oppenheimer, andEavey 1987 for similar ﬁndings).

Replications and Extensions of Commons Dilemma Experiments
One of the great advantages of laboratory experiments, as we saw with
the Trust experiments in the ﬁrst part of this chapter, is that they can be
replicated as well as modiﬁed by other researchers so that one can gain
ever greater conﬁdence in the ﬁndings. One of the ﬁrst replications was
conducted by Rocco and Warglien (1995), who found very similar outcomes in the baseline, no-communication situation as well as in the faceto-face communication settings. They were interested, in addition, in thequestion of whether similar results would be obtained in an experimental
design where communication was not organized on a direct face-to-facebasis. They used identical structural variables of our earlier Commons
Dilemma design with and without face-to-face communication. Then,they added a design with limited communication in a form of computerized exchange. They replicated the substantial improvement in efﬁciency
that was earlier obtained in face-to-face communication. When the communication was conducted electronically without a face-to-face discussion, subjects did not increase their cooperation levels to the same extent.
Another very interesting series of replications and extensions was conducted by Juan-Camilo Cardenas (2000, 2003) using ﬁeld laboratories
set up in school buildings in rural Colombia rather than a computer-basedlaboratory on a university campus. Cardenas initially invited over two
hundred villagers to participate in a series of common-pool resource experiments. Several closely paralleled the ones conducted at Indiana University and discussed above. Others extended the questions that could be
addressed. The villagers who Cardenas invited were actual users of local
forests for the extraction of ﬁrewood, natural ﬁbers, and log timber aswell as local water resources. One of the basic questions he wanted topursue was whether experienced villagers who were heavily dependent
on local forests for wood products would behave in a manner broadly
consistent with that of undergraduate students at an American university.
The answer to this ﬁrst question turned out to be positive. He wrote
his instructions in Spanish and in a manner that would be easily understood by villagers. Instead of tokens—which are an easy medium for un-

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dergraduates to understand—he asked villagers to decide on how many
months a year they would spend in the forest gathering wood products
as contrasted to using their time otherwise. Each villager had a copy of apayoff table, which was the same as that of the other seven participants,
showing that as the number of months that each individual would spend
in the forest increased, she would gain more returns, but that the returnto all of them depended on their own keeping the harvesting time to avery low level.
In the baseline, no-communication experiments, Cardenas found a similar pattern as we had found with subjects from Indiana University. Villagers substantially overinvested in the resource. While there was considerable variation among groups, villagers on average achieved 57.7 percentof their optimal return in the last three rounds of the baseline experiments(Cardenas 2000, 316). The daily wage that most of the villagers could
earn at the time of the experiment was around 7,000 pesos (or around
U.S. $5.40 at the time). If they all invested at an optimal level, they would
earn around 12,900 pesos. They did earn around 7,884 pesos in theseexperiments for the two or three hours they were involved in initial practice sessions and the actual experiments themselves.
Face-to-face communication enabled them to increase efﬁciency on average to 76.1 percent of optimal. Considerable variation among groups
existed, which Cardenas was able to explain using information about theparticipants ﬁlled in after that experiment was completed. He found, forexample, that when most members of the group were already familiar
with common-pool resources such as the collective use of a mangrove,
they used the communication rounds more effectively than when mostmembers of the group were dependent primarily on their own assets. Cardenas also found that “social distance and group inequality based on theeconomic wealth of the people in the group seemed to constrain the effectiveness of communication for this same sample of groups” (2000, 317;
see also Cardenas 2003).
Cardenas, Stranlund, and Willis (2000) report another fascinating extension. In ﬁve experiments, the villagers were given a chance to communicate after the initial ten rounds of the baseline condition. In ﬁve otherexperiments, they were also told that a new regulation would go into
force that mandated that they should spend no more than the optimallevel of time in the forest each round (which in this case was one month
per villager). They were also told that there would be a 50 percent chancethat conformance to the rule would be monitored each round. The experimenter rolled a die in front of the subjects each round to determine
whether an inspection would take place. If an even number showed up,
there would be an inspection. The experimenter then drew a number fromchits numbered between one and eight placed in a hat to determine who
would be inspected. Thus, the probability that anyone would be inspectedS T U D Y I N G A C T I O N S I T U AT I O N S I N T H E L A B

95

was 1/16 per round—a low but very realistic probability for monitoring
forest harvesting in rural areas. The monitor checked the investment of
the person whose time had come without revealing the result to others. If
the person was over the limit imposed, a penalty was subtracted fromthe payoff to that person. No statement was made to others whether theappropriator was complying with regulations or not.
The subjects in this experimental condition actually increased their harvesting levels in this externally imposed sanctioning experiment in contrast
to behavior when no rule at all was imposed, and the subjects could communicate on a face-to-face basis. What was remarkable about these outcomes was that subjects, who were simply allowed to communicate with
one another on a face-to-face basis, were able to achieve a higher joint
return than the subjects who had an optimal but imperfectly enforced external rule imposed on them. These experiments provide further supportfor Bruno Frey’s (1997a, 1997b) hypothesis that external rules imposedon citizens can crowd out intrinsic motivation and lead to worse outcomesthan reached through voluntary agreements.6 As the authors conclude:
We have presented evidence that indicates that local environmental policies thatare modestly enforced, but nevertheless are predicted by standard theory to bewelfare-improving, may be ineffective. In fact, such a policy can do more harmthan good, especially in comparison to allowing individuals collectively to confront local environmental dilemmas without intervention. We have also . . .
presented evidence that the fundamental reason for the poor performance of
external control is that it crowded out group-regarding behavior in favor ofgreater self-interest. (Cardenas, Stranlund, and Willis 2000, 1731)

Common-pool resource experiments have also been replicated and extended upon by Marco Casari and Charles Plott (2003). Casari and Plottwanted to explore whether an institution that had been used in the Italian
Alps for centuries and was thought to be highly effective would generate
positive results in a laboratory setting. The Alpine system had a relatively
simple structure.
The population of a village developed a contract among themselves, subject tothe approval of the regional government, called “Carte di Regola,” where theydescribed a system for monitoring and sanctioning those who are discovered
violating or exceeding patterns of use that the villagers agreed upon in the contract. The “Carte di Regola” speciﬁed in advance the conditions under whicha sanction could be inﬂicted on a person found in violation of the contract andthe amount of the ﬁne. . . . Any villager could report a violation but he usually
incurred a cost in the form of a monitoring effort to discover the violator andadditional costs to bring him to court. A share of such a ﬁne usually went to
the person who discovered the violator in order to give an incentive to monitor.
(Casari and Plott 2003, 218)

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Casari and Plott used the same functional form for a payoff function as
we had earlier used with Indiana University subjects (see equation 1
above), but they increased the monetary incentives by more than threefold.7 Using Cal-Tech subjects, they ﬁrst ran a baseline experiment thatclosely paralleled our earlier baseline experiments. Without communication or sanctioning, they found—as we had earlier found—that the resource was substantially overused, even more than the Nash equilibrium.
Subjects earned only 28.4 percent of the optimal return, while the Nashequilibrium would have earned them 39.5 percent. They also found substantial variations among individual subjects in the amount they overused
the resource, as we had earlier.
Casari and Plott then changed the transformation function and the payoffs of the game. They used two sanctioning conditions—weak andstrong. In both conditions, after the decision regarding harvesting had
been made and the total investment levels had been announced, a subject
could select an option to inspect the decision made by any of the othersubjects at a set cost. After this decision had been made, the harvesting
decision of the inspected subject was made public information, but not
the identity or number of subjects requesting an inspection. A ﬁne was
imposed for each unit appropriated above the announced level and transferred to the inspector. The “inspector” made a proﬁt when the fee that
had been paid to carry out the inspection was less than the amount transferred, which was in turn dependent upon the amount in which the inspected appropriator had exceeded the announced level. The weak sanctioning option did not change the predicted Nash equilibrium for thegame, but the strong sanctioning option made the predicted Nash equilibrium approach the socially optimal level of appropriation.
In the experiments conducted with weak sanctions, slightly over halfof the actions were inspected—a much higher level than predicted by classical game theory. And subjects obtained closer to optimal levels of returns than they had without sanctions. In the strong sanction condition,
the efﬁciency of the joint return was 94 percent, but when inspection fees
are subtracted, the net return was 77 percent of optimal (238). Almost all
actions were inspected. It turned out that the subjects making the lowest
uses were more aggressive inspectors than those making the highest uses.
Casari and Plott found that the subjects behaved in a manner consistent
with having heterogeneous preferences rather than all subjects having
preferences that were monotonically aligned with the available payoffs.
Some individuals appeared to be more spiteful than others. This helps to
explain the success of the “Carte di Regola” system, as it would appear
that it was able to use the “heterogeneity of preferences to socially advantageous ends” (241). By sharing the ﬁne with the person who reported
the violation, the system channeled the behavior of the more spiteful indi-

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97viduals into socially useful purposes. Overall, they found that the experiment mimicking the set of rules used in the Italian Alps greatly improved
the efﬁciency of resource use as contrasted to the baseline experiment
without the sanctioning options.
Casari and Plott provide a cogent and important theoretical explanation for their ﬁndings based on a model of individual choice called a “heterogeneous, linear other-regarding model.” Their model predicts the outcomes of their experimental designs well, while the model of individual
behavior focused only on monetary returns does not explain the behavior
in their common-pool resource experiments (or that of our own earlier
experiments) well at all. Given that the results from both types of experiments—as well as many other extensively replicated experiments—are
not consistent with what is predicted from classical game theory, it is time
to discuss the deep problem of animating institutional analysis. We will
do so in chapter 4.

Conclusions
My intention in writing this chapter was to provide several concrete illustrations for the reader of the working components of an action situationand how they combine to generate a clear-cut structure of extrinsic incentives. The experimental researcher must create all of the working parts ofan action situation in the protocols for an experiment and attempt toisolate the experiment from external, confounding variables. The experimental lab is thus an excellent environment to learn about action situations and how changes in one component of a situation affect interactions
and outcomes.
We have also learned a great deal about interactions and outcomes in
two particular games: the Trust Game and the Commons Dilemma. Basically, we have learned that:
1. In a two-person Trust Game, both Investors and Trustees engage in morecooperative behavior than predicted when using a model of behavior based on
purely monetary returns.
2. The level of trust and reciprocity achieved in a Trust Game depends on
many factors associated with the relative position of the subjects, the information they have, and the type of sanctions made available to participants.
3. When participants in an N-person Commons Dilemma are held apart andunable to communicate on a face-to-face basis (or via the type of signaling thatis feasible in two-person situations), they overuse a common-pool resource.
4. Participants initially use an opportunity for face-to-face discussions toshare their understanding of how their actions affect the joint outcomes andarrive at a common understanding of the best joint strategy available to them.

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5. Participants are willing to promise others whom they assess as being trustworthy that they will adopt a joint plan of action. Most individuals keep theirpromises (in situations where substantial advantage can accrue for breaking
the promise).
6. If agreements are broken, individuals become indignant and use verbal
chastisements when available. They are also willing to use costly sanctions
when they have the opportunity to select this option, and even tend to overuse them.
7. Participants use heuristics in dealing with complex problems.
8. Heuristics vary in their capabilities to cope with a changing conﬁgurationof actions by other participants.
9. When given an opportunity to communicate, craft their own rules, andsanction nonconformance to these rules, some groups were willing to do so.
Through their own efforts, these groups achieve close to optimal results.
Those who forego such an opportunity are not able to sustain a high level of
performance.

In other words, a substantial number, but not all, of the individuals inthese carefully controlled experiments are trustworthy and reciprocate
trust if it has been extended. When behavior is discovered that is notconsistent with reciprocity, individuals are willing to use retribution in avariety of forms.8 Individuals also initially rely on a battery of heuristics
in response to complexity. Without communication and agreements on
joint strategies, these heuristics lead to overuse. On the other hand,
individuals are willing to discuss ways to increase their own and others’
payoffs over a sequence of rounds. Many are willing to make contingent promises when others are assessed as trustworthy (Bendor and
Mookherjee 1990).
These conclusions are not consistent with predictions derived from classical game-theoretic models of participants focusing entirely on monetary
returns in these situations. They are, however, consistent with evidence
gathered from empirical research in the ﬁeld (Van Vugt et al. 2000). Thus,
it is time that we address the question of how institutional analysis should
be animated. In chapter 4, I dissect the other holon—the participant—which, together with an action situation, creates an action arena. To makea prediction about the likely outcomes to be achieved in a particulararena, one must animate the analysis by specifying key assumptions about
the individual actors holding positions in the situation.

FourAnimating Institutional Analysis

IN THE FIRST three chapters of this book, I focused on the exogenous
variables that underpin all action situations and the components of action
situations at operational, collective-choice, or constitutional-choice levelsof analysis. So far, I have provided only a minimal sketch (in chapter 3)of the contemporary theory used to explain and predict how participantsin action situations are expected to choose among actions. It is this theory(and models of this theory) that analysts use to predict likely actions ofdiverse participants and their cumulative outcomes. Participants are thesecond holon of an action arena and the animators of institutional worlds.
Without humans who make decisions in a situation, there is nothing but
the biophysical world to explain.
In this chapter, I discuss the approaches taken by scholars to the puzzleof animating diverse types of situations ranging from highly competitivemarkets to various types of social dilemmas. We will start with situations
that are relatively well understood—open, competitive, posted price markets. In these settings, a theory of full-information, rational behavior focusing on material outcomes has been shown to be a powerful engine ofprediction and is consequently very valuable for institutional analysis.
Then, I add complicating assumptions that redeﬁne the information processes, the valuation mechanisms used by individuals, and/or the selection
processes that individuals adopt.
An institutional theorist must self-consciously posit the kind of information participants possess, the relevant preference structure of the participants, and the process they use for choosing among actions. Assumptions about information, preferences, and choice mechanisms are thus theessential components of this holon. All three need to be speciﬁed in orderto generate hypotheses about interactions and outcomes that can be tested
in a particular type of action situation or linked set of action situations.1
As a scholar trained extensively in both political science and economics,
I have used (and plan to continue to use for many purposes) the basic
assumptions about human behavior in models developed to represent
what is called “rational choice theory” as it has currently evolved in contemporary microeconomics and game theory (for basic textbooks see
Gardner 2003; Dixit and Skeath 1999; Gintis 2000a).

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Animating Open, Competitive Processes
First-generation rational choice theory and related models have proved
valuable for predicting human behavior in stable, competitive market settings and in competitive electoral and legislative settings where the issue
space is constrained (Aldrich 1995; Nardulli 1995; V. Smith 1991, 2000).
In these stable and repetitive settings, individuals are able to learn about
the full, relevant structure of the situation and attach positive or negative
preferences to actions and outcomes. When one is explaining behavior infamiliar and often-used situations with complete information, one is not
faced with the problem of explaining how individuals learn about thesituation and its likely outcomes and payoffs. One can assume that learning has taken place and proceed with an explanation of behavior by informed participants using a mental model of the situation that is at least
roughly approximate to the external situation itself. Explaining how individuals learn turns out to be extremely challenging.2
Predictions from these models are empirically supported at an aggregate level in open, competitive, posted-price market settings and at anindividual level in carefully designed experimental settings of competitivemarket situations (see V. Smith 1982; Kagel and Roth 1995; Lian andPlott 1998; Noussair, Plott, and Reizman 1995). If open, posted-price,competitive markets for the exchange of goods (or similar situations) were
the only type of action situations that individuals faced, then rational
choice theory and its currently accepted models would clearly be the only
theory (and related models) of human behavior that one would need forprediction of outcomes. Frohlich and Oppenheimer (2001, 22) assess “thetraditional spare economic model of decision making” as being “usefuland robust in predicting behavior in contexts of choice which are relatively stable, hence in which subjects have learned to call up particular
representations on a repeated basis” (22).
Thus, it is important to learn about relatively simple situations thathave been successfully analyzed and shown to have empirical support
before venturing off into the interesting but difﬁcult work of understanding and explaining behavior in more complex settings.3 To some extent,
we can think of learning how to analyze behavior in these games as somewhat similar to a young chemist learning how to make simple compounds
or a biologist learning how to dissect a frog. These tools are not all thatare needed to explain complex chemical and biological phenomena, butthey are a useful starting place. Further, the ways of analyzing relatively
simple situations as formal games have already been developed. It makes
little sense to try to utilize a complex form of analysis, or develop entirely
new forms of analysis, when tools are already available for the analysis
and explanation of behavior in many situations.

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101Thus, I strongly advise institutional analysts to learn how to use theworking assumptions of models developed to apply contemporary rational choice theory. These are:
1. Individuals possess as much information about the structure of a situation
as is contained in the situation itself.
2. Individuals assign a complete and consistent, internal valuation to outcomes that is a monotonic function of an individual’s own net external payoff.4
3. After making a complete analysis of the situation, individuals choose anaction in light of their resources to maximize expected material net beneﬁts tothemselves given what others are expected to do.

We will use the term rational egoist to describe a participant in a situation
whose behavior can be predicted using these three assumptions. Knowing
the assumptions of a rational egoist and how to use them enables an analyst to begin using theoretical tools to predict how individuals makechoices and generate outcomes in competitive situations. Further, someindividuals are present in most situations whose choices of strategies canbe predicted using a model of a rational egoist who focuses on the immediate material payoffs to self and not on other values.
Armen Alchian (1950) demonstrated long ago that those who do not
behave as rational egoists in an open, competitive market will not be present in any signiﬁcant number once demand and supply have led to an
equilibrium. More recently, Gode and Sunder (1997) demonstrated that
it is not even necessary to assume that all individuals are rational egoists—even at equilibrium—to establish efﬁcient markets. Rather, they show thatthe efﬁciency of market exchanges derives from the set of rules constituting a market, rather than the sophisticated calculation of the participants.
In their model, they show that “zero intelligence traders” reach efﬁcient
outcomes when seven essential rules constrain the actions of buyers andsellers and not otherwise (see chapter 7, note 13). Thus Alchian, as well
as Gode and Sunder, show that it is the structure of markets that leads
participants to make efﬁcient choices rather than the assumptions madein economic theory about the internal structure of individual valuationand choice.

The Challenge of Imperfect Information
Competitive markets or other full-information, competitive situations are
not the only situations facing individuals that are of interest to institutional analysts. Further empirical research has uncovered a rich array ofanomalies that systematically occur in situations that were once thought
to have properties leading to clear predictions.5 Situations that are rela-

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tively simple social dilemmas that make clear predictions about expectedbehavior, for example, frequently evoke positive or negative internal values for participants who are not monotonically related to the objective
payoffs involved (as discussed in chapter 3).
When other-regarding preferences and/or intrinsic values are assigned
to outcomes and actions, the situation is one of incomplete, rather thancomplete, information because other players cannot know exactly how
an individual is valuing these actions and outcomes. In order to analyze
these situations, one needs to make different assumptions about the values
taken into account by individuals. Further, as Kenneth Koford (2003)
illustrated with his research conducted in Bulgaria, the way a situation isframed may strongly affect how individuals embedded in a particular culture and history view it and value actions and outcomes (McDermott
2001). Frohlich, Oppenheimer, and Kurki (2004) have analyzed simple
situations where small changes in the structure of the situation itself—what they call the context of the situation—evoke norms in some participants that were not expressed in a baseline situation.
Instead of complete information, some situations generate only partial
information. Further, the distribution of the information may be asymmetric. One may also be confronted with the need to examine how individuals view risk, uncertainty, and information asymmetries and how they
react to the actions and perceived attributes of other participants. Situations also differ in regard to the extent of repetition and whether the same
individuals continue to interact with one another over time or whether
interactions are largely with a continuous stream of strangers. How doall of these affect choice? When rational choice models—as usually operationalized—fail to predict outcomes, other theoretical tools are needed(Vanberg 2002).
Political economists thus ﬁnd themselves at a very interesting juncture.
Experimental researchers have shown that behavior after several rounds
in experimental markets and other tightly constrained, competitive processes tends to be consistent with the predictions stemming from a rational egoist model of human behavior. On the other hand, experimental
researchers have also shown that behavior in many forms of social dilemmas and other games is not consistent with what would be predicted if
all individuals behaved in a manner consistent with the rational egoist
model (see Gintis 2000a for a review). Further, the level of trust and cooperation exhibited varies substantially from one experimental design toanother. The combination of the extensive experimental research and thestrong theoretical arguments made ﬁrst by Alchian (1950) and then byGode and Sunder (1997) that the institutional structure of a market,
rather than the model of the individual, leads to the outcomes predictedby market theory challenges all political economists. We need to know

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103when to use the assumption of a rational egoist for all participants or toassume some individuals may have other-regarding preferences or value
norms such as trust or reciprocity.

Assumptions Used in Animating Participants
Consequently, I strongly advise institutional analysts not to rely on one
and only one theoretical tool to explain human behavior unless they wish
to conﬁne their analyses entirely to situations that can be successfullymodeled as simple, competitive, complete-information situations. The
three basic assumptions of rational choice theory are, however, a usefulstarting point for doing institutional analysis. They illustrate the components of any theory of human behavior that an institutional analyst would
use in analyzing situations other than highly competitive situations. Thus,
whether a participant is an individual or a corporate actor, the analyst
must make assumptions about three components of human behavior inorder to animate an institutional analysis. These assumptions are the components of the holon called “participants” in the Institutional Analysis
and Development (IAD) framework. Each of these assumptions can also
be unpacked into multiple layers. These are:1. the way that participants acquire, process, represent, retain, and use
information;
2. the valuation that participants assign to actions and outcomes; and3. the processes (maximizing, satisﬁcing, or using diverse heuristics) that participants use for selecting particular actions or strategic chains of actions inlight of their resources.

Once one decides to explore alternative assumptions about human behavior, the number of choices that the researcher has to make are substantial. Alternative assumptions are not likely to be as clean and elegantly
simple as those of rational choice models. Frohlich and Oppenheimer(2001, 22) reﬂect that the “standard rational choice model, simple, elegant and decisive, is not liable to be replaced with as simple and manipulable a model. The anomalies which have been identiﬁed are broad anddiffuse and they are likely to require more theoretical superstructure for
their explanation” (see also Levi 1997b). The grave hesitation of sometheorists to adopt “more realistic” assumptions stems, to a large extent,
from the messiness of the alternative superstructures.
One strategy adopted by many (including the author) is not to change
all of the assumptions at the same time when trying to model a particular
type of situation. In stable and repetitive situations where intrinsic values
are important, one may retain assumption one—regarding full informa-

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tion about the structure of the situation—while focusing primarily on thetypes of values of importance to individuals and the resulting strategies
they select (see Ahn et al. 2003; Cox 2004). Alternatively, one may focus
primarily on the information processing and assume individuals areboundedly rational (Selten 1998; B. Jones 2001). If the situation is uncertain and complex, one may focus on the third assumption related to how
individuals make decisions about their choices. In many ﬁeld settings,
participants use heuristics or rules of thumb that they have learned overtime—or were taught to them by their seniors—that give them goodenough solutions that there is little need to pay the costs of a full search(see Gigerenzer and Selten 2001). Thus, which assumption (or assumptions) one changes and the particular assumptions made depend on thesituation to be explained.
A thick book could be devoted to elucidating the different theories andmodels that posit diverse assumptions about these three broad components of human choice. In order to keep the present book focused, I will
brieﬂy discuss here only some of the most relevant alternative assumptions that are used by institutional theorists and will refer the reader tocontemporary literature related to these assumptions. I will conclude witha discussion of the importance of the institutions within which individuals
interact to the likelihood that individuals with normative orientations andother-regarding preferences may be able to achieve higher outcomes overtime in social dilemmas and may even change the distribution of strategies
used by individuals over time.
Assumptions about Information Processing and Mental Models
Many of the situations of interest to institutional analysts are uncertain
and lack the kind of rules leading to the selective pressures and information-generating capabilities of highly competitive processes. In analyzing these situations, theorists frequently substitute the assumption ofbounded rationality—that participants are goal oriented and try to berational but face cognitive limits—rather than the assumptions of complete information (see Simon 1957, 1972, 1995; Williamson 1985, 2000;
V. Ostrom 1997; B. Jones 2001). In some complex situations, the available information may greatly exceed the competence of an individual tocompute a solution based on fuller analysis (Heiner 1983). As discussed
in chapter 2, the game of chess exceeds the capacity of any human tocompute an analytical solution. In analyzing the behavior of chess players,
one can assume that they know the current pattern of chess pieces on theboard, but not that they know all of the action-outcome linkages.
Most cognitive scientists stress that humans expend substantial effort
to make sense out of the variety of signals (and symbols) they receive as

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A N I M AT I N G I N S T I T U T I O N A L A N A LY S I SExternal Action Situation
Participant in a Situation
Information about
Action Situation

Information about
Actual Outcomesof Prior Action

Perception of
the Situation

ReviseModel
Possible
Actions

MentalModel(s)Expected
Outcomes

CultureChosenActions

Actual Outcomes
Figure 4.1 The relationship between information, action-outcome linkages, andinternal mental models. Source: Adapted from Denzau and North 2000, 36.

they interact within a variety of situations in life (Busemeyer and Myung
1992). Individuals attempt to create a mental model or a representationof diverse situations so as to be able to make reasonable decisions in these
multiple settings (Holland et al. 1986). Mental models are affected by atleast two basic sources—feedback from the world and the shared culture
or belief system in which an individual is embedded.
As individuals interact in a diversity of situations, they receive information about the structure of the situations they are in prior to making adecision and usually receive some kind of feedback after they take anaction. Participants need to discover an appropriate model of the situation
they are in through repeated interactions in it or similar structures (Plott1996). Drawing on Denzau and North (2000), one can envision a participant in a situation receiving information about its structure (how many
participants, who they are, the beneﬁts and costs of action-outcome linkages, etc.) (see ﬁgure 4.1). The participant initially relies on earlier mental
models formed of this situation to calculate expected outcomes of diverse
actions. If satisﬁed with the outcomes, they may not search for further
information. What they learn about the outcome of their own and others’
actions, however, will potentially stimulate an effort to revise their mental
model if there is an incongruity or a lack of satisfaction.

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Cultural belief systems also affect the mental models that individuals
utilize. Most of childhood is spent in a combination of observing others
interacting, being told the prudent or proper way to perceive situations
and to act within them, and taking actions based on both observation andinstruction (Tomasello 2004). Parents actively encourage their children tolearn a culturally appropriate set of strategies, including industriousness,
responsibility, and self-reliance, for doing well in a variety of situations(Low 1989). The distribution of imparted traits varies by whether a culture is highly stratiﬁed or not, and by the gender of the child (ibid.). When
we indicate that people share a culture, it is a shorthand way of indicatingthat the wide diversity of mental models that individuals have invented
has been reduced to a smaller set within those sharing the culture (Benedict 1934). Culture may also be viewed as an intergenerational transferof past experience. The mental models that scientists hold come about
initially from what they learn in school as these are modiﬁed by their own
research and that of others (Gopnik 2004).
Individuals learn from experience and from shared mental models.
Learning is enhanced in situations that are often repeated. Interactions
with the same set of individuals enable an individual to obtain a better
estimate of the strategies that speciﬁc others adopt. Theoretical and experimental work has shown that individuals tend to experiment with diverse
actions and then adopt those that have returned the highest payoffs in thepast (Selten, Abbink, and Cox 2001; Busemeyer and Myung 1992). If thesituation in which individuals interact is relatively stable and repetition
occurs frequently, and if there is pressure to improve performance, individuals will tend to discover those strategies that an omniscient individual
would have selected.6 Of course, the larger the number of individuals involved in a repeated situation, and the more diverse their strategies, themore difﬁcult it is for anyone to gain an accurate perception of others’
strategic behavior.
The convergence through learning to the same strategy as is predictedunder full rationality is unlikely to happen when the number of participants in a situation is large and the situation itself is complex, changes
frequently, and/or the individuals do not participate in that situation withregularity or any induced need to increase performance. Information
search is costly. The information-processing capabilities of human beings
are limited. Individuals must often make choices based on incomplete
knowledge of all possible alternatives and their likely outcomes. With
incomplete information and imperfect information-processing capabilities, individuals may make errors in perception, in their comprehensionof how a complex structure works, or simply in computations (V. Ostrom
1986, 1997).

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107The experiences that different individuals have had and their interpretation of them may differ substantially. It is thus possible that a single individual might be able to call up more than one mental model or that themental model of participants in the same situation will differ. This presents a theoretical problem for the analyst trying to understand behavior.
“How can we understand a person’s choices if, when confronting a given
environment, she can have multiple representations, each of which is associated with different values?” (Frohlich and Oppenheimer 2001, 6).
Denzau and North (2000) stress the importance of communication as
one way to enable individuals to develop a shared mental model. If thesame individuals repeatedly interact in free and open communication, andif they already share some broad cultural views of the world, mental models are more likely to converge. The positive impact of communication—
particularly face-to-face communication on joint outcomes in a wide diversity of social dilemma experiments—has been replicated frequently, as
discussed in chapter 3.
Individuals may not always be able to engage in open, repeated communication, however. John Schiemann (2002) examines this problem in his
analysis of the mental models used by different age groups coming fromthe same culture but who have had vividly different experiences in life.
Older Hungarians, who had directly experienced the bloodshed of WorldWar II, interpreted the situations following the ending of the Soviet Union
differently than did younger Hungarians who only read about the earlier,dramatic events. A generational difference also exists on how individuals
view the Chinese leadership’s decision to use force in the Tiananmen incident in 1989.7 Unless there is open and active communication among
participants over some time, they may simply use different mental models
when interacting in the same external situation. This can lead to grossmisunderstandings and disappointments or even to major violence.
Frohlich and Oppenheimer (2001) address the puzzle of multiple internal models by asking what factors in an environment are likely to affectthe way a participant perceives a situation and the way the individual
optimizes in that situation given that “paying attention” is costly. Few
humans gain full mastery of all of the potentially relevant details in acomplex, ongoing situation. Frohlich and Oppenheimer single out two
properties of a situation as most important in affecting the way a situationis perceived. “The ﬁrst property is the salience of the elements in a choicesituation,” by which they mean “the degree to which an element is linked
to possible changes in the welfare of the decision maker” (8). The second
property is the vividness of the situation or the “amount and quality ofthe sensory details of the objects encountered” (8). These attributes areimportant in gaining attention given the variety of signals an individual
receives. “In order for something to grab one’s attention it must displace

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Communication
External Action Situation
Vividness

Salience

Participant in a Situation

Information about
Action Situation

Information about
Actual Outcomesof Prior Action

Perception of
the Situation

ReviseModel
Possible
Actions

MentalModel(s)Expected
Outcomes

CultureChosenActions

Actual Outcomes

Figure 4.2 The impact of communication, vividness, and salience on the relationship between information, action-outcome linkages, and internal mental models.

something else to which one is attending. To accomplish this, a new focus
of attention must have a higher claim. Attention shifts from one object ofattention to another as if there were a threshold of value attached to theformer which has to be surpassed for the competitor to displace it” (8).
The repeated ﬁnding that face-to-face communication in social dilemmas
is more effective than written communication may be related to its beinga more vivid form of communication.
The role of vividness of symbols or rituals in solidifying the sharedmental models of large groups of individuals is an important theme in thework of anthropologists (Rappaport 1979, for example). The philosopher
Allan Gibbard (1990) stresses the importance of rituals in achieving
shared norms of what participants should do in particular situations andhelping to increase the probability that they will do so in the future partly
because they share a vivid memory of what should be done.8 Thus, thevivid ritual or symbol helps to select one mental model over others. The
resulting congruence in behavior and outcomes helps to reinforce thatmodel among those sharing the same culture. The role of communication,
salience, and vividness in affecting mental models can be represented as
in ﬁgure 4.2.

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109The architecture of information processing broadly reﬂected in ﬁgure
4.2 illustrates that individuals do not respond immediately to all information but rather to that which is communicated in a salient and vivid manner.9 On the basis of his own and others’ research, Bryan Jones argues
that individuals and the organizations with which they work are disproportionate information processors. “Disproportionate information processing means that inputs into a decision-making process do not link directly to outputs. As a consequence, there is an imperfect match between
the adaptive strategies people devise and the information they receive.
This mismatch is the inner cognitive and emotional architecture of thehuman brain ‘showing through’ in responding to information” (Jones
2001, 9). Jones illustrates this mismatch with a variety of imaginative
quantitative studies of disproportionate information processing in shifting electoral responses over time, in public budgetary changes, and in
the level of coverage of national policy issues by Washington-based news
publications. The substantive interpretation of his results for an institutional analyst is that: “change in human institutions tends to be quite
conservative—most cases cluster around a central peak—but is subject tooccasional quite large punctuations (the tails). . . . It would seem that ahypothetical decision maker would have to be prepared either for virtually no change or a very large change—he or she could not hope for moderate adjustments to changing circumstances” (184; see also Jones,
Baumgartner, and True 1996).
Denzau and North (2000) reﬂect a similar view of the way change happens. They use the term “punctuated equilibrium” to reﬂect their sensethat the internal models used by individuals remain relatively similar until
some event triggers a large change in the mental model and resulting
changes in the actions that individuals (or organizations) take. Denzau
and North, as well as Jones, also illustrate how individuals are able toovercome some of their own information-processing limits by establishingrules and routines that structure situations in such a way as to enhance
the likelihood that individuals will share a mental model of the situationand take actions that lead to better rather than worse outcomes (North
2005). Humans are thereby able to compensate for—as well as replicate—cognitive processing limits by the way they organize themselves and theprocedures they follow (see also Dawes 1988).
Assumptions about Valuation Processes
How individuals value actions and outcomes has become a very active
discourse among scholars interested in institutional questions (Fehr andFalk 1999; Fehr and Gächter 2000a, 2000b; Bolton and Ockenfels 2000;
Casari and Plott 2003; Frohlich, Oppenheimer, and Kurki 2004; Janssen

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and Ahn 2003). Assuming that all individuals are self-interested utility
maximizers without exploring how individuals reach utility judgments
was a satisfactory scholarly strategy for many years when explaining behavior in highly competitive market settings where one could implicitly
equate utility with proﬁts. As mentioned above, substantial ﬁeld and experimental research supports the second assumption of rational choice
when individuals are interacting in a stable, competitive environment
with full information about the short-term options available to them.
When one turns to the large realm of social dilemmas, however, little
empirical support exists for an assumption that all individuals value only
the material outcomes that ﬂow only to them.10 If this were the case, wewould not have seen the extensive evidence presented in chapter 3 that
Investors frequently send funds to Trustees and Trustees often send funds
back to the Investor at a personal cost to themselves to fulﬁll the trustthat the Investor placed in them. Nor would we have seen subjects keeping
promises that they have made in face-to-face communication rounds toother subjects in a Commons Dilemma experiment to keep their appropriation levels low. Many subjects appear to have other-regarding preferences and to accept norms of behavior backed up by emotions including
pride, guilt, shame, and anger. Whether norms are invoked and lead tocooperative behavior varies across experiments that differ only in regards
to relatively small structural features.
The results discussed in chapter 3 are hard to explain using the standard
rational choice model that all individuals who face the same objective
game structure evaluate decisions the same way!11 And, we cannot simply
resort to the easy criticism that undergraduate student subjects are being
paid a modest sum (or nothing at all) and thus the game is not a valid
test of the theory. Many subjects were not students in these and otherexperiments conducted in multiple countries (Henrich et al. 2004).12 Increasing the size of the payoffs offered in experiments does not appear tochange the broad patterns of empirical results obtained.13 Nor, on thebasis of experimental evidence, can we simply change from an assumptionof universal selﬁsh behavior to an assumption of universal altruism. As
Fehr and Gächter (1998, 847) stress, “Homo Oeconomicus Also Exists.”
In all social dilemma experiments, a “non-trivial minority of subjects exhibits selﬁsh behavior” (847).
Thus, the results of the experiments summarized in chapter 3—andmany others documented by Camerer (2003)—substantially challenge thesecond assumption of contemporary rational choice models that valuation of all players is always focused entirely on extrinsic, immediate, net
beneﬁts to the individual. These well-substantiated facts provide an essential foundation of a more eclectic (and classical) view of human behavior.
Further, as Ben-Ner and Putterman (2000a) stress, humans invest substan-A N I M AT I N G I N S T I T U T I O N A L A N A LY S I S

111tial time in their attempt to “mold the character” of their offspring. If thiswere truly impossible, it would be irrational for adult humans to spend
so much time trying to transform the preference structure of their offspring. On the other hand, adopting the closely related, internal normsof trustworthiness and trust are not the unchanging, universal attributesof all individuals. It is important that we develop tools of analysis thatreconnect the normative side of individual orientations with the calculation of individual beneﬁts.14
Recent research by Rilling and his colleagues (2002) provides an evenfurther challenge. Freely recruited and paid subjects participated in a series of repeated Prisoner’s Dilemma games while connected to a magnetic
resonance imaging (fMRI) scanner. In each round, subjects pressed a button to indicate their choice of cooperation or defection leading to a payoff
per round of $2 each if both cooperated, or of $3 if a subject defected
and if the other cooperated, or $1 if both defected. The researchers compared levels of cooperation and brain activities in several designs including one in which the second player was a programmed computer strategy.
The results of each round were projected onto a screen that the player inthe scanner could see and onto the computer screen of the other player(where applicable). The observed behavior was similar to that of otherrepeated dilemma experiments.
While the observed interactions and outcomes were not novel, linking
the portrait of brain activity to behavior in social dilemmas adds a new
dimension to the body of ﬁndings. And what they found was surprising
to the research team (as reported to Angier, July 26, 2002) as well as tomany readers. Pressing the cooperate key in a reciprocating relationshipgenerated the brightest reactions in the pleasure zones of the brain (theanterovential striatum and the orbitofrontal cortext). The researchers also
tested whether the same level of brain activity was associated with receiving a payoff of $2 in a nonsocial context. They found different patternsof neural activation depending on whether the scanned player thought
she was playing a real human or a computer (see also McCabe 2003).
They concluded that they had “identiﬁed a pattern of neural activationthat may be involved in sustained cooperative social relationships, perhaps by labeling cooperative social interactions as rewarding, and/or by
inhibiting the selﬁsh-impulse to accept but not reciprocate an act of altruism” (Rilling et al. 2002, 403). These ﬁndings are consistent with theearlier theoretical work of Robert Frank (1988), who posited that humanemotions underpin a commitment to reciprocity so that humans feel goodwhen achieving mutual cooperation and feel bad when defections (theirown or others) occur.
Given the extensive evidence from many empirical studies, at least somesocial scientists now tend to follow Amartya Sen’s (1977) advice to stop

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assuming that all individuals are “rational fools.” Instead, they assume
that at least some individuals in some situations do have other-regarding
preferences and do follow norms of behavior (see, for example, Frohlichand Oppenheimer 1996; McCabe and Smith 2003). As discussed insome detail in chapter 5, norms can be thought of as shared conceptsof what must, must not, or may be appropriate actions or outcomes inparticular types of situations. Individuals add positive or negative values
to objective payoffs invoked by emotions such as pride (when positive)
or shame (when negative). Norms such as those of trustworthiness,trust, and fairness will lead individuals to take actions in some situations—like those reported earlier—that are directly contrary to those predicted using a rational egoist assumption. While “foolish” according tothese models, if those following norms can identify others with similarnorms, they will actually earn more than those seeking their own shortrun selﬁsh objectives.15
The behavior of many participants can be thought of as partially basedon intrinsic preferences related to how they prefer to behave (and, obviously would like others to behave) and the kind of outcomes they wish
to see themselves and others obtain. Norm-following individuals take into
account other individuals’ interests as well as their own in the decisions
they make (Frey 1994, 1997a). These “nonselﬁsh” individuals, however,differ among themselves in terms of the extent to which they depart frompurely selﬁsh motivations and do not adopt an unchanging strategy wheninteracting with others in repeated situations. Further, individuals differ
in regard to how they interpret actions by others that reduce their own
payoffs. Some individuals can easily ﬁnd themselves enmeshed in everexpanding threat systems (Boulding 1963) whereby one bad deed cannot
rest unpunished. Reciprocity can have a very dark side when bads haveto repay bads over time. Unfortunately, “envy, vengeance and the desire
to dominate are not less intrinsically motivated than altruism, conscientiousness, and love. All of these motives contribute to immediate satisfaction rather than to achieving externally set goals” (Osterloh and Frey
2000, 540).
Turning to the bright side, psychological research provides evidence
that positive intrinsic motivation is increased when individuals feel that
their own self-determination or self-esteem is enhanced (Deci and Ryan
1985; Deci, Koestner, and Ryan 1999). This leads to the possibility that
intrinsic motivation can be “crowded out” in situations where individuals
do not perceive themselves to have sufﬁcient self-control over the actions
they take. The recent experiments by Fehr and Rockenbach (2003) and
Cardenas, Stranlund, and Willis (2000), discussed in chapter 3, provide
strong evidence for the crowding out of reciprocity by the imposition of
external sanctions. In a review of crowding-out theory, Frey and Jegen

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113(2001, 591) identify the conditions that affect the level of intrinsic motivation that individuals may feel:
1. External interventions crowd out intrinsic motivation if the individuals
affected perceive them to be controlling. In that case, both self-determinationand self-esteem suffer, and the individuals react by reducing their intrinsic motivation in the activity controlled.
2. External interventions crowd in intrinsic motivation if the individuals
concerned perceive it as supportive. In that case, self-esteem is fostered, andthe individuals feel that they are given more freedom to act, which enlarges
self-determination.

Just as individuals may have different mental models of the situations
they are in, they may differ in regard to their internal valuation patterns—
the extent they take others into account in the decisions they make andthe intrinsic valuation they may place on taking particular types of actions(e.g., being trustworthy) or reaching particular types of outcomes (e.g.,more equitable). Further, the form that normative and other-regarding
behavior takes “may be substantially context dependent. No ‘single’
model which explicates a particular set of values is likely to be adequate
to capture behavior in all contexts” (Frohlich, Oppenheimer, and Kurki
2004, 116).
Assumptions about Selection Processes
Reviewing the assumptions about information and valuation processes
has already opened a Pandora’s box of complexity. Examining alternative
assumptions about the selection processes that individuals use does nothelp reduce the complexity. One can make statements like: “Individuals
will try to do as well as they can given the information they obtain.” Or,“Individuals will use heuristics that have been proved to work in
the past.”
In Rules, Games, and Common-Pool Resources, for example, we examined the possibility that individuals in an experimental common-pool resource situation were using a heuristic that we called “measured reaction”(E. Ostrom, Gardner, and Walker 1994, 199). After engaging in face-toface discussions where subjects had reached a simple agreement regarding
what they all should do in future rounds, each subject had to make apersonal decision as to whether to keep to the agreement and what to do
if someone deviated from their agreement. Most subjects kept to theiragreement. When deviations did occur, most subjects ﬁrst reacted with amoderate increase in their own harvest rates.
A different strategy that scholars have posited that participants will use
in coping with this type of situation is called the “grim trigger.” A person

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using a grim-trigger strategy would react to any deviation from a verbal
agreement by withholding cooperation on every play for the rest of thegame. While subjects discussed such an option, they always rejected it. In
trying to understand how they were behaving, we posited that players
were reacting mildly (if at all) to a small deviation from an agreement. If
defections continued over time, subjects using a measured response slowly
changed from keeping the agreement toward actions consistent with the
Nash equilibrium (E. Ostrom, Gardner, and Walker 1994, 200). An analysis of the round-by-round decisions by subjects to the deviations that did
occur generated strong statistical support for the use of the measured
reaction heuristic (200–215). By following this heuristic, individuals
achieved a much higher payoff than predicted if they had used a maximization of expected net beneﬁts assumption.
That experience certainly increased the warrantability of the claim that
individuals use heuristics, in my view. And, the extensive research byGigerenzer and his research team on “fast and frugal heuristics” provides
substantial evidence across a diversity of situations (Gigerenzer, Todd,
and the ABC Research Group 1999; Gigerenzer and Selten 2001).
There is, however, a “serious and perhaps intractable induction problem
in inferring the structure of a black box from the structure of the behavior
it produces” (J. Anderson 1991, 471). In other words, it is hard to tellfrom behavior which of a variety of potential heuristics individuals arereally using.
In an effort to answer this question, scholars are using a variety ofingenious methods. Rieskamp and Hoffrage (2003), for example, deviseda series of experiments where they asked subjects to make decisions abouta business ﬁrm’s proﬁtability based on their best estimate obtained froma cursory examination of an array of cues. By observing the cues subjects
used—by opening a box on a computer screen—and how much time theyspent, Rieskamp and Hoffrage could assess the heuristics that the subject
used. As part of the experimental conditions, they also changed theamount of time allowed to subjects in order to observe how time constraints affect the selection process.
Rieskamp and Hoffrage identiﬁed the eight heuristics shown in table
4.1 as among those that subjects might use in this kind of a choice situation. As one can see from table 4.1, there is a rich array of potential
heuristics that subjects could potentially use. Under low time pressure,
Rieskamp and Hoffrage found that subjects tended to use noncompensatory heuristics—the PROS heuristic shown on table 4.1. The simple heuristic LEX was the best behavioral model to describe their behavior underhigh time pressure. They then conducted a Monte Carlo simulation toevaluate the performance of the eight heuristics listed on the table against
using a multiple regression method to make the same decision. TheyA N I M AT I N G I N S T I T U T I O N A L A N A LY S I S

115

TABLE 4.1
Description of various decision strategies
Strategy*

Description of strategies

LEX

The lexicographic strategy (Fishburn 1974) selects the alternativewith the highest value on the cue of the highest validity. If two or
more alternatives have the same highest cue value, then for thesealternatives the cue with the second highest validity is considered,and so on. LEX is the general form of the “Take the Best” heuristic
investigated in Gigerenzer and Goldstein (1996).

LEX-Semi

Lexicographic semiorder (Luce 1956) works like LEX, with theadditional assumption of a negligible difference (in the present
paper, this difference was set at 1). Pairs of alternatives with anegligible difference between the cue values are treated as notdiscriminative.

EBA

Elimination by aspects (Tversky 1972) eliminates all alternatives
that do not exceed a speciﬁed value on the ﬁrst cue examined (inthe present paper, this value was set at 2). If more than one alternative remains, another cue is selected. This procedure is repeateduntil only one alternative is left. Cues are selected in order of theirvalidity.**

Features

The features strategy (Alba and Marmorstein 1987) selects thealternative with the highest number of good features. A good feature is a cue value that exceeds a speciﬁed cutoff (in the present
paper, this cutoff value was set at 3).

ADD

The additive strategy calculates for each alternative the sum of
the cue values (multiplied by a unit weight of 1) and selects thealternative with the highest score.

LEX-ADD

The LEX-ADD strategy is a combination of two strategies. It
ﬁrst uses LEX-Semi to choose two alternatives as favorites, thenevaluates them by the ADD strategy and selects the one with thehighest sum.

PROS

The weighted pros strategy (Huber 1980) selects the alternativewith the highest sum of weighted “pros.” A cue that has a higher
value for one alternative than for the others is considered a pro forthis alternative. The weight of each pro is deﬁned by the validity ofthe particular cue.

WADD

The weighted additive strategy calculates for each alternative thesum of the cue values multiplied by the corresponding cue validates and selects the alternative with the highest score.

Source: Rieskamp and Hoffrage 2003, 50.
* Strategies occasionally do not end up with one single prediction; in this case, it was
assumed that the strategy would randomly choose between the remaining alternatives.
** In contrast to this deterministic selection, in the original formulation of the EBA heuristic the cues are selected with a probability proportional to their weights (Tversky 1972).

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found that the simple heuristics—particularly the LEX heuristic, which
actually requires the least information—did very well in comparison to
the “optimal” method for making this decision.16 In their effort to understand the use of heuristics, Jager, Janssen, and Viek (2001) used the dimensions of uncertainty and satisfaction to determine which heuristic would
be used. The more an individual is both satisﬁed and certain about theenvironment, the less likely they will put much effort into making a calculated choice.
Another puzzle in regard to the use of heuristics is: What is the effectof diverse heuristics on outcomes achieved? Gigerenzer and his colleagues(1999) have identiﬁed a number of heuristics that they have found toenable individuals and ﬁrms to make quick decisions and do very wellover time. Other researchers are somewhat more skeptical of the efﬁciency of heuristics—especially in highly volatile environments. Güth and
Neuefeind (2001), for example, explore the over-time efﬁciency of a heuristic that appears to be used in many consumer choices—a form of directional learning. This involves continuing or increasing an action if pastactions have generated good outcomes and decreasing if bad outcomesoccurred. In relatively stable environments, they found that the heuristic
helped individuals move toward an efﬁcient set of decisions. In a highly
volatile environment, however, learning from the use of this heuristic only
slowly improves achieved outcomes.

Variety and Complexity: An Asset or a Liability?
What I hope the reader gains from this brief overview of the broad set ofassumptions used by theorists to animate analyses of multiple situations
is a good sense that research in the behavioral social sciences is gradually
increasing our capabilities to understand and predict interactions and outcomes in a diversity of action situations. The large number of replicatedexperiments provide substantial evidence for theorists to use in testingout a diversity of assumptions about basic human behavior (see Bolton
and Ockenfels 2000; Fehr and Schmidt 1999; Frohlich and Oppenheimer
2001; Charness and Rabin 2003; Bowles 1998; Cox, Friedman, and Gjerstad 2004; Cox and Sadiraj 2004; Gintis 2000a; Rabin 1993; E. Ostrom
1998; Camerer 2003). What is also encouraging is that research in biological evolution (Maynard Smith and Szathmáry 1997; Kurzban 2003), cultural evolution (Boyd and Richerson 1985), and their interrelationships(Henrich 2004; Richerson and Boyd 2002; Sethi 1996) is helping to provide a better foundation for understanding how humans could have
evolved such a rich set of preferences in addition to the universal goal of
seeking to do well in regard to material payoffs.

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117The reaction of some scholars, however, to the growing richness of
behavioral theories of human decision making is to view the variety as
leading to messy complexity. Given that humans may be viewed as pursuing multiple values in diverse situations depending to some extent on theirprior life’s experiences, does it matter anymore which assumptions one
uses? Does theory underlie all of these models? Is it just a hodgepodge ofassumptions? Or, are there some major lessons to be learned?
My answers to these questions are: Yes, there is a broad underlying
theory of human behavior that is itself developing over time as a result of
the extensive empirical research and theoretical effort. Yes, it matters a
lot that we can make multiple assumptions about how participants in asituation make decisions. The variety of assumptions is an asset and nota liability when used to design research so as to test out the implicationsof one set of assumptions versus others (see McCabe and Smith 2003; Cox
2004). We are beginning to make some real headway in understanding the
behavior of the extremely complex animal called Homo sapiens. In thiseffort, we can learn a lot from engineers and how they draw on the generallaws of physics.
Engineers work with multiple types of motors that are used to propelvehicles. To predict energy use, length of service, fragility to impact, andother important outcomes, an engineer needs to know speciﬁc facts about
the situation (the kinds of roads or rail systems, their roughness and steepness, the amount of trafﬁc, etc.) and about the motor involved (its internal
composition, fuel needs, horsepower, etc). A skilled engineer is trained
to work with multiple types of animation devices—motors—that are allconsistent with underlying laws of physics. The underlying laws are too
broad and general to provide speciﬁc guidance as to what to expect inparticular settings. Engineers must design motors to perform well in speciﬁed contexts rather than in any and all contexts. Engineers use very detailed speciﬁcations. This does not mean, however, that the design of motors lacks a foundation in general physical laws.
Like engineers, social scientists need to recognize that to predict outcomes we must match the animating assumptions about the participants
to the structure of the situation or linked set of relevant situations we are
analyzing. While social scientists do not design the animators of action
situations, we must understand how humans tend to behave in diverse
situations. Our explanatory models of human behavior will differ when
we explain behavior in a repeated social dilemma among individuals who
have built reputations for trustworthiness and conditional cooperationas contrasted to total strangers who cannot even communicate with one
another. When the situation relates to private goods and is competitiveand stable; when it generates considerable information about its structure
and the actions of participants; and when participants voluntarily enter

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primarily to achieve material outcomes, animating the situation with rational egoists generates empirically supported propositions. Or, if the situation is extremely simple and lacks any context that provides a clue as towho else is involved and the appropriate norms to use, individuals tend
to rely primarily on what is best for themselves (Frohlich, Oppenheimer,
and Kurki 2004).
Unlike motors, however, the animators of social life adapt and changeover time. Humans do not have ﬁxed characteristics. What makes understanding human behavior so difﬁcult is exactly our capacity to try out
multiple norms, heuristics, or strategies, and to learn to use one set in one
situation while using different mixes in other situations. The reason wecan characterize participants as rational egoists in an open, competitivemarket is because of the institution, not that all of the participants are
narrowly selﬁsh! The same individuals who energetically pursue proﬁtmaximizing strategies from 8 A.M. to 5 P.M. every workday may also volunteer several evenings a month on neighborhood projects, contribute
substantial funds to diverse charities, regularly vote, and be known tofriends and coworkers as kind, considerate individuals who always domore than their share of any team project. These individuals are also likely
to have biases in the way the world is interpreted and a variety of otherhuman failings.
All of these aspects of humans are consistent with a theory of boundedly rational, fallible individuals who pursue multiple goals for themselves and others, adopt contextually relevant norms of behavior, andcan learn better strategies in a particular situation over time—particularly
if it generates accurate information about key variables. We need to draw
on and expand the basic work of Herbert Simon, Vincent Ostrom, Douglass North, Reinhard Selten, Bryan Jones, Oliver Williamson, and others
who have posited and continue to develop a general theory of bounded
rationality.
Like all creatures honed by millennia of evolutionary processes, humans do seek beneﬁcial outcomes for themselves. Information search iscostly, and the information-processing capabilities of human beings arelimited. Individuals, therefore, often must make choices based on incomplete knowledge of all possible alternatives and their likely outcomes.
With incomplete information and imperfect information-processing capabilities, all individuals may make mistakes in choosing strategies designed
to realize a set of goals (V. Ostrom 1986). Over time, however, individuals
can acquire a greater understanding of their situation and adopt strategies
or heuristics that result in higher returns. Bounded rationality, however,has focused mostly on the information condition related to how participants choose.

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119The theoretical challenge facing scholars today is developing an appropriate family of assumptions to make about the intrinsic values individuals place on actions and outcomes—particularly outcomes obtained byothers. Individuals adopt norms of behavior taught them by parents andothers in the situations in which they ﬁnd themselves. Which norms aresupported and become a strong inﬂuence affecting decisions is affectedby the history of experiences that individuals have (or have not) sharedand by the speciﬁc experiences they face in a particular decision situation. Humans tend to partition action situations into those where norms
are not taken seriously into account and those where norms matter (Harsanyi 1955).
Social scientists have to match their initial assumptions about the orientations of participants to the situation they are trying to understand andexplain. What kind of goods and services are involved, what rules, andwhat kind of community surrounds a particular situation? We have to
ask whether the situation is stable or changing, conveys substantial information about its structure and the behavior of participants, tends to invoke norms such as trust and reciprocity (or those of an eye for an eye),and allows participants to adapt more effective strategies over time?
The effort to develop better theory, specify testable propositions, and undertake carefully designed ﬁeld and experimental work is crucial to ourenterprise.
The two fundamental lessons from the vast empirical and theoreticalresearch of the last several decades are: ﬁrst, humans have complex motivations including narrow self-interest as well as norms of proper behavior
and other-regarding preferences; and second, institutions matter! To movebeyond these important lessons to better understand institutional diversity, we need to address three major issues: (1) How can we cut throughthe complexity to focus on problems that are ripe for further growth? (2)How can we include norms in our analysis without falling into the trapthat all that is needed is to assume that individuals learn and use norms?
and (3) How can we gain a better grasp of what we mean when we saythat institutions matter?

A Focus on Collective Action to Overcome Social Dilemmas
First, to cut through the complexity we need to concentrate on broad
areas of nonmarket situations. Market institutions work well when the
goods involved are private goods. Individuals trying to provide public
goods or sustain common-pool resources (as deﬁned in chapter 1) ﬁnd
themselves facing a variety of social dilemmas that are not easy to solve.
We are likely to make more progress if we do not try to develop a single

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model of human behavior that can be used to predict behavior in all market and nonmarket action situations. A more focused effort to explain
collective action in overcoming social dilemmas appears to have a higher
probability of success in the near future.
After all, traditional rational choice theory emerged from a consistenteffort of many scholars to develop a rigorous theory and models of humanchoice within one broad type of institutional arrangement—that of acompetitive market. The expansion of this theory to other types of action
situations did not occur until long after it had proved itself to be successful
in predicting outcomes in one type of institutional arrangement. It was
not until the inﬂuential work of Kenneth Arrow (1951), Anthony Downs(1957), Mancur Olson (1965), William Riker (1962), and James Buchanan and Gordon Tullock (1962) that scholars began to take seriously
the possibility of using rational choice theory to explain voting, legislative decisions, decisions within a hierarchy, and engagement in collective
action to overcome social dilemmas. And we have learned a lot fromthis effort to apply a systematic theory to explain behavior in nonmarket
situations.
Part of what we have learned is that highly competitive situations exist
outside of the market where the narrow theory of rational behavior generates useful explanations for important empirical phenomena (Shepsle andWeingast 1984, 1987; Tsebelis 2002). We have also learned that the predictions of zero contributions derived from this theory are not supported
in many social dilemmas. Explaining successful and unsuccessful efforts
to engage in collective action to overcome social dilemmas should be ahigh priority for institutional theorists.
Explaining the diversity of outcomes in social dilemma situations is apuzzle that is ripe for further development given the quality and quantityof relevant research. It is also an important question to pursue if one
presumes that humans are capable of developing, transmitting, and learning norms of trust, trustworthiness, reciprocity, and equity as well as
learning how to govern themselves. Without further progress in developing our theories and models of human valuation in social dilemma situations, those convinced that all human behavior can be explained using
rational egoist models will continue to recommend Leviathan-like remedies for overcoming all social dilemmas. Hopefully, much of what welearn from focusing on behavior in social dilemmas will be useful in otherpuzzling nonmarket situations.
In focusing on social dilemmas, we need to address how to focus on
the role of norms and other-regarding preferences. Simply explaining
puzzling ﬁndings post hoc, as “they must somehow share some norms,”
is not a satisfactory strategy in the long run. Focusing on norms andA N I M AT I N G I N S T I T U T I O N A L A N A LY S I S

121other-regarding preferences is not enough, however, to explain fully how
individuals do overcome social dilemmas. Rules are needed to back up
these norms (or counteract dangerously escalating negative reciprocity).
We then need to dig into the analysis of institutions so that we can understand how individuals adopt norms as well as rules to overcome social
dilemmas.
In the remainder of this chapter, we will focus on the role of norms
in overcoming social dilemmas—how to represent norms, why they areimportant, why norms alone are not sufﬁcient to cope with many collective-action problems, and how norms may evolve. Then, the rest of thisbook digs into the concept of institutions and tries to clarify the meaningof key concepts, the reason we need to develop a theoretically relevant
way of classifying rules, the way we identify classes of rules used in theﬁeld, and the manner in which rules may be used as tools to affect theorientations of participants over time.

Norms Fostering Collective Action
Some scholars are hesitant to use the concept of norms because a generally
accepted method for representing norms in formal models does not yet
exist. Sometimes norms are simply used as a casual explanation after observing behavior that is not consistent with that predicted by noncooperative game theory. Arguing that “Oh, they must have been using a normof reciprocity” as a post hoc explanation of puzzling behavior is not sufﬁcient in the long run for arriving at empirically warrantable theory.
Representing norms in formal theory and then positing testable propositions from these theories is not immensely difﬁcult. Many contemporary
theorists add one or more symbols to the payoff function they examine
in a formal game to represent the internal valuation that participants mayplace on outcomes that others may receive—other-regarding preferences—or on actions or outcomes to which an individual assigns an intrinsic value that differs from its extrinsic value (see Fehr and Gächter 1998;
Frey 1997a, 1997b; Bolton and Ockenfels 2000).
Sue Crawford and I tackled this problem in 1995 in our American Political Science Review article “A Grammar of Institutions,” which has beenrevised and updated as chapter 5 of this book. As we deﬁne them, norms
are prescriptions held by an individual that an action or outcome in asituation must, must not, or may be permitted. Norms can be represented
in formal analyses as a delta parameter that represents the intrinsic beneﬁts or costs of obeying a normative prescription in a particular situation.
The changes may occur as a result of intrinsic motivation such as pride

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Figure 4.3 The Snatch Game with norms.

when keeping a norm or guilt when breaking a norm.17 The delta parameter may also occur as a result of the action being observed by others leading to esteem for following a norm or shame for breaking it.
The Snatch Game—with Norms
As an example for the reader, it would be useful to illustrate how one
could explicitly introduce the concept of norms into a formal game—theSnatch Game—presented in chapter 2. Norms change the internal valuethat participants place on an action or outcome in a situation (see ﬁgure4.3). In the initial Snatch Game, participants did not possess norms
against snatching goods. Thus, their internal preference functions could
simply be represented as the value placed on the goods they received (oras a monotonic function thereof). Given this preference, Household 1
would predict that Household 2 would snatch any goods put out for exchange. Thus, Household 1 would not offer any goods in the ﬁrst place.
Let us now assume that Household 2 has a norm against snatchinggoods. The preference function for Household 2 could now be represented as:
u 2 = π2 − δb, where
π2 = payoff obtained by Household 2,− δb = decrease in the value of π2 for breaking the norm.
Now, if Household 1 has good reason to believe that Household 2 has anorm against snatching, and that the size of the norm is greater than thevalue of the goods that could be snatched, then Household 1 is likely to
put the goods out for exchange. Both Households are better off than theywould be simply keeping their own commodities.

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123

Whether the strength of the norm is sufﬁcient to motivate Household
2 to refrain from snatching the commodities depends on the size of thedelta parameter and not simply its presence. In this example, Household
2 would need to suffer a loss of more than ﬁve payoff units to offset what
could be obtained by snatching. If the two households were embedded in
a community with an effective observation and gossip network, so thatanyone who snatched goods offered for sale would be likely to be observed and shamed, the shame of snatching might be enough to lead
Household 2 to prefer an exchange rather than to snatch the goods.18 If
the intrinsic value of the norm is not high enough to compensate for the
potential gain of material payoffs in a Snatch Game, then the behavior
will be the same as in the earlier game. Thus, in addition to specifyingthat norms exist, the theorist needs to posit the relative size of the costs
or beneﬁts of following a norm. Norms can deﬁnitely change behavior
but may not do so. Whether norms have an impact on behavior depends
both on the strength of the norm and the context of the situation.
For norms against snatching goods offered for exchange to be effective
without any rule enforcement present, a community in which the households are located needs to be relatively stable. All households must teachchildren the prescription against snatching other people’s goods. All
households must share the knowledge that all the other households are
doing this. In other words, the presence, size, and sign of delta parametersmust become common knowledge over time. A small clique of households who obtain a positive internal reward from the daring act ofsnatching goods could unravel years of stability in the exchange ofagreements among the households.19 In most market economies, whererules are relatively well enforced, shared norms play an important complementary role in enabling participants to engage in a wide diversity of
economic transactions without relying entirely on external rule enforcers(Cooter and Ulen 1996).
The Problem of Heterogeneity
More puzzling than ﬁnding ways of representing norms in formal analyses
is coping with the wide heterogeneity of norms that individuals learn andthe internal strength that they attach to these norms. As illustrated by theexperiments discussed in chapter 3, a substantial proportion of participants in social dilemmas is observed to take actions that are more cooperative than predicted for rational egoists. One must stress, however, that
not all of the participants appear to have intrinsic delta parametersattached to the actions of trusting others, reciprocating trust, cooperating
to solve collective-action problems, or sanctioning others who are not

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cooperative. Nor can we assume a uniform “delta” that everyone who is
not a rational egoist would share.
Different participants have their own orientations and adopt diverse
strategies in the same situation. In other words, we need to assume heterogeneity of preferences. The classic model of noncooperative game theory
now becomes a special case for modeling situations where one assumes
that individuals do not attach any internal value to following norms or
to the payoffs received by others. Further, one can expect some individuals
to behave in most situations in a manner consistent with this model.
Many new models have been posed in an effort to devise another general model of human behavior (see Rabin 1993; Ito, Saijo, and Une 1995;
Chan et al. 1997; Levin 1998; Fehr and Gächter 2000a; Bolton and Ockenfels 2000; Gintis 2000a; Casari and Plott 2003). None of these generalmodels are yet sufﬁciently well supported by experimental and ﬁeld datathat we can just substitute a new general model for the old classical general model. What is exciting, however, is that scholars are developing
careful experimental designs precisely to examine how these competingmodels fare in critical experiments (see Cox 2004; Cox and Sadiraj 2004;
Cox, Friedman, and Gjerstad 2004).
Many reasons exist for a concern that all of the explanations for cooperative behavior can be placed on a new model of individual behavior.
Fundamentally, one has to know key information about the situation before one can specify the kind of values participants are likely to adopt. As
discussed above in relation to the Snatch Game with norms, the norms
that individuals may adopt in general may or may not be strong enoughto overcome a temptation to break a norm in a particular situation. Further, for norms such as those leading an individual to be a conditional
cooperator to generate positive returns, some arrangement for clustering
conditional cooperators together is needed. Studies that look at niches,or spatial relationships among participants, often show that conditional
reciprocity can lead to cooperation to overcome social dilemmas (Laland,
Odling-Smee, and Feldman 2000).
A key requisite for successful cooperation is that conditional cooperators must be able to ﬁnd one another—due to either institutional or
spatial connections. If too many rational egoists surround conditional
cooperators, cooperation can just collapse. It is also the case that somenorms are potentially destructive rather than constructive (Boulding1963). McGinnis (forthcoming) demonstrates for us how individuals engaged in threat systems can ﬁnd themselves in ever deepening conﬂict
situations.
What is important about recognizing that not all individuals in all situations act like rational egoists is that we can begin to take institutions very
seriously. As Colin Camerer (2003, 117) captured the current situation:

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125“Institutional arrangements can be understood as responding to a worldin which there are some sociopaths and some saints, but mostly regularfolks who are capable of both kinds of behavior.” Thus, institutions areamong the tools that fallible humans use to change incentives to enable
fallible humans to overcome social dilemmas. Sadly, however, institutions
can also exacerbate bad outcomes as well as ameliorate them. We needto recognize that not only are humans complex systems; so are the structures they build. Thus, we need to really dig in and examine what we meanby institutions and how they can be used to overcome social dilemmas—a
task that we will begin in chapter 5 and continue through the rest of thevolume. Before we turn to this task, however, we need to examine thequestion of how human norms could survive in settings where rational
egoists are initially a large part of a population.

Emergence and Survival of Norms in Evolutionary Processes
While evolutionary theory has been used for years to explain why only
rational egoists can survive in a competitive process, recent developmentsin evolutionary theory are coming to a different set of conclusions (Maynard Smith and Harper 2003; Marcus 2004; Richerson, Boyd, and Paciotti 2002). Contemporary evolutionary theories provide useful ways ofmodeling the emergence and survival of multiple strategies in a population of participants. In a strict evolutionary model, individuals inherit
strategies and do not change strategies in their lifetime. Those carrying
the more successful strategies for a particular environment reproduce ata higher rate (Axelrod 1986). After many interactions, the more successful
strategies come to prominence in the population.20
Human evolution occurred mostly during the long Pleistocene era thatlasted for about 3 million years to about 10,000 years ago. During thisera, humans roamed the earth in small bands of hunter-gatherers who
were dependent on each other for mutual protection, sharing food, andproviding for the young. Survival was dependent not only on aggressively
seeking individual returns but also on solving many day-to-day collectiveaction problems. Those of our ancestors who solved these problems
most effectively and learned how to recognize who was deceitful andwho was a trustworthy reciprocator had a selective advantage over those
who did not (see Barkow, Cosmides, and Tooby 1992; Ben-Ner and Putterman 2000a; Brown and Moore 2002). Humans have acquired wellhoned skills at face recognition and strong abilities to detect cheating.
Research provides evidence that humans keep rough internal accounts—
both in regard to goodwill (McCabe and Smith 2003) and threats (Boulding 1963).21

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Evolutionary psychologists who study the cognitive structure of thehuman brain speculate that humans do not develop general analyticalskills that are then applied to a variety of speciﬁc problems. Humans are
not terribly skilled at general logical problem solving (as any scholar who
has taught probability theory to undergraduates can attest). Rather, thehuman brain appears to have evolved a domain-speciﬁc, human-reasoning architecture (Clark and Karmiloff-Smith 1991). For example, humans
appear to use a different approach to reasoning about deontic relationships—what is forbidden, obligated, or permitted—as contrasted toreasoning about what is true and false. When reasoning about deontic
relationships, humans tend to check for violations or cheaters (Manktelow and Over 1991). When reasoning about whether empirical relationships are true, they tend to use a conﬁrmation strategy (Oaksford andChater 1994). This deontic effect in human reasoning has repeatedly
been detected even in children as young as three years old, and is not
associated with overall intelligence or the educational level of the subject(Cummins 1996).
Recent studies have also examined how strategies are transmitted via
diverse cultural processes and thus can evolve rapidly (Richerson andBoyd 2002; Henrich 2004; Sussman and Chapman 2004). Cultural transmission is largely enhanced by language. While evidence is mounting thatsome animals have gained simple languages (Kaminski, Call, and Fisher
2004), humans have acquired language skills involving thousands ofwords that can be combined, given the rules of a grammar, into an inﬁnite
number of sentences conveying both speciﬁc information about a situation as well as general conceptual and normative information (Bloom
2000). The acquisition of human language provides “a second system of‘heredity’” (Maynard Smith and Harper 2003, 140). This second systemof heredity links the generations and is able to support rapid culturalchange. “With language, genetic change ceases to be the main basis ofchange: history begins” (140).
Developments in evolutionary theory and supporting empirical research provide strong support for the assumption that modern humans
have inherited a propensity to learn rules and norms similar to our inherited propensity to learn grammatical rules (Pinker 1994). In their chapter
on the acquisition of language, Maynard Smith and Szathmáry (1997)
stress the formal similarity between an “action grammar” and a “language grammar.” Children begin to learn strategies of constructing sentences and undertaking rule-ordered actions in the world at about thesame time in their development. Which prescriptions are learned, however, varies from one culture to another, across families, over time, andwith exposure to diverse social norms expressed within various types of

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127

situations. In most societies, however, the norm of reciprocity—that one
should return cooperative actions of others that beneﬁt self, with similar
efforts to help others—appears to be present to some extent related to
speciﬁc activities, locations, and relationships among individuals. The
evidence that individuals do learn norms of reciprocity—including a normof punishing others who do not reciprocate cooperation as well as punishing those who do not punish others—is steadily mounting (Bowles andGintis 2004).
The Indirect Evolutionary Approach to Adaptation
through Experience
Recent work on an indirect evolutionary approach to the study of humanbehavior offers a rigorous theoretical approach for understanding how
preferences—including those associated with social norms—may evolveor adapt in shorter time horizons than posited in biological evolutionary
theory (Güth and Yaari 1992; Güth 1995). In an indirect evolutionarymodel, players receive objective payoffs (measuring economic or reproductive success), but make decisions based on the transformation of thesematerial rewards into their own intrinsic values. Those who place a value
on reciprocity, fairness, and being trustworthy can be thought of as adding a subjective delta parameter to actions (of themselves or others) thatare consistent or not consistent with their norms. This approach allows
individuals to start with a predisposition to act in a certain way, but it
allows preferences to adapt within a generation (rather than over many
generations) given the objective payoffs received depending on the interpretation of the evolutionary dynamics.22
Social dilemmas are particularly useful for exploring the indirect evolutionary approach. An indirect evolutionary approach explains how a mixture of norm-users and rational egoists would emerge in settings where
standard rational choice theory assumes the presence of rational egoists
alone. In this approach, social norms may lead individuals to behave differently in the same objective situation depending on how strongly theyvalue conformance with (or deviance from) a norm. Rational egoists canbe thought of as having intrinsic payoffs that are the same as objective
payoffs since they do not value the social norm of reciprocity.
Conditional cooperators (to add one additional type of player) would
be modeled as placing a positive delta parameter on (1) contributing to agroup outcome in a Commons Dilemma or (2) trusting others when in
the position of an Investor or on being trustworthy when in the position ofa Trustee in the Trust Game. By their behavior and resulting interaction,
however, different types of players are likely to gain differential objective

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returns. In a Trust game where players are chosen from a population that
initially contains some proportion of rational egoists and conditional cooperators, the level of information about player types affects the relative
proportion of rational egoists and conditional cooperators over time.
With complete information regarding the presence and size of a delta parameter, conditional cooperators playing a trustworthy strategy will more
frequently receive the higher extrinsic payoff, while rational egoists willconsistently receive a lower payoff since others will not trust them.
In indirect evolutionary theory, only trustworthy participants survive
in an indeﬁnitely repeated social dilemma characterized by complete information about the past actions of all subjects (Güth and Kliemt 1998,
386). If one thinks of this theory as a learning process, those who wereless successful would tend to learn the intrinsic values or delta parameters of those who had achieved higher material rewards (Börgers andSarin 1997).23 Where a player’s type is common knowledge, rational egoists would not survive. Conditional cooperators interacting with otherknown conditional cooperators will obtain higher payoffs and come todominance.
Full and accurate information about all players’ intrinsic preference,
however, is a strong assumption and difﬁcult to achieve. When participants
try to limit who else is involved in overcoming dilemmas to others wholive in a community and who can establish a reputation for being trustworthy, they may be able to create the conditions needed for the evolution of
conditional cooperators to become the dominant type of player.
If there is no information about player preferences for a relatively large
population, preferences are predicted to evolve so that only rational egoists survive.24 If information about the proportion of a population thatare trustworthy is known, and the ﬁrst player has no information about
the trustworthiness of a speciﬁc second player, Güth and Kliemt (1998)
derive a prediction that ﬁrst players will trust second players as long as
the expected return of meeting trustworthy players and receiving thehigher payoff exceeds the expected payoff obtained when neither player
trusts the other. In such a setting, however, the share of the population
held by the norm-using types is bound to decline. On the other hand, if
there is a noisy signal about a player’s type that is at least more accurate
than a random signal, trustworthy types can survive as a substantial proportion of the population. Noisy signals may result from seeing one another, face-to-face communication, and various internal accounting mechanisms that humans have evolved to monitor each other’s behavior. As
we discuss later in this volume, the institutions that humans craft to copewith diverse problems can affect the kind of information that individuals
obtain about each other’s past actions.

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129Evidence Testing the Indirect Evolutionary Theory
Indirect evolutionary theory is able to explain how a mixture of contingent cooperators and rational egoists would emerge in settings where traditional game theory predicts that only rational egoists should prevail.
Given the recent development of this approach, direct tests of this theory
are not yet extensive. From the viewpoint of an indirect evolutionary process, participants in a collective-action problem would begin an interaction with differential, intrinsic preferences over outcomes due to theirinitial predispositions toward norms such as reciprocity and trust. Participants would learn about the likely behavior of others and shift their behavior in light of the experience and the objective payoffs they have received. Several recent experiments provide evidence of these kinds ofcontingent behaviors and behavioral shifts.25
In a one-shot, sequential, double-blind, Prisoner’s Dilemma experiment, for example, the players were asked to rank their preferences overthe ﬁnal outcomes after they had made their own choice, but before theyknew their partner’s decision. Forty percent of a pool of 136 subjects
ranked the cooperative outcome (C,C) higher than the outcome if theydefect while the other cooperates (D,C), and 27 percent were indifferent
between these outcomes, even though their individual monetary payoff
was higher in the latter outcome (Ahn, Ostrom, and Walker 2003). Thisﬁnding conﬁrms that not all players enter a collective-action situation as
pure forward-looking rational egoists who make decisions based solely
on individual outcomes.26 Some bring with them a set of norms and values
that can support cooperation.
On the other hand, preferences based on these norms can be altered bybad experiences. One set of 72 subjects played 12 rounds of a ﬁnitely
repeated Prisoner’s Dilemma game where we randomly matched partners
before each round. Rates of cooperation were very low. Many players
experienced multiple instances where partners declined to cooperate(Ahn, Ostrom, and Walker 2003). In light of these unfortunate experiences, only 19 percent of the respondents now ranked (C,C) above (D,C)
while 17 percent were indifferent (ibid.). In this uncooperative setting,the norms supporting cooperation and reciprocity were diminished byexperience, but not eliminated.27
In another version of the Prisoner’s Dilemma game, Cain (1998) ﬁrst
had players participate in a “Dictator Game”—in which one player divides a sum of money, and the other player must accept the division, whatever it is—and then a Prisoner’s Dilemma game. “Stingy players,” deﬁned
as those who retained at least 70 percent of their endowment in the earlier
Dictator Game, tended to predict that all players would defect in the Pris-

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oner’s Dilemma game. “Nice players,” deﬁned as those who gave away
at least 30 percent of their endowment, tended to predict that other niceplayers would cooperate and stingy players would defect. Before playing
the Prisoner’s Dilemma game, players were told whether their opponent
had been “stingy” or “nice” in the dictator game. Nice players chose
cooperation in the Prisoner’s Dilemma game 69 percent of the time when
they were paired with other nice players and 39 percent of the time when
they were paired with stingy players.
Finally, interesting experimental (as well as ﬁeld) evidence has accumulated that external imposed rules tend to “crowd out” endogenous cooperative behavior (see Frey 1994). For example, consider some paradoxical ﬁndings of Frohlich and Oppenheimer (1996) from a Prisoner’s
Dilemma game. One set of players—the control group—played a regular
Prisoner’s Dilemma game (some with communication and some without). A second set of players used an externally imposed, incentivecompatible mechanism designed to enhance cooperative choices. In theﬁrst phase of the experiment, the second set of participants gained higher
monetary returns than those in the control group, as expected. In thesecond phase of the experiment, both groups played a regular Prisoner’s
Dilemma game. To the surprise of the experimenters, a higher level ofcooperation occurred in the control groups that played the regular Prisoner’s Dilemma in both phases, especially for those who communicated ona face-to-face basis. The greater cooperation that had occurred due tothe exogenously created incentive-compatible mechanism appeared to betransient. The authors speculated that removing the external mechanism
undermined subsequent cooperation. Having an effective rule imposedon them, even though it induced them to improve their outcomes, appeared to leave the players worse off once it was removed, in comparison
with the players in the control group who relied entirely on face-to-facecommunication.
The studies by Fehr and Rockenbach (2003) and Cardenas, Stranlund,and Willis (2000), discussed in chapter 3, have conﬁrmed the notion thatexternal rules and monitoring can crowd out cooperative behavior. Thesestudies typically ﬁnd that a social norm, especially in a setting where there
is communication between the parties, can work as well or nearly as wellat generating cooperative behavior as an externally imposed set of rules
and system of monitoring and sanctioning. Moreover, norms seem to havea certain staying power in encouraging a growth of the desire for cooperative behavior over time, while cooperation that is primarily there due toexternally imposed and enforced rules can disappear very quickly. Finally,
the worst of all worlds may be one where external authorities impose
rules but are able to achieve only weak monitoring and sanctioning. In

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131a world of strong external monitoring and sanctioning, cooperation isenforced without any need for internal norms to develop. In a world of no
external rules or monitoring, norms can evolve to support cooperation. In
an in-between case, a low level of external monitoring discourages theformation of social norms, while also making it attractive for some players to deceive and defect, given the low risk of being caught.

Conclusion
The institutional analyst at the beginning of the twenty-ﬁrst century facesa challenging task in animating analyses of expected behavior in a wide
diversity of situations. To do a detailed analysis of expected behavior, theanalyst must ﬁrst ask some crucial questions about the action situation.
First, does the situation generate substantial information about the structure of the situation itself? Second, do individuals voluntarily enter thesituation in order to compete for valued, objective outcomes such as income, commodities, winning an election, beating others in a sports tournament? Third, do those who engage in this type of situation place primary value on obtaining the immediate objective outcomes of thesituation? If the answers to these three questions lead the analyst to conclude that the situation is an open competitive situation focused primarilyon objective payoffs, then using the assumptions of a rational egoist to
animate the model is the best strategy. When all of these factors are present, the theorist is likely to make predictions about behavior and outcomes that are supported by empirical data.
On the other hand, if the situation is a social dilemma rather than anopen competitive process, I would urge the analyst to animate an initial
analysis assuming that participants hold multiple value orientations anduse strategies ranging from those used by rational egoists to those used by
players who value trust, reciprocity, and equity very highly. The relative
proportion of each type of player that would survive after repetition of
the situation would depend heavily on multiple aspects of the structureof the situation and the initial distribution of types as affected by thebiophysical world, the rules in use, and the community in which it isembedded.
If the dilemma involved many individuals located in diverse settings
around the world who have little opportunity to communicate and shareno common rules—like an open-access ocean ﬁshery or the global atmosphere—then the best predictions and explanations of behavior would bederived from assuming that most participants are rational egoists. Conditional cooperators can do little without an appropriate institutional struc-

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CHAPTER FOUR

ture to support their norms. Those who would like to cooperate withothers ﬁnd themselves unable to do anything but follow the dominant
strategy. Recent evidence related to the massive depletion of ﬁsheries in
the open oceans conﬁrms this prediction (Myers and Worm 2003).
This points to the importance of larger institutions that enable participants in social dilemma situations to have sufﬁcient autonomy thatthey can change the rules that affect their ongoing situations. If individuals face a social dilemma situation repeatedly and they have the autonomy
to change the rules that structure it so as to enhance the probability
that the proportion of conditional cooperators and willing punishers cangrow over time, many individuals have crafted ingenious institutions that
help them reach mutually productive rather than mutually unproductive
outcomes.
Given the importance of institutions in affecting the structure of a situation and the population of types of individuals who are most likely tocome to prominence in a particular type of situation, we now need totackle a major focus of this book—the rules that humans can use as tools
to fashion the action situations they engage in repeatedly. The biophysicalworld and the attributes of a community work together with rules toconstitute action situations that enhance or reduce the likelihood of individuals reaching better outcomes. Rules are the tools, however, that fallible humans can use to try to change situations to achieve better outcomes.
In chapter 5, we will focus on how rules, norms, and strategies sharesome attributes and differ on others. A high level of confusion exists inthe literature as to what these concepts mean. It is hard to develop better
policies to overcome collective-action problems when scholars disagree
on fundamental terms such as these. In chapter 6, we will then focus on
the question of why we should classify rules themselves. In chapter 7, we
will develop a systematic language for identifying and classifying rules.
In chapter 8, we will illustrate many of the rules actually used in ﬁeld
settings and develop a theoretical calculus for how appropriators from acommon-pool resource decide to change the rules affecting their interactions. Chapter 9 will then focus on what types of resource governanceregimes tend to be robust in a changing world.
As scholars and as policy analysts, we need to learn the artisanship ofworking with rules so as to improve how situations operate over time.
Human beings are neither all-knowing saints nor devilish knaves. The
institutions they grow up in—families, schools, playgrounds, neighborhoods—differentially reward or punish them over time so that intrinsic
and extrinsic motivations are learned and developed over time. The situations they ﬁnd themselves facing as adults in the workplace and theircommunity also affect which norms they use and the outcomes they reach.

A N I M AT I N G I N S T I T U T I O N A L A N A LY S I S

133When individuals learn the artisanship of crafting rules, they can experiment and learn to create more productive outcomes (as well as participants) over time. Learning to craft rules that attract and encourage individuals who share norms of reciprocity and trustworthiness, or who learn
them over time, is a fundamental skill needed in all democratic societies.

Part II
FOCUSING ON RULES

FiveA Grammar of Institutions
S U E C R AW F O R D A N D E L I N O R O S T R O M

CHAPTER 4 focuses on the challenge that social scientists face in animating
analyses of social situations so as to generate understandings and predictions that are then tested.1 The need to study holons within holons makes
this a difﬁcult process. Until recently, political economists had at least one
unchanging constant in their analyses—the model of the individual used.
One paid primary attention to the focal action situation and then asked
what participants modeled as rational egoists would do in this situation.
The general strategy recommended in this book is similar but more difﬁcult. First, one needs to examine the structure of the situation. Then one
asks how boundedly rational, fallible but adaptive individuals would interact in that situation over time.
In this chapter, we return to the task of analyzing the structure of situations so as to better model the interaction of actors in those situations.
Earlier chapters establish the basic components of diverse action situations. Here we turn to analysis of speciﬁc kinds of institutional statementsthat shape incentives in action situations. Speciﬁcally, we elucidate a syntax that illustrates the similarities and differences between shared strategies, norms, and rules. These concepts are not clearly distinguished inmuch contemporary social science literature. In this chapter, we show
why there is so much confusion and provide tools to clarify the distinct
inﬂuences of each kind of institutional statement on human interaction
in diverse action situations. As in other chapters, we illustrate how thegrammar works for institutional analysis that uses various research
approaches.

Parsing Institutional Statements
We view the concept of an institutional statement as a broad term encompassing three types of statements—rules, norms, and shared strategies.
These statements describe opportunities and constraints that create expectations about other actors’ behavior. In other words, an “institutional
statement” encompasses a broad set of shared linguistic constraints and138

CHAPTER FIVE

opportunities that prescribe, permit, or advise actions or outcomes forparticipants in an action situation. We stress the shared nature of theseconcepts. Many written statements have the form of a rule (or a norm or
strategy) but are not known to participants and do not affect behavior.
Such statements are considered rules-in-form rather than rules-in-use(Sproule-Jones 1993). We concentrate our attention here on rules-in-use.
As the Institutional Analysis and Development (IAD) framework stresses,
these shared linguistic constraints interact with inﬂuences from the biophysical world and those attributes of the community not easily understood as institutional statements (for example, ethnic heterogeneity) toshape the structure of the action situation.
The grammar tool allows analysts to distinguish more systematicallybetween institutional statements that are best understood as attributes of
the community (strategies and norms) and those that are best understoodas rules. This distinction recognizes that rules operate in such a distinct
way in action situations that analysts need to know when an institutional
statement is a rule and when it is not.
We focus on rules for two basic reasons. First, institutional analysts
conducting policy analysis are frequently asked to analyze the impact ofsome change in rules—either a change that has already occurred or the
possible impacts of a proposed change. Second, institutional analysts
working to craft solutions to negative outcomes in an action situation
recognize that changes in the rules may be easier or more stable thanattempts to change the situation through changes in the biophysical world
or attributes of the community. The grammar of institutions provides atool to help those crafting institutions to ensure that the institutional statements that they craft are indeed rules, so that they can better predict theinﬂuence that the institutional statement will have on the action situation
and better tap into the institutional strengths that rules provide.
We assume with von Wright (1968) that rules can be expressed using
two basic linguistic forms. One is a generative form: “let there be an X.”
Rules that create positions (e.g., voter, judge, mayor) or organized bodies(e.g., the U.S. Senate) are generative rules. The other is a regulatory form,
which has a more complex syntax elucidated in this chapter. We return
to a brief discussion of generative rules in chapter 7 when we examine thechallenge of classifying and using rules.
In this chapter, we posit that regulatory rules are one grammatical stepaway from norms and two steps away from strategies. Our explicit recognition of the differences between rules, norms, and strategies clariﬁes analytical questions about the similarities among these concepts and about
the existence and origin of institutional rules. Using this grammar, one
can ask clear questions about when strategies or norms evolve into rules
and why.

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139

The grammar of institutions thus provides a syntax for analyzing andexpressing institutional statements that can be used to distinguish systematically among rules, norms, and shared strategies. It draws on and speaks
to discussions of rules, norms, and institutions from a wide range of thesocial sciences. But ﬁrst, a disclaimer: we do not assume that institutional
statements affecting behavior can always be articulated easily and fully
by participants. Knowledge of institutional statements often becomes habituated and part of the tacit knowledge of a community (Epstein 2001).
Moreover, we do not assume that all individuals recognize the existenceof an institutional grammar and explicitly use it to formulate institutional
statements. The grammar of institutions is a logical tool that complements
other ways of representing institutional phenomena. It is a useful tool
for summarizing and analyzing the content of institutional statements,
distinguishing between types of institutional statements, and studying theformation and evolution of institutional statements.

The Syntax of a Grammar of Institutions
In this section, we identify the syntax and components of a grammar of
regulatory rules that can be used in the analysis of institutional statements. To ease the discussion of the syntax, we provide ﬁve examples oftypical institutional statements. We refer to these examples throughout
this chapter.
1. All male, U.S. citizens over eighteen years of age must register with theSelective Service by ﬁlling out a form at the U.S. Post Ofﬁce or else face arrestfor evading registration.
2. All senators may move to amend a bill after a bill has been introduced, or
else the senator attempting to forbid another senator from taking this action
by calling him or her out of order will be called out of order or ignored.
3. All villagers must not let their animals trample the irrigation channels, orelse the villager who owns the livestock will have to pay a ﬁne.
4. If you use the microwave, you must clean up your own mess!
5. The person who places a phone call, calls back when the call gets disconnected.

Let us now turn to the syntax that we will use to analyze these and otherexamples.
The general syntax of this grammar includes ﬁve components: [ATTRIBUTE], [DEONTIC], [AIM], [CONDITIONS], and [OR ELSE] where,
A ATTRIBUTES is a holder2 for any value of a participant-level variable that
distinguishes to whom the institutional statement applies. Examples include

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CHAPTER FIVE

eighteen years of age, female, college educated, 1-year experience, or a speciﬁc
position, such as employee or chairperson.
D DEONTIC is a holder for the three modal verbs analyzed by von Wright.
These are “may” (permitted), “must” (obliged), and “must not” (forbidden).
I AIM is a holder that describes particular actions or outcomes in the action
situation to which the deontic is assigned. An AIM may include a formula specifying an amount of action or outcome or a description of a process for an action.
C CONDITIONS is a holder for those variables that deﬁne when and wherean action or outcome is permitted, obligatory, or forbidden.
O OR ELSE is a holder for the institutionally assigned consequence for not
following a rule.

There are several advantages of using this ADICO syntax. First, elements from the syntax make up all three types of institutional statements.
Second, elements from the syntax also distinguish among these three typesof statements. Rules contain all ﬁve components (ADICO). Norms contain four components (ADIC) and shared strategies contain three components (AIC). Third, the syntax provides a format for writing institutional
statements in a consistent manner. Regardless of how institutional statements are expressed in natural language, they can be rewritten or summarized in the ADICO format.
All regulative rules can be written as: [ATTRIBUTES] [DEONTIC][AIM] [CONDITIONS] [OR ELSE]; all norms can be written as: [ATTRIBUTES] [DEONTIC] [AIM] [CONDITIONS]; and all shared strategies
can be written as: [ATTRIBUTES] [AIM] [CONDITIONS]. By writing
the statements in a consistent manner, we can then better compare theinstitutional statements in use in a variety of settings.3 Finally, since theaddition or subtraction of components switches institutional statementsfrom one type to another, the syntax provides a tool for analyzing theevolution of institutional statements from one type to another.

The Syntax Components
We now turn to a discussion of the components of the ADICO syntax.
Understanding the components helps analysts develop common methodsof distinguishing between rules, norms, and strategies to cumulate knowledge for key questions such as: What difference does it make if the prescription is a rule or a norm? and What difference does it make if an
institutional statement is a shared strategy or a norm? The syntax does
not eliminate all gray areas. One gray area of particular concern to manyscholars since the original publication of the syntax has been the precise

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141

point at which a norm can be said to have evolved into a rule. The discussion of the OR ELSE component here clariﬁes this distinction between
norms and rules to address some of these concerns.
ATTRIBUTESAll institutional statements apply to a subset of participants in an action
situation. The subset can range from one participant to all participants.
A set of ATTRIBUTES establishes the subset of the participants affectedby a particular statement. If individuals make up the participants in an
action situation, the ATTRIBUTES will be individual-level values. Individual-level ATTRIBUTES include values assigned to variables such asage, residence, sex, citizenship, and position.4 When the participants governed by a set of institutions are corporate actors, rather than individuals,
the ATTRIBUTES refer to organizational variables such as size of membership, geographic location, or who owns the residuals.
In the ﬁrst example, the relevant ATTRIBUTES are: male, citizen of the
U.S., and over eighteen years old. Some version of the fourth example, themicrowave cleanup statement, can often be found taped to the door of acommunity microwave. The beginning of that statement, “If you use themicrowave,” could be parsed as the ATTRIBUTE of “microwave users.”
In the last example, the ATTRIBUTE is the caller who placed the call. The
other examples list no speciﬁc attribute. When no speciﬁc attribute islisted, the default value for the ATTRIBUTE component is: all membersof the group.5 This means that the ATTRIBUTE component always has
something in it, even when a speciﬁc attribute is not contained in the statement. Thus, the second example applies to all senators in a legislature, andthe third example applies to all villagers in a particular village.
Within a particular action situation, the ATTRIBUTE component ofinstitutional statements maps the authority or prescription of an institutional statement to particular positions or to all positions. In order forthis assignment to work, then, there must be other institutional statementsthat assign participants into positions (discussed as boundary rules inchapter 7). When analyzing an institutional statement with speciﬁc positions in the ATTRIBUTES component, it is important to extract the rules
assigning participants into positions in order to understand how a speciﬁc
institutional statement with that position actually works in the action
situation.
DEONTICThe DEONTIC component draws on the modal operations used in
deontic logic to distinguish prescriptive from nonprescriptive statements

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CHAPTER FIVE

(see von Wright 1951; Hilpinen 1971, 1981). The complete set of DEONTIC operators, D, consists of permitted P, obliged O, and forbidden F.
The logical relationships among the DEONTIC operators include thefollowing:
D = P ∪ O ∪ F.
F ∩ P = Ø; O ∩ P = O; and F ∩ O = Ø.
If O then P.
Institutional statements use the operative phrases may, must/should, andmust not/should not to assign these operators to actions and outcomes.
“Should” and “must” are both commonly used to oblige a person to act.
Similarly, “must not” and “should not” both forbid. For the sake of simplicity, we use “must” and “must not” throughout this chapter in nearly
all examples. However, the deontic terms can be used equally well for“should” statements. Generally, in everyday language, “must” obligatessomeone more strongly than “should,” and “must not” forbids someone
more strongly than “should not.” Later in the chapter we describe howdelta parameters allow more precision in the weight of the Oblige or Forbid and thus can be used to distinguish between “should” and “must”
when needed in analysis.
The statement that all members may vote assigns the DEONTIC permitted, P, to the action of voting. The assignment of a DEONTIC operator
to an action [ai] may be represented as [D] [ai], where D stands for P,
O, or F. Similarly, [D] [oi] represents the assignment of a deontic to an
outcome.
DEONTIC OPERATORS RELATE TO THE PHYSICALLY POSSIBLE

Regulation refers to statements about what is presumed to be physicallypossible. A person cannot logically be required to undertake an actionthat is physically impossible for anyone to do. As expressed by von Wrightin regard to actions: “The notion of ability or can do . . . signiﬁes ability
so to say in its ‘naked form,’ subject only to the restrictions imposed by
the laws of nature (including the limits of man’s innate capabilities ofgrowth and learning). Within this broad concept of ability (can do) one
can distinguish a narrower concept. When, in this narrower sense, we sayof an agent that he can do or that it is possible for him to do a certain
thing, we mean that his doing of this thing will not violate a set of rules(norms) or conditions such as, for example, the rules of a certain legal
order or moral code” (von Wright 1966, 33). The DEONTIC operators
are related to each of the components of an action situation. In the above

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143

paragraph, the DEONTIC operators are related to actions. In scope rules,
the operators refer to outcome variables. In information rules, the operators refer to communication channels.
DEONTIC OPERATORS ARE INTERDEFINABLE

The three DEONTIC operators are interdeﬁnable (von Wright 1968, 143).
In other words, if one of them is taken as a primitive, or the initial starting
point, the other two can be deﬁned in terms of this primitive. For example,
let us use permission P as a primitive. If we are referring to a possible
action [ai], then [P] [ai] would be read: One is permitted to do ai, or one
may do ai. The statement that an act is forbidden [F][ai] can be restatedusing P as the primitive as [∼P][ai]. In other words, when an action isforbidden, one is not permitted to do [ai]. On the other hand, if the negation of an action [∼ai] is forbidden, one is obliged to take the action. The
statement that an act must be done, [O][ai], can be deﬁned as [∼P][∼ai]. If
an action is obligatory, one is not permitted to not do [ai]. Alternatively,
we could use F as the primitive. Then, P can be deﬁned as [∼F][ai] and O
can be deﬁned as [F][∼ai]. With O as the primitive, P can be deﬁned as[∼O][ai or ∼ai], while F can be deﬁned as [O][∼ai]. This same interdeﬁnability exists for prescriptions that refer to outcomes instead of actions. Any
prescriptions with a DEONTIC assigned to some OUTCOME, oi, can berestated using either of the other two DEONTIC operators.
The ﬁrst four examples listed above can be restated using F as the primitive yielding the following:
1. U.S. citizens with [ATTRIBUTES] [F] [∼a1] [CONDITIONS] [OR ELSE]
All male, U.S. citizens, over eighteen years of age are forbidden not to registerwith the Selective Service by ﬁlling out a form at the U.S. Post Ofﬁce, or else
face arrest for evading the draft.
2. Senators [All] [∼F] [a2] [CONDITIONS] [OR ELSE]
All senators are not forbidden to move an amendment to a bill after a bill has
been introduced, or else the senator attempting to forbid another senator fromtaking this action by calling him or her out of order will be called out of order
or ignored.
3. Villagers [All] [F] [o3] [CONDITIONS] [OR ELSE]
All villagers are forbidden to let their animals trample the irrigation channels,
or else the villager who owns the livestock will have to pay a ﬁne.
4. [Microwave users] [F] [∼a4] [CONDITIONS]
Microwave users are forbidden to not clean up their own mess.

Notice that the ﬁfth example stated earlier has no DEONTIC. The ﬁfth
statement does not state a “must” and “must not” or a “may.” This means

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CHAPTER FIVE

that the strategy of the person, who initiated a telephone call being the one
who “calls back” if the call is interrupted, does not contain a DEONTIC
component. It is a strategy rather than a norm or a rule.
MAKING SENSE OF PERMISSION RULES

The meaning of the DEONTICS Obliged (“must”) and Forbidden (“mustnot”) ﬁt well into most conceptions of normative statements. The meaning of Permitted (“may”) is more perplexing for many scholars. For example, Susan Shimanoff (1980, 44) concludes that “it is incongruous to talkof rules prescribing behavior which is merely permitted.” Her conclusionbegs the question that we address here—namely, what does it mean for arule or norm to permit an action?
Statements that assign permission (P) to an action inﬂuence the structure of action situations in at least three ways. Most often, rules andnorms that assign permission deﬁne constraints on permission by establishing limited CONDITIONS in which permission exists. In the UnitedStates, consumers above the age of twenty-one may purchase alcohol inmost states. In some states, Sunday liquor laws constrain this permission
further and allow consumers above the age of twenty-one to purchasealcohol only Monday through Saturday. In effect, these rules establish thesettings in which permission exists and thus forbid the action in circumstances that do not meet the stated CONDITIONS. A rule that grants
permission to cut trees with a permit from a forestry agency implies thatthe absence of a permit renders tree cutting forbidden.
Less often, assigning a “may” to an action is the equivalent of “constituting” that action (Searle 1969). For example, a statement that an individual may vote in an election creates an action—voting—that did notexist before. The rule assigning the permitted action constitutes the action. These permission rules, then, add new action options to the action
situation that did not exist before.
Finally, some permission rules change the action situation by granting
participants with particular attributes a right to take an action. For example, voting in some political systems is a legal right. As Commons ([1924]
1968) warns, however, the permission to vote does not operate as an effective legal right unless at least one other rule assigns a duty to someone
else. Others, who have a duty to recognize a person’s right, are the ones
who are forbidden or required to take actions or affect outcomes.6 The
Voting Rights Act of 1965 included rules that assigned the United States
the duty of ensuring that African Americans were permitted to vote. Thatlegislation converted a simple permission to vote into a right to vote.
John R. Commons ([1924] 1968), drawing upon the work of Wesley
Hohfeld (1964), further develops the meaning of permission in the form

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A GRAMMAR OF INSTITUTIONS

Party B

Party A
Correlatives
Right
L
i
m
i
t
s

Duty
ns
pa tiobi
ta litie
i
s
m
Li
Ca

Exposure

Liberty

Figure 5.1 Authorized relationships: authority to act. Source: V. Ostrom and
E. Ostrom 1999, 46.

of rights by clarifying the relationships between rights and duty. He argues
that a right depends on a correlative obligation or duty on the part ofothers to act in accordance with the right being asserted. Rights are subject to limits. Limits bearing upon any right deﬁne the area of decision
making where a claimant stands exposed. Thus, Commons has deﬁned
the limit of a right as an exposure. The correlative of an exposure is beyond the limit of a duty. A person who is no longer under duty is at liberty
to act. Thus, the correlative of an exposure is a liberty. When these arerepresented in a boxed space, as in ﬁgure 5.1, the correlatives indicate thereciprocal interdependencies among two different legal parties or sets oflegal parties. The limits are applied to each particular party. Taken together, the diagonal or reciprocal relationship represented by right andliberty establish the capabilities assigned to both parties. The duty andexposure establish the limitations assigned to their respective decisionmaking capabilities. The correlates in ﬁgure 5.1 result from rule conﬁgurations, not from a single permission rule. One would need to go beyond
the parsing of single rules to deﬁne the full set of rights, liberties, duties,
and exposures of actors within an action situation.7
If a permission rule exists in an action situation, with no rule that assigns a duty to some position to ensure that those permitted to act areable to do so, then the permission rule simply establishes that one (ormore) participants are allowed to take this action without having a rightto take it. Others are exposed to this liberty. The U.S. Constitution permits
people who meet certain attributes to run for the ofﬁce of president. Laborlaws permit individuals over a set age to be legal employees. These rules

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CHAPTER FIVE

establish eligibility. They do not confer “rights” because another rule does
not assign someone the duty to allow anyone meeting those attributes
to be president. Nor do U.S. laws assign duties to anyone to ensure full
employment of those of legal working age. Although these permission
institutions do not confer rights, because they do not assign a duty toanyone to ensure that the actions can be carried out, these permission
institutions do create liberties. They constrain efforts by others to forbida participant to take the permitted AIM. Notice that the rule in the second
example contains an OR ELSE that indicates what happens when senators
attempt to treat the permitted action as forbidden (the offending senatoris called out of order or ignored).8 Even without an established right, theassignment of permission inﬂuences the opportunities and constraints inthe action situation.
REPRESENTING DEONTICS IN FORMAL ANALYSIS

What does including a DEONTIC in an institutional statement imply for
the way a formal game is analyzed? As initially discussed in chapter 4, it
implies that the payoff structure for individuals in situations where theyshare common understandings related to prescriptions will differ fromsimilar situations in which players merely accept a shared understandingof prudent, rational action. Our way to capture this change in the payoffs
is to add parameters, called delta parameters, to the payoffs related toconforming or not conforming to a shared prescription. In simple terms,
delta parameters are added to an individual’s payoff to represent the perceived costs and rewards of obeying (o) or breaking (b) a prescription.
The delta parameters can thus be deﬁned as:∆ = δ o + δ b, where∆ = the sum of all delta parametersδ o = the change in expected payoffs from obeying a prescription
δ b = the change in expected payoffs from breaking a prescription.
One can further divide these rewards and costs into those that arise fromexternal versus strictly internal sources of valuation. Thus:δ o = δ oe + δ oi and δ b = δ be + δ bi, wheree = changes in expected payoffs originating from external sourcesi = changes in expected payoffs originating from internal sources.
The distinction between external and internal sources of delta parameters is similar to that of Coleman (1987) between “internalized norms”
and “externally sanctioned norms.” Delta parameters originating fromexternal sources are a way to capture the beneﬁts and costs of establishinga particular reputation (see Kreps 1990). The delta parameters originating

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147

from internal sources can be thought of as the guilt or shame felt when
breaking a prescription and the pride or “warm glow” felt when following a prescription—particularly if it is costly to follow in a particularsituation (Andreoni 1988; Ledyard 1995). “People who have developed
an internal sanctioning system, for example, feel guilt and shame at behaving in a deviant way” (Bicchieri 1997, 19).
The analyst may not wish to focus on all four parameters in any particular analysis. Three of the four delta parameters could be assigned a zero
value in a game-theoretic analysis involving a norm or a rule. In order toanalyze the impact of a DEONTIC on expected outcomes of a game,however, at least one of the delta parameters must have a nonzero value.
In situations where it is reasonable to assume that all players who break
the prescription feel the same cost, the delta parameters can be modeled
as if they were the same for all players and as if their magnitude is public
information. As discussed in chapter 4, the theorist can also model players
as having different orientations (Harsanyi 1967–68) for situations whereactors react differentially to breaking prescriptions. One player can perceive the costs of breaking a prescription (δ bi or δ be) to be high while
another perceives costs (δ bi or δ be) to be low. Coleman’s (1988) zealot, forexample, is a player with high external deltas for obeying norms (a highpositive δ oe).
If an action is forbidden by a norm and an individual engages in that
action, we expect that player to experience some type of cost represented
by at least one component of delta. If norms indicate that an AIM is
permitted, then we expect that others, who treat that action as if it wereforbidden, will experience some cost. In other words, norms or rules thatforbid or require some AIM will be reﬂected in a cost parameter to theindividuals to whom the prescription applies. Agreements that establish
permission for an action place the cost parameter on others. Others may
experience a cost if they try to obstruct an individual when a shared norm
indicates that the individual is permitted to take that action.9
The existence of a DEONTIC implies the presence of additional information that individuals use in developing their expectations about others’
behavior and thus their own best response. If players share a norm, thepayoff structure looks different from the payoff structure for a similar
situation in which the players do not share a norm. The payoffs may evenchange enough so that the predicted outcome of the game differs entirely
from that predicted by a similar game that includes no delta parameters.
Uncertainty about whether other actors, who have accepted certain
norms, are present in a situation may be sufﬁcient grounds for changing
the behavior of players. Kreps and colleagues (1982) have analyzed repeated Prisoner’s Dilemma games where information asymmetries exist
among players concerning the probability that other players will play tit-

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CHAPTER FIVE

for-tat.10 In such games, players who are “perfectly rational” (i.e., theplayers’ payoff functions have a zero value for delta components associated with playing tit-for-tat) will adopt behavior consistent with the norm
for most of the game.
AIM
The AIM is the speciﬁc description of a working part in an action situation to which an institutional statement refers. The description can include information about a process (ﬁlling out a form at the U.S. Post
Ofﬁce) or a formula (pay $10 per hour worked). In order for an institutional statement to inﬂuence behavior, the AIM must be physically possible and its negation [,ai] must also be physically possible. An individual
cannot logically be required to undertake a physically impossible action,and prescribing an action can only inﬂuence behavior if it is physicallypossible to not do that action. The capability of voting implies the capability of not voting. Voting for candidate A implies the option of not voting
for candidate A. The AIM sometimes speciﬁes states of affairs in theworld or an outcome instead of an action. Outcomes, like actions, mustalso be possible and avoidable to be parts of a well-formed institutional
statement. Moreover, any particular outcome [oi] implies the existence of
its negation [,oi].
In the ﬁrst example given earlier, the AIM is the action (a1) of registering
for the Selective Service by ﬁlling out a form at the U.S. Post Ofﬁce andthe DEONTIC operator required, O, is assigned to the action for all individuals with the ATTRIBUTES listed in the rule. In the second example,
the AIM is the action of offering a motion to amend a bill and the DEONTIC operator is P, or permitted for all senators. The AIM in the fourth
example also includes an action—cleaning the microwave. The third example assigns the DEONTIC F, or forbidden, to the outcome of livestock
damage. The AIM of the rule does not specify actions that an irrigatormust or may take. The AIM speciﬁes only the forbidden outcome. Villagers may select any actions that are not forbidden by another rule to keep
their livestock from damaging the irrigation channel. Finally, in the ﬁfth
example of a strategy, the AIM, a5, is the action of calling back.
The AIM often supplies the focus for formal and empirical studies.
Once the set of actions or outcomes is selected, the next step is developing
the analysis of institutional statements related to those AIMs. Scholars
decide to study the impact of institutions on behavior for some subset ofactions or outcomes. Studies of agenda setting and voting institutions, forexample, focus on those actions related to setting agendas and voting
within a particular forum. In studies of voting rules, it becomes particularly important to specify the formulas in the voting rules that indicate

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149

when the collective body has permission to change the status quo (Shepsle
1989). The AIM component for a majority rule will be different from the
AIM component for a consensus voting rule. Analyses of the impact ofdifferent voting rules will often focus on the impact of different formulas
here. In common-pool resource action situations, the AIM formulas are
particularly important. Formulas in the AIM component of institutional
statements indicate how much a participant may extract (e.g., three units
per acre of land) and how much participants must contribute (e.g., twodays per acre of irrigated land). Analysis of the effectiveness of rules, then,often turns to analysis of the effectiveness and costs of different formulas(Yandle and Dewees 2003).
CONDITIONSCONDITIONS indicate the set of variables that deﬁne when and where
an institutional statement applies. For example, the CONDITIONS for astatement might indicate when a statement applies, such as during certain
weather conditions, at a set time, or at a particular step in some process.
Likewise, the CONDITIONS might indicate where a statement applies,
such as a particular jurisdictional area. If an institutional statement does
not specify particular variables, the default value for the CONDITION isat all times and in all places covered by that rule, norm, or strategy. Thus,
like the ATTRIBUTE, the CONDITION component always has somevalue in it even when the institutional statement fails to overtly specify it.
The CONDITIONS component in the second example indicates when
the prescription applies. After a bill has been introduced, the prescriptionof the second rule applies. Thus, any senators may move to amend a billafter it has been introduced. The strategy in the ﬁfth example applies
when a telephone call is disconnected. The ﬁrst, third, and fourth examples do not specify speciﬁc CONDITIONS; therefore, we assume that therules apply for U.S. citizens, members of the village, and microwave users
under all circumstances.
OR ELSEThe ﬁnal component of our institutional syntax is the consequence that an
institutional statement assigns to detected noncompliance with the othercomponents of that statement. In some cases, the OR ELSE speciﬁes a
range of possible punishments if a rule is not followed. Individuals in thecommunity know that if they violate a rule, they face the probability thata sanction in a speciﬁed range will be applied and that others in a similar
situation face the same range. Only rules include an OR ELSE. This com-

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CHAPTER FIVE

ponent, consequently, plays a crucial role in discerning what a rule is andhow rules differ from other institutional statements.
Sanctions for breaking a rule are a common type of OR ELSE, but the
OR ELSE may take other forms. The OR ELSE might also shift the DEONTIC assigned to some other action. For example, a violator might be forbidden to vote or engage in some other action that would otherwise be permitted. The OR ELSE might also shift the DEONTIC assigned to some activity
for an individual from permitted to obligatory (P to O). The violator might
be required to allocate resources to a public jurisdiction (i.e., pay a ﬁne),or another actor might be required to check on the violator. Those actions
might be permitted (P) under all other CONDITIONS, but obligatory (O)when the CONDITION of a violation of the rule is met.
Although the OR ELSE often refers to physical punishments, the OR
ELSE may also involve institutional actions, such as taking away a position
or refusing to accept an amendment as legal. For example, one of the rules
governing the amendment process may state that legislators with [ATTRIBUTES] [must] [take a particular action] [when voting for an amendment][OR ELSE—the amendment fails].
Three qualiﬁcations must be met for an OR ELSE to exist. First, theconsequence stated in the OR ELSE must be the result of collective action.
A collective decision must have been made in a relevant collective-choice
arena to determine the consequence.11 Second, the threat in the OR ELSE
component of a rule must be backed by another rule or norm that changes
the DEONTIC assigned to some AIM, for at least one actor, under the
CONDITION that individuals fail to follow the rule. Often the actions
threatened in the OR ELSE are forbidden under most CONDITIONS(e.g., imposing a ﬁne, incarcerating a citizen, or taking someone’s livestock and putting them in a village pen). The prescription backing the OR
ELSE makes these actions permitted or required in the CONDITION thatsomeone breaks a rule. The shift in the deontic is not always from F to P
or O. The OR ELSE might involve forbidding some action that is usuallypermitted; a shift from P to F. For example, the OR ELSE might forbid agovernment agency from providing a tax incentive to a corporation on
the condition that the corporation violated the speciﬁc provisions authorizing the incentives in the tax code (see Lederman 2003).
Third, in order for an OR ELSE to exist, a prescription must affect theconstraints and opportunities facing an actor or actors with the responsibility of monitoring the conformance of others. Although the actors whomonitor frequently sanction nonconforming actors, they may only reportnonconformance to someone else responsible for sanctioning. We do notconsider government sponsorship or government backing to be a necessary condition for a statement to include an OR ELSE. Many self-orga-

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151

nized, communal, or private organizations develop rules that include (1) asanction, (2) backed by another rule or norm that changes the DEONTIC
assigned to some AIM for at least one actor if individuals fail to follow
the rule, and (3) a norm or rule (a sanctioning prescription) that affects
the constraints and opportunities facing an actor or actors to take theresponsibility to monitor the conformance of others to the prescription(a monitoring prescription).
Turning again to the examples of institutional statements listed above,the ﬁrst three examples appear to contain an OR ELSE. Of course, wewould need to check to be sure that there are rules or norms regarding
monitoring and sanctioning backing the stated OR ELSE before we would
be sure that the stated sanction fully qualiﬁes as an OR ELSE. For example, the potential punishment for villagers who let their livestock trample
the irrigation channels qualiﬁes as an OR ELSE only when rules or norms
accepted in that village prescribe others to monitor and to employ the
sanctions deﬁned in the OR ELSE. Without the establishment of positions
with the authority for monitoring and sanctioning, phrases that containthe words “or else” fail to constitute an OR ELSE that distinguishes aninstitutional statement as a rule as deﬁned herein.
Institutional statements with content in the OR ELSE slot, then, areinstitutional statements that add information to the action situation about
what will happen if a participant violates the prescription. The Senate
example of a rule indicates that participants who violate the prescription
will be called out of order. A participant in the Senate action situation,
then, knows that if he or she chooses to violate the rule that he or she will
have the consequence of being called out of order or at the very least
having his or her attempts ignored. In effect, the institutional consequenceof breaking this rule is the removal of legal standing. The senator has nolegal standing to restrict another member from moving an amendment inthis simple rule.
Compare the information about the consequences in the Senate action
situation to that in the microwave example. The sign on the microwave
does not provide any speciﬁc information about what will happen to aparticipant who chooses to ignore the norm and leave a puddle of soupat the bottom of the microwave. Ofﬁce workers seeing the sign on the
microwave have only information about their own internal costs or beneﬁts of following the norm and their beliefs about how others in the ofﬁce
are likely to respond when they open the door and see the crusty soup
spill there.
The OR ELSE component of a rule is frequently linked to a CONDITIONS component that speciﬁes the number of times that a rule has been
violated (Dana 2001). The range of sanctions is likely to be lowest when

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CHAPTER FIVE

someone has committed their ﬁrst offense against a speciﬁc rule. A different rule, specifying a more stringent range of sanctions, will then be applicable if that individual has committed a second, or a third, or more offenses. Many rules against drunk driving use this form of graduatedsanctions and increase the sanction for this offense substantially for second or third offenders.
The content of the OR ELSE affects the very nature of a rule. Rowe(1989) discusses the difference between a speed limit law with minor sanctions and a speed limit law with the death penalty as the sanction. The
prescription is the same. The severity of the sanction in the OR ELSE is
the only difference. Yet a speeding law with a death penalty is quite adifferent rule than a speeding law with a minor ﬁne. Given the same levelof enforcement, the type of OR ELSE involved may make a substantial
difference on the behavior of participants.12 This example is not meant as
an argument for severe sanctions. Rather, it points out that the OR ELSE
is a fundamental part of rules and that the implications of the sanctions
that are assigned to prescriptions are important for the analysis of institutional arrangements. This is particularly true for studies of rule compliance and stability.
Use of the grammar thus far suggests that the OR ELSE component of
the grammar has been the most challenging for scholars to apply, but
the discussions of determining whether this component exists have led to
productive wrestling about the important theoretical differences between
rules and norms in action situations.13 The presence of an OR ELSE, then,is absolutely crucial to the effective use of the grammar for analysis ofrules. Identifying when this component exists distinguishes when an institutional statement includes “rule information.” These statements, then,inﬂuence the action situation so that one would describe the situation as“rule-governed” or “shaped by a rule.” Thus, the OR ELSE clariﬁes thedifference between institutional statements that inﬂuence the action situation as norms or strategies alone and those that can inﬂuence the action
situation as rules.

Applying the Grammar
No one will want to spend time learning the intricacies of the grammar
of institutions developed herein without a sense that it is useful for atleast some purposes. In the remainder of this chapter, we illustrate initial
applications of the grammar to three broad endeavors: (1) game-theoretical analyses, (2) the synthesis of diverse theoretical approaches to thestudy of rules and norms, and (3) learning about norms and rules in empir-

A GRAMMAR OF INSTITUTIONS

153

ical research. The remaining chapters of the book will dig into these applications still further.
Using the Grammar in Game-Theoretical Analyses
Our ﬁrst application demonstrates the grammar for a very simple game.
By using a game familiar to most contemporary scholars, we illustrate theanalytical usefulness of the working parts of the grammar. The concept ofa strategy, as developed in the institutional grammar, is the same as theconcept of strategy used in contemporary game theory. Thus, a game-theoretic analysis of a situation in which there are no norms or rules would
not differ in any way from current usage. The Snatch Game, ﬁrst presented
in chapter 2, is such an analysis. To incorporate the syntax into formal
analyses of behavior, the payoffs for actions governed by norms alone needto include delta parameters that capture the DEONTIC (as illustrated inchapter 4), and for rules they must also specify the institutionally assigned
consequence deﬁned in the OR ELSE (as we now illustrate).
If the enforcing players are brought into the analysis, the enforcing
player would have a delta parameter assigned to the action of “not sanctioning,” since there is a norm or rule that prescribes sanctioning. If the
OR ELSE is backed by a rule, then we expect the payoffs for sanctioning
or not sanctioning to include delta parameters and a variable representing
the cost of the sanction deﬁned in the OR ELSE of the sanctioning rule.
A game that includes the enforcement players must also specify the monitoring institution inﬂuences on the monitor. Once a monitoring norm orrule comes into play, the delta parameters and possibly the OR ELSE
parameters become part of the monitors’ payoff formulas.
If it is costly to monitor the actions of others and/or to impose sanctions
on them, those assigned these tasks may not be motivated to undertakethese assignments unless (1) the monitor or sanctioner face some probability of themselves being sanctioned for not monitoring and/or sanctioning,14 (2) social pressure to monitor or sanction is large and is salientto the monitor and sanctioner (large δ oe and δ be), (3) the monitor or sanctioner hold some strong moral commitment to their responsibilities (large
δ oi and δ bi), or (4) the payment schemes for the monitor or sanctioners
create prudent rewards high enough to offset the costs. When norms backan OR ELSE, monitoring and enforcement rests solely on the value of thenormative delta parameters and on the payment schemes for the monitor
and sanctioner (i.e., Are the monitors and sanctioners paid? If so, is payment a set fee regardless of the number of defectors they catch, based on
the number of defectors caught and punished, or some mixture of a set
fee and a commission?).

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CHAPTER FIVECOLLECTIVE-ACTION PROBLEMS

To illustrate applications of the syntax to ongoing research, our discussionof applications uses shared strategies, norms, and rules related to an abstract social dilemma problem rather than the ﬁve examples discussed
earlier in this chapter or the Commons Dilemma discussed in chapter 3.
The scholarly discourse about social dilemmas and their solution throughcollective action has involved terms such as common understanding,shared beliefs, scripts, norms, rules, procedures, institutions, informal
rules, informal institutions, conventions, internal solutions, external solutions, as well as a wide diversity of highly technical terms related to particular solution theories.
Collective-action problems can be represented by many different gamestructures (see discussion in Taylor 1987; E. Ostrom, Gardner, andWalker 1994; Gintis 2000b). Because almost all social scientists know the
Prisoner’s Dilemma (PD) game well, however, we can more easily jump
into existing debates and rely on extensive earlier work. We start with asimple two-person Prisoner’s Dilemma game and use the ADICO format
to illustrate differences in the research issues, the game structures, andthe predicted outcomes that arise from (1) changed expectations of otherplayers’ behavior only (AIC statements); (2) changed normative views of
the appropriate actions to be taken or the adoption of norms (ADIC statements); and (3) changes in the rules (ADICO statements). Table 5.1 summarizes the institutional and payoff characteristics of four games basedon a two-person Prisoner’s Dilemma situation. The ﬁrst game is the base
two-person Prisoner’s Dilemma game. The shared strategies game adds a
set of shared strategies that equate to the grim trigger strategy. The norms
game adds a cooperating norm to the base situation. The rules game adds
a cooperating rule, a monitoring norm, and a sanctioning norm to thebase Prisoner’s Dilemma game. These four examples represent only one
way to add the ADICO statements to a two-person Prisoner’s Dilemma
game. We do not develop a new solution theory. Rather, our effort is toillustrate how an application of the ADICO syntax distinguishes between
three structural adjustments to social dilemma situations.
In ﬁgure 5.2, we present both the extensive and the normal forms of
the base two-person PD game. We make only the assumption that thepayoffs are related in the following way: 1 > c > d > 0. Both players are
better off choosing D, no matter what the other player chooses, so D is thedominant strategy for both players. The Commons Dilemma discussed inchapter 3 is frequently represented as an N-person Prisoner’s Dilemma
game under particular cost and beneﬁt assumptions.
The game-theoretic solution to this game, if played only once, is forboth players to choose D and receive d instead of the more desirable c155

A GRAMMAR OF INSTITUTIONS

Table 5.1
Game summaries
Institutional statements
Base game
NONE (physical world)

Shared strategies game
AIC statements[All players] [ ] [C] [ﬁrst round] [ ][All players] [ ] [C] [if all C in previous
round] [ ][All players] [ ] [D] [all rounds after a D] [ ]Norms game
ADIC statement[P1* and P2] [must] [C] [always] [ ]Rules game
ADICO statement[P1 and P2] [must] [C] [always] [f]ADIC statements[P3] [must] [monitor] [always] [ ][P4] [must] [impose f on defector] [whenP3 reports a D] [ ]Source: Crawford and Ostrom 2000, 134.
* P1 refers to player 1, and so on.
** (P3 ⇒ M) indicates that player 3 chooses M.

Payoffs
Player 1 or 2
C = c if other C= 0 if other D
D = 1 if other C= d if other D
Player 1 or 2
C = c + t (c) if other C= 0 + t (d) if other D
D = 1 + t (d) if other C= d + t (d) if other D
Player 1 or 2
C = base game payoffs + δ oi + δ oe if P3 ⇒ M**= base game payoffs + δ oi if P3 ⇒ ∼M
D = base game payoffs − δ bi − δ be if P3 ⇒ M= base game payoffs − δ bi if P3 ⇒ ∼M
Player 3
M = E if (P1 and P2) ⇒ C= R − E if (P1 or P2) ⇒ D∼M = 0Players 1 and 2
C = norm game payoffs
D = norm game payoffs + f if (P3 ⇒ M) and(P4 ⇒ S)
= norm game payoffs if (P3 ⇒ ∼M) or(P4 ⇒ ∼S)
Player 3
M = norm game payoffs + δ om
∼M = norm game payoffs − δ bm
Player 4
Only plays if P3 ⇒ M
S = δ os − Es
∼S = − δ bs

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CHAPTER FIVE

c
c

0
1

C

d
d

1
0

D

C

2

D
2

C

D
1

BASE GAMECD
C c,c 0,1
D 1,0 d,dFigure 5.2 Base game. Source: Crawford and Ostrom 2000, 143.

that they could have received if they had both chosen C. In the Commons
Dilemma game, this would mean that subjects would overinvest rather
than investing at the optimal level. Even if repeated for a ﬁnite numberof times, the solution is for both players always to choose D.
SHARED STRATEGIES

Predictions that individuals will select C rather than D in a PD game basedon shared strategies rely upon changes in players’ expectations about eachother’s future behavior. In order to incorporate those expectations into
formal analysis, we use an indeﬁnitely repeated version of the base game.
By making the game repeated, we can include future expected payoffs aspart of a player’s calculation at any one round. It is now logically possiblefor individuals to adopt shared strategies involving plans of action to cooperate in the ﬁrst round, and to defect for all periods thereafter, if theother player defects in a prior round.
Figure 5.3 illustrates the extensive form of an indeﬁnitely repeatedgame with a set of shared strategies that create the famous grim trigger:“all players cooperate in each round of the game or else all other players
will defect for the rest of the game.” Both players cooperating in every

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A GRAMMAR OF INSTITUTIONS

c+t(c)
c+t(c)Cd+t(d)
d+t(d)

1+t(d)
0+t(d)

0+t(d)
1+t(d)

D

C

2

D
2

C

D
1

t = number of expected future roundst( ) = expectation of payoffs from future rounds of the gameInstitutional Statements
Shared Strategies[All Players] [C] [First Round][All Players] [C] [All Rounds in which All Players Play C in the Previous Round][All Players] [D] [All Rounds after a D]

Figure 5.3 Repeated game with shared strategies. Source: Crawford and Ostrom
2000, 144.

round is the predicted result if and only if c + t(c) > 1 + t(d) where t is the
expected number of future rounds, assuming that players do not discountfuture payoffs.15 Whether all C is the predicted outcome to this gamedepends on the relative size of 0, d, and t, all features of the physicalworld.
Shared strategies, even the crude trigger strategy, are institutional statements. They require shared understanding. If all players do not consider it
prudent to defect for all rounds after someone initially defects, the trigger
strategy is not shared and will not work. Little empirical evidence existsthat individuals share a belief in the prudence of a grim trigger (E. Ostrom,
Gardner, and Walker 1994). Herein lies the frailty of the grim trigger asa resolution of PD situations.
The words “or else” in the grim trigger shared strategy might cause one
to wonder whether it is a rule. The shared strategy with a grim trigger is
not a norm or a rule, using the ADICO syntax because there is no DEON-

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CHAPTER FIVE

TIC and no OR ELSE here. If the advice to cooperate to avoid a trigger
response is not discussed in terms of obligation nor backed by monitoringand sanctioning institutions, the massive defection that is threatened by
the trigger can only be a prudent response of a player’s defection.
The shared strategy with a grim trigger would be a rule, using theADICO syntax, if we were to assume that there were a prescription toplay the trigger strategy that is in turn backed by another rule or normthat changes the DEONTIC assigned to some AIM for at least one actor,
and there is an additional institutional statement that affects the constraints and opportunities facing an actor to monitor the conformance ofothers. The institutional statements that could back a trigger rule might
be a sanctioning rule, such as: “all other players must defect for the restof the game when one player defects in any of the rounds OR ELSE theother players face the probability of a further sanction” and a monitoring
norm that “all players must monitor all other players.” Notice that the
sanctioning rule changes the DEONTIC assigned to C from obligatory toforbidden in the CONDITION of a defection in the prior round.
NORMS

Predictions that individuals will select C rather than D in a PD base gamebased on norms rely upon changes in players’ payoffs because of the addition of at least one delta parameter to the players’ payoffs. Figure 5.4
illustrates a game in which the base PD structure has been modiﬁed by
the addition of delta parameters in the payoffs for players 1 and 2. Inorder to make the discussion more applicable to situations with more than
two players, we add a third player, a Monitor who chooses to monitor(M) or not to monitor (< M) and assume that external reinforcements forobeying or breaking a norm occur only when the Monitor reports thedefection. In the simple two-person game, this assumption is usually notnecessary; players 1 and 2 know whether the other player cooperated bysimply looking at their own payoffs. As soon as the number of players in
a PD is larger than two, however, identifying who defects is no longer
trivial. To make the game more applicable to common’s situations withmultiple actors, we “blind” the actors here and assume that defection
becomes common knowledge only when the Monitor does his or her job.
Consequently, the negative external effects of breaking the norm only kickin when the Monitor chooses to monitor.
In this game, the Monitor is motivated solely by prudential rewards
associated with discovering defection and not by any normative motives(delta parameters). In other words, we do not assume a monitoring norm.
The absence of a monitoring norm is appropriate here because the game159

A GRAMMAR OF INSTITUTIONS

c+*oi+*oe
c+*oi+*oe
!E

c+*oi
c+*oi
0

0+*oi+*oe
1!*bi!*be
R!E

0+*oi
1!*bi
0

1!*bi!*be0+*oi+*oe
R!E

1!*bi
0+*oi
0

d!*bi!*bed!*bi!*be
R!E

~M

M

~M

M

~M

M

M
3

3
C

3

D

~M
3

C

2

d!*bi
d!*bi
0

D
2

C

D
1

Payoffs to Monitor (Player 3)

DeltasR = reward for detecting defector
E = expense of monitoring*oe = external changes in payoffs from obeying prescription*oi = internal changes in payoffs from obeying prescription*be = external changes in payoffs from breaking prescription*bi = internal changes in payoffs from breaking prescription

Institutional Statements
Norm[Players 1 & 2] [Must] [C] [Always]

Figure 5.4 Game with a norm and monitoring. Source: Crawford and Ostrom
2000, 145.

represents the addition of a cooperating norm only, not a cooperating
rule. Freelance reporters are an example of this type of monitor. They
receive payment for detecting and reporting nonconformance with accepted norms. Rewards include fees for stories accepted and increased
probabilities of receiving prizes for good reporting. It is, of course, possible to illustrate the addition of norms without a player who is assigned a
specialized role as a Monitor by simply assuming that the existing institutional statements allow players to monitor each other (Weissing and Ostrom 1991a, 1993). To do this, however, one needs to model a sequential
structure, which introduces more complexity than we desire in this initial
application.
In the game represented by ﬁgure 5.4, predictions about players’ strategies depend on the relationships among the original payoffs in the basegame, the added delta parameters, and the beneﬁts the Monitor receives
for reporting nonconformance. This game has many equilibria. Assumingthat all of the delta parameters are symmetric (players 1 and 2 have thesame values for each delta parameter) and that the sum of the externalparameters is greater than the sum of the internal parameters (i.e., thesocial pressure to follow the prescription is greater than the internal pres-

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R
E

CHAPTER FIVE

C
(I)
All D
All M

(IV)
All C
All ~M(III)
Mixed (C/D)
Mixed (M/~M)
~M

R=E

(II)
All D
All ~M0LL: ) = 1!c
L': *oi+*bi = 1!c
L'

)
1!c

Figure 5.5 Equilibrium diagram: game with a norm and monitoring. Source:
Crawford and Ostrom 2000, 136.

sure), four equilibrium regions exist as shown in ﬁgure 5.5. The verticalaxis is the ratio of the Monitor’s reward for detecting defection to theexpense of monitoring (R/E). The vertical axis is divided into regions
above and below the point at which the reward to the Monitor equals theexpense (R = E).
The reward is higher than the cost above this point and lower than thecost below this point. The horizontal axis on ﬁgure 5.5 is the ratio of thesum of all delta parameters to the advantage of defecting (1 − c). As one
moves to the right, the size of the delta parameters relative to the advantage of defection increases. At point L, the sum of the delta parameters
equals the advantage of defection when the other player cooperates (δ oi +δ oe + δ bi + δ be = 1 − c). Point L′ on the horizontal axis represents that point
where the internal deltas just equal the advantage of defection (δ oi + δ bi =1 − c), and thus the area to the right of L′ represents the region where theinternal deltas offset the advantage of defecting. When (c + δ oi) > (1 − δ bi),
it is clear that both players will choose C (equilibrium region IV in ﬁgure5.5). This is the case in which C is the dominant strategy because theinternal costs and rewards for players 1 and 2 are sufﬁciently high.

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161

Such a norm could be shared in a stable, small community for sometime but would be highly vulnerable to immigration from other communities where individuals did not share the same norm. A few instances ofindividuals taking actions contrary to the norm would generate a cascadeof responses. Unless there were a forum in which the importance of
keeping the norm could be discussed with newcomers so they could learn
the importance of this norm before it disappeared, communities that regulate resources using only norms may ﬁnd themselves exposed to substantial changes in regularized behavior when exposed to considerable
inmigration.
In region III, both players 1 and 2 select a mixed strategy between C
and D.16 As one moves from left to right in region III, the sum of the delta
parameters increases and thus the probability that players 1 and 2 assign
to selecting C increases. In this region, the Monitor also selects a mixed
strategy. The relative amount of the Monitor’s reward decreases as one
moves from north to south. Since the Monitor receives a reward only if
defection is detected, the probability of obtaining a reward decreases as
one moves from west to east because the probability of defection decreases. Thus, as one moves from “northwest to southeast” in this region,
the probability assigned to M decreases. The combined effect is that theMonitor has the least incentive to monitor when relative rewards are low
and the probability of defection is low (in the southeast corner) and thegreatest incentive to monitor when the relative rewards are high and theprobability of defection is high (in the northwest corner of this region).
In equilibrium regions I and II of ﬁgure 5.5, D remains the dominant
strategy for players 1 and 2, as it was in the base game, but for different
reasons. In region II, the expenses of monitoring are higher than the expected reward of the Monitor. Thus, the Monitor will choose < M inregion II. Since < M is the dominant strategy, players 1 and 2 neednot consider the external cost parameters (δ oe and δ be). Given that theinternal deltas are relatively low in relation to the advantage of defecting[(δ oi + δ bi) < (1 − c)], D is the dominant strategy for players 1 and 2.
Region I, on the other hand, represents a socially perverse outcome
whereby players 1 and 2 always defect because the advantage of defecting(1 − c) is greater than the sum of all delta parameters. At the same time,
the Monitor has a dominant strategy of M because the rewards received
from detecting defection exceed the monitoring costs and are guaranteed
to occur, assuming perfect detection, because players 1 and 2 face thedominant strategy of D.
This analysis demonstrates that simply introducing norms or monitoring is not sufﬁcient to change predicted results in a PD base game, andthat a change in predicted results is not always socially beneﬁcial. The
only equilibrium regions where players 1 and 2 select a pure strategy of162

CHAPTER FIVE

cooperating is when internal norms generate high internal cost parameters relative to the advantage of defecting. The presence of a Monitor who
is motivated to select a mixed strategy boosts the level of cooperation in
one region. There is no region where the actions of the Monitor totally
prevent defection. Moreover, the lower the probability of defection, thehigher the monitoring rewards (R) need to be in order to offset the reduced probability of receiving the reward. These results hold because thereward to the Monitor comes only if there is defection to be reported.
Changes in the assumptions about the rewards to the Monitor substantially affect the outcomes. If the Monitor is rewarded speciﬁcally for monitoring, regardless of whether defection is discovered, there are two additional equilibrium regions. In these regions, the reward for simply
monitoring is greater than the costs of monitoring. In one of these, theresult of the choice of a pure strategy to monitor is to make C a dominant
strategy for the two players. In the other, a pure strategy to monitor is
combined with players 1 and 2 both adopting mixed strategies.
If one wanted to analyze the incentive structure found in many ﬁeld
settings where monitors are hired as external, disinterested guards, one
could change the game so that the Monitor receives a salary regardless ofwhether he or she detects defection or shirks. In such a setting, the Monitor has little incentive to monitor and thus the rate of cooperation depends
heavily on the size of the internal delta parameters for players 1 and 2.
Empirical studies and formal models suggest several other motivational
schemes for monitors. Some motivate monitors by embedding them in aseries of nested institutions that reward monitors who actively and reliably
monitor with positive returns from the increased productivity that therules generate (see Milgrom, North, and Weingast 1990). Monitors may
also be direct participants in ongoing relationships where efforts are madeto keep monitoring costs low, to reward one another for monitoring, andto ensure that monitors participate in the greater returns that all achieve
when temptations to defect are reduced. In such situations, monitors mayachieve sufﬁcient beneﬁts from monitoring that they induce a high level ofconformance (but never 100 percent) in an isolated system without recourse to central authorities (Weissing and Ostrom 1991a, 1993).17
RULES

Predictions that individuals will select C rather than D in a PD base gamebased on rules rely upon (1) changes in players’ payoffs because of theaddition of at least one delta parameter to the players’ payoffs, (2) the
addition of an institutionally assigned consequence for breaking a rule,(3) the possibility of detection, (4) at least one player who has authority
to monitor, (5) at least one player who has authority to impose the OR

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A GRAMMAR OF INSTITUTIONS
0+*oi+*oe
0+*oi+*oe
1!*bi!*be!f 1!*bi!*beo
Rm!Em+* m Rm!Em+*om!*bs*os!Es

1!*bi!*be!f0+*oi+*oe
Rm!Em+*om*os!Es

0+*oi
1!*bi
!*bm
0

S

c+*oi
c+*oi
!*bm
0

c+*oi+*oe
c+*oi+*oe!Em+*om
0

S

~S

M

~M

!*bs

~S

1!*bi
0+*oi
!*bm
0

d!*bi!*bed!*bi!*be
Rm!Em+*om
!*bs

S

~S

d!*bi
d!*bi
!*bm
0

4
M

~M

3

3
C

d!*bi!*be!fd!*bi!*be!f
Rm!Em+*om*os!Es4

4
M

1!*bi!*be0+*oi+*oe
Rm!Em+*om

M

~M
3

3
C

D

~M

D
2

2

C

D

1
Payoffs to Monitor (Player 3)

Payoffs to Sanctioner (Player 4)

Deltas for Player 1 or 2

Rm = reward for detecting defection
Em = transaction cost of monitoring*om = change in payoff from obeying
monitoring norm
*bm = change in payoff from breaking
monitoring norm

Es = expense of sanctioning*os = change in payoff from obeying
sanctioning norm
*bs = change in payoff from breaking
sanctioning norm

*oe = external changes in payoffs fromobeying prescription*oi = internal changes in payoffs fromobeying prescription*be = external changes in payoffs frombreaking prescription*bi = internal changes in payoffs
from breaking prescription
OR ELSE for Player 1 or 2
f = fine for Defection

Institutional Statements
Rule: [Players 1 & 2] [Must] [C] [All Rounds] [OR ELSE f]
Monitoring Norm: [Player 3] [Must] [Monitor] [Always]
Sanctioning Norm: [Player 4] [Must] [Impose f on defecting player] [When Monitor Reports D]

Figure 5.6 Game with a rule. Source: Crawford and Ostrom 2000, 146–47.

ELSE, and (6) the base game payoffs. In ﬁgure 5.6, we present a gamethat shifts the norm game in ﬁgure 5.4 to a game with a rule backed by
two norms. The rule that structures this game is: [Players 1 & 2] [must][Cooperate] [Always] [OR ELSE f]. The rule adds a ﬁne (f) to the payoffs
for players 1 and 2 for D if their defection is monitored and sanctioned.
This rule is backed by both a monitoring norm ([Player 3] [must] [Monitor] [Always]) and a sanctioning norm ([Player 4] [must] [Impose f on aPlayer] [When Player 3 reports that a Player has defected]). The additionof a monitoring norm adds delta parameters to player 3’s payoffs. The
sanctioning norm adds another player, player 4. Player 4 (the Sanctioner)

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CHAPTER FIVE

faces a cost of sanctioning and receives delta parameters as payoffs.18 In
other words, the Sanctioner in this game is a volunteer who is “rewarded”
solely by normative interests.
A wide variety of mixed-strategy equilibria are possible that depend on(1) the relative expected value of the ﬁne and the relative size of the delta
parameters originating from external sources for players 1 and 2, (2) the
relative strength of the reward and deltas associated with monitoring to
the costs of monitoring, and (3) the value of the deltas associated with
conforming with the sanctioning norm minus the costs of sanctioning as
compared to the value of the delta parameters for not sanctioning. Evenin a rule-governed game, if Monitors are not motivated to monitor andSanctioners are not motivated to sanction, cooperation rests substantiallyon internalized norms of the players.
We set aside the tasks of analyzing the many possible equilibrium regions and focus here on the simpler task of establishing conditions forequilibria in which players 1 and 2 always cooperate. The rule adds a
Fine and a new player, yet the Monitor still plays a crucial role. The partsof the game that come from the OR ELSE (the ﬁne and the sanctioningnorm) do not even enter the game on branches in which the Monitor
chooses −M.19 As in the norm game, we assume that players 1 and 2 do
not see each other’s choices, and that the external delta components occur
only when player 3 monitors. In order to be sure that the Monitor will
choose M when players 1 and 2 are expected to choose C, the sum of
the delta parameters for the Monitor must be higher than E, because theMonitor will not expect to receive rewards (R) for detecting defection. As
in the base game with norms, in the absence of monitoring, players 1 and
2 will select only C as a pure strategy when their internal delta parameters
are high enough to offset the advantage of defecting when others cooperate. Thus, the selection of C as a pure strategy depends either entirely oninternal sources of normative constraint for players 1 and 2 (δ oi and δ bi),or it depends on the conﬁguration of the size of the normative constraint
for the Monitor (om and bm) relative to the monitoring costs, the sum ofinternal delta parameters for players 1 and 2, the expected value of theexternal delta parameters, and the expected value of f. This latter conﬁguration matters only when the normative constraint for the Monitor ishigher than the monitoring cost. Cooperation can be a pure strategy only
when either of the following conditions is met:
1. δoi + δbi > 1 − c
2. [(δo m + δb m ) / E ] > 1 and[(δoi + δbi ) + (p(M)* (δoe + δbe ) + (p(S)* f ) ) > 1 − c ].
The ﬁrst condition is the same as in the game in ﬁgure 5.4. The second
condition was not possible in the earlier game as the Monitor was not

A GRAMMAR OF INSTITUTIONS

165

motivated by a norm. Defection by both players continues to be an equilibrium of this game as well as of all the games considered so far.
In settings where players in the base game develop strong internalized
norms (high internal delta parameters), the presence of even a lowto-moderate f may be sufﬁcient when combined with Monitors and Sanctioners who are motivated to do their job, to encourage a high rate ofcooperation. Consequently, the effect of f depends both on its relative
size, the size of the deltas for players 1 and 2, and the behavior of theMonitor and the Sanctioner. If players 1 and 2 expect the Monitor or
the Sanctioner to break their respective norms, then the expected probability of S (the Sanctioner choosing to sanction) tends toward zero and fdrops out of the decision calculus for players 1 and 2. In order for the
Monitor and the Sanctioner to be motivated to do their jobs, their valueof following the monitoring and sanctioning norms has to be greaterthan the relative cost of doing their jobs. In the case of the Sanctioner, thevalue of the delta parameters needs to be greater than the cost for imposing that sanction: (δos + δbs ) > Es.20 Clearly, recognizing rules in formal
analysis of dilemma situations does not automatically “solve” the dilemma and end analysis. Instead, adding rules suggests a whole new setof research questions.
FURTHER RESEARCH QUESTIONS

The game in ﬁgure 5.6 is intended to stimulate further formal and empirical research. It suggests questions such as: How do changes in the levelof internalization of rules (δ bi and δ oi) affect the levels of monitoring andsanctioning required to bolster cooperation at given levels of social pressure (δ oe and δ be)? What size do external delta parameters need to be inorder to ensure cooperation at various rates of monitoring and sanctioning with a given value of f that is less than the advantage of defection?
How do the incentives to monitor and sanction differ if we assume that
players 3 and 4 are the same person? And, what are the empirical equivalents of delta parameters and the external ﬁne in similar situations?
The disjunction between theoretical predictions of complete free-riding
in PD situations and the rates of cooperation in case studies and laboratory experiments have provoked much intellectual ferment and development (Udéhn 1993; Camerer 2003; Boyd et al. 2003). The syntax facilitates empirical analysis of the components in institutions that foster
cooperation in dilemma situations by providing a language with whichto discuss differences between institutions in various settings and changes
in institutions over time. When an ADICO component is added to an
institutional statement, the statement has changed from one type of institutional statement to another. Similarly, when a component drops out,
the type of the institutional statement changes. Key questions then in-

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CHAPTER FIVE

clude: What are the processes that lead to such additions or deletions(Burns and Dietz 1991; Opp 1982)? Do the additions or deletions increaseor decrease the level of cooperation (Orbell, van de Kragt, and Dawes
1991)? What aspects of the content of the components explain differencesin the level of cooperation (e.g., the level of the punishment in the OR
ELSE [Rowe 1989], the inclusiveness of the ATTRIBUTE)? A focus on
the presence or absence of components, the content of components, andthe source of the components promises to be more replicable by scholars
than some of our current dialogue that uses the distinctions of “formal
and informal” or “internal” and “external.”
Using the Grammar for Synthesis
Once we have developed a syntax for expressing institutional statements
in a systematic fashion, we can better address a number of important
theoretical questions. The above analysis of social dilemmas is just one
example. In any science, however, understanding what others have already discovered is an important part of research. Synthesis and discovery
go hand in hand.
DISENTANGLING FORMAL LAWS AND INFORMAL INSTITUTIONS

Synthesizing ﬁndings from the different subﬁelds that relate to each typeof institutional statement is one important task for those interested ininstitutions. The ADICO syntax and its game applications can be used tohelp disentangle discussions of formal laws, informal institutions, andordered behavior. Written laws or procedures often conform to the
ADICO syntax requirement for a rule, yet before analyzing the law as arule in use, one would ﬁrst examine whether those listed in the ATTRIBUTES share an understanding of the law, and whether shared understanding also exists for the related monitoring and sanctioning institutional statements. As Cooter (1994) stresses, the complexity of modern
economies is so great that centralized law creation cannot effectively copewith the need to achieve normative regulation among communities ofindividuals who repeatedly face collective-action problems (see also Ellickson 1991). Thus, individuals frequently adopt norms or rules without
enacting them as written law. These prescriptions are sometimes called“informal” institutions, but they may well be binding if challenged in acourt (Cooter 1994).
We argue that if the prescriptions are shared, then they are either norms
or rules. Both formal and informal prescriptions can be classiﬁed asshared norms or rules depending on the presence of the OR ELSE component. Whether or not the formality of an institution inﬂuences the level

A GRAMMAR OF INSTITUTIONS

167

of compliance raises an interesting set of questions that disappear if we
use the terms “formal” and “informal” without attention to what thosedistinctions mean. Do we ﬁnd differences between situations governedby a similar ADICO statement that arise from the formalization of theinstitutional statement? Do differences in delta parameters explain the
difference in behavior? Do we regularly expect delta parameters to increase or decrease when an institutional statement becomes formalized insome way?
LEGITIMACY AND COMPLIANCE

The language of the syntax enables researchers to address importantquestions related to the legitimacy of rules (Tyler 1990). If rulers impose
rules primarily by force and ﬁat, individuals subject to these rules areunlikely to develop internal delta parameters associated with breaking
the rules. Nor are delta parameters stimulated only by observation byothers in such settings likely to enhance the rate of rule conformance. If
those who are supposed to follow a rule view it as illegitimate, they may
even reward one another for actions that break the rules (a positive be)instead of adopting the type of metanorm envisioned by Axelrod (1986)(a negative be).
The complementarity of deltas and the OR ELSEs emerges as importantin analyses of compliance. When delta parameters are close to zero, thecosts of maintaining compliance with the OR ELSE drastically increase(Levi 1988; Margolis 1991; Ayres and Braithwaite 1992). Without a relatively high level of voluntary, contingent compliance to rules, Margaret
Levi (1988) explains that rulers can rarely afford the continuing costs
involved in hiring enough monitors and sanctioners, motivating them tobe efﬁcient, and achieving compliance by the actual imposition of sanctions in a sufﬁcient number of instances that citizens try to conform to
the rules rather than risk the chance of detection and punishment. If violators can expect to reap the beneﬁts of violating prescriptions without facing the probability of some established punishment (if there is no OR
ELSE), however, then the experience of feeling the “sucker” may erode
the value of the delta parameter (Levi 1988, 1990; E. Ostrom 1990;
E. Ostrom, Gardner, and Walker 1994; Mansbridge 1994).
BASIC NORMATIVE ASSUMPTIONS

A growing body of work on cooperation and compliance considers themix of normative and material motivations that individuals consider
when faced with choices.21 These works treat the normative aspects ofdecisions up-front as a signiﬁcant part of the analysis. Margolis (1991,

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CHAPTER FIVE

130) argues for the necessity of such an approach: “[i]f we analyze everything in terms of strict self-interest and then include some social motivation only if we get stuck or if there is something left over, it is not likely
to lead to nearly as powerful a social theory as if the two things are built
in at the base of the analysis.” Delta parameters provide a conceptual
language with which to incorporate explicitly normative considerations
into analysis from the beginning and to discuss differences in studies thatincorporate normative incentives along with extrinsic payoffs.
In table 5.2, we list studies that have addressed three types of questions
about normative motivations. The top section of the table lists different
assumptions regarding the meaning and sign of delta parameters. Knack’s(1992) analysis of voter turnout, for example, illustrates the insight possible from a careful study of the normative inﬂuences captured by the delta
parameters. He offers empirical evidence of the substantive content ofinternal deltas and external deltas associated with a turnout norm and of
the inﬂuence of these delta parameters on the probability that an individual will vote. Interestingly, his interpretation of the declines in voter turnout echoes the importance of monitoring and sanctioning discussed earlier. He ﬁnds that social pressures (external deltas) are a key inﬂuence on
voting turnout. Social pressure operates, however, only when voters expect to be in situations where someone may ask them if they voted, andmay express disapproval if they did not. As the percentage of individuals
in organizations, in relationships with neighborhood residents, and in extended family situations drops, this monitoring decreases, and the powerof the social sanction (the external delta) diminishes (see also Amaro deMatos and Barros 2004).
The middle section of table 5.2 cites work that addresses the implications of assuming different individual orientations in a situation as discussed in chapter 4. For example, a rational egoist would assign a zero
value to praise or blame for obeying or breaking prescriptions. One interesting variant of this analysis ties the size of the delta to the number ofothers who conform to the prescription; conformance is conditional (Elster 1989a).
Scholars cited in the last section of table 5.2 discuss variables that inﬂuence the creation and maintenance of delta parameters. Offe and Wiesenthal (1980) offer an interesting substantive application. They consider
the inﬂuence of the costs labor unions face in building and maintaining
shared commitment to participation norms as this in turn affects theirability to compete with other interest groups. Several other authors ask
whether normative incentives increase or decrease with use. Olson (1991)
views the delta parameters as scarce resources that can be dissipated withtoo much use, while Hirschman (1985) and Mansbridge (1994) come tothe opposite conclusion. They argue that the normative constraints in-

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A GRAMMAR OF INSTITUTIONSTABLE 5.2
Delta parameters and normative concepts used in recent literature
Delta parameters

Concepts used by otherauthors

CitationsSize, sign, and interpretation of delta parameters
+δ oi
+δ oe

−δ bi
−δ be

+δ oi and −δ bi+δ oe and −δ beTypes of players+δ oe large∆=0+δ oi and/or −δ bi large+δ o large when numberof cooperators low+δ o large when numberof cooperators high∆ larger when number ofcooperators > thresholdwarm glow
encouragement
status improvement /reputation enhancement
honor
duty
cost of being punished(P and P′)
social sanctions
third-party sanctions
internalized norms
public-spiritedness
moral duty
duty
externally sanctioned norms
reputation
responsibility
moral judgment

Knack 1992
Bendor and Mookherjee 1990**
Coleman 1987
Mansbridge 1994
Etzioni 1988
Commons [1924] 1968
Coleman 1987
Kreps 1990
Commons [1924] 1968
Sugden 1986

zealotselﬁsh rational individual
everyday Kantian
elite participationists

Coleman 1988
Elster 1989a
Elster 1989a
Elster 1989amass participationists

Elster 1989apeople motivated byfairness

Elster 1989aCreation and maintenance of delta parameters∆ affected by labor union
activities∆ are scarce resources thaterode with use∆ are resources that
increase with use∆ affected by external ﬁnes
∆ lower when rules comefrom outside authority

Andreoni 1989; Ledyard 1995
Coleman 1988
Coleman 1988
Ullmann-Margalit 1977
Knack 1992
Axelrod 1986*

Offe and Wiesenthal 1980
Olson 1991
Hirschman 1985; Mansbridge 1994
Frey and Jegen 2001
Frey 1994

Source: Crawford and Ostrom 2000, 131.
* In some cases, these sanctions may meet the criteria of an OR ELSE.
** As with the P and P′ of Axelrod’s, the third-party sanctions may at times meet the criteria of an OR
ELSE.

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CHAPTER FIVE

crease in size as they are used repeatedly by individuals in a group. Frey
and Jegen (2001) contend and provide evidence that external interventions, such as ﬁnes, adversely affect the size of delta parameters, particularly the internal deltas. Frey (1994) speculates that the deltas associatedwith rules will be higher when individuals participate in making their own
rules than when rules are made by higher authorities. The other works inthis section debate whether deltas increase or decrease with use.
FREEDOM AND CONSTRAINT

By partitioning sets of actions or outcomes into required, forbidden, andpermitted subsets, rules both constrain and expand the levels of freedom
for actors. Frank Knight reﬂected on the relationship of institutional rules
to freedom through the constraints that they impose. “But freedom is likeother traits of human nature, in that it is created by a social situation or,
in more technical terms, a complex of institutions. This also sets limits tofreedom” (Knight 1965, 304). Freedom is restricted by rules that limit theactions an individual can take in an action situation. In a market, forexample, an individual is only legally authorized to conduct speciﬁc commercial transactions and not to take goods by force. Legislators can voteonly on bills presented to them through a formalized set of procedures.
The restrictions on the freedom of one person, however, open up opportunities for creative actions by others. So individual freedom of action is
also expanded by enforceable rules since rules enhance the predictabilityof the actions taken by others in a decision situation. A buyer may obtain
credit from a seller to buy goods for which the buyer does not yet havesufﬁcient funds because the seller knows that a contract for future payment can be enforced in a court. The capacity to bind oneself to future
performance through a set of enforceable rules thus opens opportunities
not available without a system of enforceable rules. It is, of course, thisrelationship of the freedom to accomplish something by being able toconstrain oneself that underlies the myth of Ulysses tying himself to themast in order to resist the call of the sirens and thus safely guide his boat
through a rocky passage (Elster 1979; see also V. Ostrom 1996, 1997).
INSTITUTIONAL CONFIGURATIONS

So far, the descriptions of the components of rule and norm statements
focus primarily on single statements as if the content of the institutional
statements is independent. The focus on single statements is for expository reasons only. When we examine the interactions of individuals in asituation, we expect to ﬁnd that a conﬁguration of rules, norms, andshared strategies inﬂuences the choices of individuals at any one point in

A GRAMMAR OF INSTITUTIONS

171

time. In fact, we often ﬁnd nested institutional conﬁgurations, professional norms, or agency rules nested within government regulations, forexample.
In some cases, the CONDITION component of an institutional statement explicitly states the linkages between statements in a conﬁguration.
For example, a rule permitting some action may state as a CONDITIONof the rule that the individual follow some procedure outlined in anotherrule. In other cases, the linkage between statements is implicit. For example, the CONDITION component of a voting rule for legislation may not
overtly make reference to the quorum rule, but the speciﬁc quorum rulein place strongly inﬂuences the effects of the voting rule.22 A rule statingthat a majority must approve before a bill becomes a law affects behavior
differently depending upon (1) the quorum rule that states how manymembers must be present and voting for a vote to be legal and (2) the rulethat states what happens if no positive action is taken (e.g., the OR ELSE
rule for a collective-choice aggregation rule that states a return to thestatus quo or an OR ELSE that states some alternative outcome) (seetables 2.2 and 2.3).
A syntax for individual prescriptions serves useful functions in conﬁgurational analysis. It provides an initial method for sorting the conﬁguration of institutional statements into rules, norms, and shared strategies.
The ability to identify particular parts of the prescriptions and to state allprescriptions in the same DEONTIC makes organizing and comparing
the institutional statement in a conﬁguration easier. This facilitates analysis of inconsistencies in conﬁgurations, such as instances when institutions
simultaneously permit and forbid the same action. The syntax can also
be used to help in the organization of types of rules—as we discuss inchapter 7.

Using the Grammar in Empirical Field Research
In empirical ﬁeld studies, the researcher’s task is to discover the linguistic
statements that form the institutional basis for shared expectations andpotentially for the observed regularity in behavior. Essentially, this entails
discovering which of the components exist in these statements and thecontents of those components. This frequently requires research employing qualitative methods including in-depth interviews or the reconstruction of historical and case materials.
In explaining established patterns of interaction, the researcher has todecide whether it is reasonable to use an institutional statement that assigns a DEONTIC to an action or outcome. In the ﬁeld, the researcher
listens for normative discourse. Is there an articulated sense of moral or

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CHAPTER FIVE

social obligation expressed? If the individuals in a study share only AIC
statements, their discussion of why they would follow such advice focuses
only on prudence or wise judgment. “The best thing to do when facedwith a choice between A and B under condition Y is to choose A because
one is usually better off with this choice.” When individuals shift to alanguage of obligation, they use terms such as “must” or “must not” todescribe what they and others should do. When social and moral obligations are discussed, an empirical researcher initially assumes that it is appropriate to include a DEONTIC in institutional statements used to explain behavior. “The obligatory action when faced with a choice between
A and B under condition Y is to choose A, because this is the proper
action.” Occasionally, the analyst can directly pick out rules, norms, andstrategies from written policy statements such as a law code, written regulations, speciﬁc statements in a court decision, or written agreements established by a village or association. It may also be possible to extract
clear institutional statements from institutional statements retained
through oral traditions. (All of these methods were used to identify therules discussed in chapter 8.)
For example, if one were analyzing the use of a commons in workplacesettings, it would be easy enough in many ofﬁces to ﬁnd a sign taped to
the front of the microwave that speciﬁcally states “If you use the microwave you must clean up your own mess!” (as in example 4 above, which
was observed in the commons of the Complex Systems Group at the University of Michigan in November 2002). In such cases, the institutional
statement can be directly parsed using the grammar of institutions. The
main operational question here is whether or not that statement foundamid the written statements or oral traditions is really a shared institutional statement that participants know and use. The “know and use”
conditions here are particularly important for analysis of the inﬂuence of
the institutions on the outcomes in the action situation, which is the emphasis of this book (“rules in use” as described in chapter 2).
When the institutional statements relating to a speciﬁc action situation
are clear and shared, and when those statements are at an appropriate
level of precision for analysis of the question at hand, then extracting
them is a relatively straightforward process. The second condition here
(appropriate level of precision) returns attention back to the question ofscale. Just as different maps are needed depending on the geographic question at hand (e.g., whether one is explaining, for example, where an islandis versus explaining to someone how to drive to a speciﬁc location on thatisland), so too different levels of detail or precision may be appropriate
to different kinds of policy questions. Sometimes the level of precision of
the statements in the empirical setting is quite appropriate for analysis (asis discussed in chapter 8).

A GRAMMAR OF INSTITUTIONS

173

Fieldwork will not always lead to easy extraction of simple and clear
institutions-in-use occurring exactly at the appropriate level of precision.
Consequently, empirical researchers of institutions usually must develop
appropriate summaries of the complex morass of legal, written, or oral
statements that are relevant to the action situation being analyzed. For
some analyses of campaign ﬁnance rules, for example, it might be sufﬁcient to summarize volumes of legal code, federal regulations, and court
decisions concerning campaign ﬁnance in the United States into the set of
institutional statements: “Candidates may spend unlimited amounts ofmoney on the condition that disclosure rules are followed. If the spending
does not occur in a manner that meets this condition, then the candidate
is ﬁned.” Another set of institutional statements that could be extracted
here would be: “Candidates may spend unlimited amounts of money on
the condition that they refuse public ﬁnancing. If a candidate does notrefuse public ﬁnancing, then the FEC must ﬁne the candidate for exceeding legal spending limits.”
The analytical work of identifying the statements that most need to besummarized and identifying the appropriate level of precision are important research design questions in institutional analysis analogous to
specifying the appropriate variables and the appropriate precision of variables in statistical analysis. As we have used the syntax, we ﬁnd that it
sometimes takes trial and error to extract a useful summary of institutional statements in an empirical action situation that bring analyticalfocus to the appropriate questions at the appropriate level of precision.

Some Next Steps
We started this enterprise in an effort to deﬁne the concept of rules clearly.
We found that in order to do this, we needed to clarify how rules wererelated to norms and strategies. As a result, we have learned a great deal
about norms and strategies. Delving deeper into each of the components
brought to light connections among these concepts, and the literature that
focuses on them, which had not previously been linked. Moreover, ﬁtting
this all into a grammar helped us to catch inconsistencies and to further
test and reﬁne our understanding of each type of institutional feature andeach component.
A larger question that this research effort must eventually address inorder to return to the core concerns of political science is how an institutional grammar relates to a theory of knowledge and a theory of action(see V. Ostrom 1997). Our notion of delta parameters brings normative
considerations into the analysis of action and consequences. This is not
the same as incorporating concerns about the welfare of other actors

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CHAPTER FIVE

into an individual’s calculus. The delta parameters arise from commitments to the norms and rules of a community, not from the incorporationof other’s payoffs into one’s own payoff.23 Our notion of OR ELSE brings
institutional consequences into individual-level decisions. All of the institutional statements affect expectations about others’ behavior, which create stability in social life. The ADICO syntax illustrates the potentiallycumulative manner in which institutional statements can affect individual
expectations. Thus, improving our ability to analyze and to discuss theinstitutional statements prepares us for a more thorough analysis of institutions and human action. Attending to a grammar of institutions equips
us to return to the core issues of institutions and political order with neweffectiveness.

Six
Why Classify Generic Rules?

IN CHAPTER 5, Sue Crawford and I use the ADICO grammar to distinguishamong three essential components of all institutional analyses: strategies,
norms, and rules. Hopefully, the reader has grasped the importance ofunderstanding how individuals adopt strategies in light of the norms theyhold and within the rules of the situation within which they are interacting. In chapter 7, we will move forward to discussing a way of consistently grouping rules so that the analysis of rule systems can be mademuch more cumulative. This chapter is a prologue to chapter 7 in that it
addresses why we would introduce still further conceptual tools beyond
those of the general Institutional Analysis and Development (IAD) framework and the grammar of institutions discussed in chapter 5.
The approach to classifying rules to be discussed in this and the next
two chapters has taken many years to develop. Some of the notes drawn
on in writing these chapters were written in the early 1980s. A lecturedelivered in Bielefeld in 1982 was my ﬁrst effort to elucidate how rules
were the “hidden” structure underlying games. When I gave a revised
version as my Presidential Address to the Public Choice Society meetingsin March of 1984 (published two years later as E. Ostrom 1986), andposited the seven types of generic rules that are discussed in this and thenext two chapters, several colleagues strongly criticized me for introducing so much complexity. They asked, “Why are you driven to do something so unnecessary?”
Among the answers I have given to these questions is the following listof “needs”:• to work further toward solving babbling equilibrium problems for scholars
doing institutional analysis;• to understand how action situations are constructed so that reasonable reforms can be considered;• to move beyond slogan words to describe institutions;• to cope with the immense diversity of rules by clustering them into seven
generic rules;• to look at rules as information-transformation mechanisms; and• to study the underlying universality of rules.

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CHAPTER SIX

Solving Babbling Equilibrium Problems
In his book Convention, David Lewis (1969) clearly laid out the problemsof communicating with one another. Assuming that scholars—as well asanyone else—are better off when successful communication occurs, theproblem of communication is that of arriving at a shared convention for
the names (or symbols) that we will attach to diverse states of the world.
In essence, communicators are engaged in a signaling game. If the senders
and receivers of a signal do not understand it the same way, they are
unfortunately in a “babbling equilibrium.”
The sender of a signal wishes to convey information about states of theworld to a receiver of that signal. Since terms are not endowed with somepreexisting meaning, the sender could use any of a wide diversity of terms
for the particular states of the world being described. If there were, forexample, three states of the world that the sender wanted to describe, thesender could use colors to describe the relevant states of the world, suchas red for state 1, green for state 2, and orange for state 3. In essence, thisis what a trafﬁc signal does when it is appropriately engineered. By sending a green signal to the drivers on one street, it conveys to them theinformation that the drivers on the cross street have a red signal and will
not be crossing the intersection. By sending an orange signal to one street,
it is conveying that the signal on the cross street is about to change anddrivers there will begin to cross the intersection. By sending a red signal
to the drivers on the ﬁrst street, it is conveying that drivers on the crossstreet have the right of way and it is dangerous (as well as illegal) to crossthe street.
One equilibrium of a signaling game is that everyone uses and understands the signals in the same way. This is obviously the optimal equilibrium. When it comes to trafﬁc signals, the advantage of achieving thisequilibrium is obvious! There is nothing inherent in red, green, and orangethat means stopping, going, and slowing down. The signals used for stopping and going could easily be different colors (or even the reverse, sothat green means stopping and red means going); the crucial problem of
optimal communication is that senders and receivers use the same signal
to mean the same thing. If they do not, their signals are a form of babbling
and the equilibrium outcome is highly undesirable.
In developing the ADICO syntax as a way of sorting out the diverse
elements involved in institutional statements, we could just as well havereversed the terms we use for statements containing (or not containing)“or elses.” In chapter 5, we proposed that a statement containing four
elements of the syntax (ADIC) should be called a norm and ﬁve elementsof the syntax (ADICO) should be called a rule. Instead, we could have

WHY CLASSIFY GENERIC RULES?

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called the ﬁrst a rule and the second a norm. A quick look at table 6.1
shows that several scholars do exactly this. We even observe some scholars who use the term “norm” at one time for an ADIC statement and atother times for an ADICO statement.
As senders of a signal, they probably know which attributes are present
when they use one term or another. As receivers of their signals, however,
how are we to know which attributes are present or not? Unless we haveaccess to the same materials and spend costly effort determining which
attributes are present or absent, we cannot know the meaning of a termwhen Author Jones uses all ﬁve attributes as reference for a norm andfour attributes to be a rule as contrasted to Author Smith (or even Author
Jones in another text). If senders and receivers have the same attributesof the states of the world in mind, when they use a particular word todescribe this state, they reach the optimal equilibrium in a signaling
situation.
The similarities between rules, norms, and shared strategies in the
ADICO syntax help us to understand why the literature so frequently uses
these terms interchangeably. They share several of the same features.
Table 6.1 sorts the concepts that other authors use into the types of institutional statements created by our syntax. All of the terms used by otherauthors, shown in the top section of the table, appear to describe institutional statements that are shared strategies according to the syntax; they
contain AIC components. The need for a consensus in the use of terms isvividly illustrated by examining the number of different terms in eachsection of the table as well as the fact that several terms appear in all three
sections. That these terms have been used in so many different ways is
not a criticism of past work. Rather, it illustrates the difﬁculty of untangling key social science concepts that are foundational for institutional
analysis.
Lewis proposes that meaning occurs when senders and receivers of signals reach such an equilibrium no matter which speciﬁc term is being
used. There are, of course, other equilibria possible where there isagreement on the attributes of one term but not the others. This appears
to characterize the current situation where scholars are more likely toagree on the use of the term strategy. This is contrasted to the use of rules
and norms for which there is little agreement. Lewis calls these “babbling” equilibria. Receivers have no reasonable method for imparting
meaning to some of the signals they receive, while there is agreed-uponmeaning for others (see also Crawford and Sobel 1982; Farrell 1993).
The problem of babble about rules and norms is not conﬁned to scholars who study something other than rules and norms and only occasionally misuse a term whose meaning has become well established by those
who work extensively on these concepts. In a recently published book

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TABLE 6.1
Shared strategies, norms, and rules as used in ADICO syntax and in recent literature
ADICO
components

Terms used by
Crawford-Ostrom

AIC

Shared Strategies

ADIC

Norms

ADICO

Rules

Source: Crawford and Ostrom 2000, 130.

Terms used by other authors
Axelrod 1981—Rules, strategies
Axelrod 1986—Norms
Bourdieu 1977—Doxic elements of action
Calvert 1992—Equilibrium strategies
Hodgson and Knudsen 2004—Conventions
Levi 1990—Norms
March and Olsen 1989—Rules
Meyer and Rowan 1991—Taken-for-granted actions
Myerson 1991—Rules
Rowe 1989—Rules of action
Schank and Abelson 1977—Scripts
Schelling 1978—Focal points
Schotter 1981—Institutions
Ullmann-Margalit 1977—Conventions
Azar 2004—Norms
Bicchieri 1997—Norms
Braybrooke 1987—Conventions
Braybrooke 1996—Rules
Coleman 1987—Norms
DiMaggio and Powell 1991—Institutions
Levi 1990—Norms
Lewis 1969—Conventions
March and Olsen 1989—Rules
McAdams 2001—Normative attitudes
Meyer and Rowan 1991—Taken-for-granted actions
North 1981—Ethical codes
Rowe 1989—Obligations
Schotter 1981—Institutions
Sugden 1986—Conventions
Ullmann-Margalit 1977—Social norms
Weber 1947—Conventions
Albert 1986—Laws
Axelrod 1986—Norms backed by metanorms
Coleman 1987—Norms
Commons [1924] 1968—Working rules
Hurwicz 1994—Rules of the game-form
J. Knight 1992—Rules
Levi 1990—Legalistic institutions
North 1990—Rules
Shepsle 1979a, 1989—Rules
Ullmann-Margalit 1977—PD norms, decrees
Weber 1947—Laws

WHY CLASSIFY GENERIC RULES?

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called Social Norms, the editors—who had convened several conferences
and worked extensively with a group of knowledgeable scholars conducting studies on social norms—introduced the resulting volume by commenting: “These chapters suggest that consensus about social norms islimited both across disciplines and within them. To begin, the concept ofsocial norm clearly means different things to different scholars” (Hechterand Opp 2001, xii). They go on to bemoan the fact that as “there is nocommon deﬁnition of social norms, there can be little agreement about
how to measure them” (xiii). Obviously, various authors in the volume
did deﬁne social norms—many of them in a way that is consistent withthe syntactic deﬁnition provided in chapter 5. The book contained, however, a multiplicity of deﬁnitions of the central concept under study. In an
excellent recent review of how the concept of norms is used in the ﬁeldof law and economics, McAdams and Rasmusen (forthcoming) also bemoan the lack of consensus on the deﬁnition of a norm.
The existence of a babbling equilibrium in regard to these terms is a
key problem for the social sciences.1 The concepts of rules and norms areat the heart of many core theoretical questions having to do with how
individuals coordinate activities with one another. Lacking agreed-upon
deﬁnitions of these terms leaves us all in a suboptimal babbling equilibria
rather than in a scientiﬁc signaling game where general progress is likely.
If scholars were to accept the syntax as we have laid it out in chapter
5—or an improved version of it—it would help to solve the babbling
equilibrium problem.
At an earlier juncture, Douglass North (1990) dug into another babbling equilibrium problem. Many scholars tended to use the concept ofan organization and of an institution interchangeably. North has insisted
on a key difference between organizations and institutions. As North described his approach:A crucial distinction in this study is made between institutions and organizations. . . . Organizations include political bodies (political parties, the Senate, acity council, a regulatory agency), economic bodies (ﬁrms, trade unions, family
farms, cooperatives), social bodies (churches, clubs, athletic associations), andeducational bodies (schools, universities, vocational training centers). They aregroups of individuals bound by some common purpose to achieve objectives. . . . [T]he emphasis in this study is on the institutions that are the underlying rules of the game and the focus on organizations (and their entrepreneurs)
is primarily on their role as agents of institutional change; therefore the emphasis is on the interaction between institutions and organizations. (4–5)

We are using terms in a manner consistent with North’s distinction. Rules
are part of the underlying structure that constitute a single action situation
or a series of them. Organizations may be participants in a situation struc-

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tured by rules and can, in turn, be analyzed by looking at the linked action
situations used by the group “bound by some common purpose to achieve
outcomes.” Most organizations would be composed of multiple simultaneous and sequential action situations—all constituted by rules as well as
by the physical world.
Sorting out organizations and institutions, and strategies, norms, andrules is a beginning. It is, however, not enough. The problem becomeseven more substantial when one moves beyond the effort to develop ageneral deﬁnition of rules and norms to ways of classifying rules. As soon
as one digs below the surface of an action situation to explore the prescriptions partly responsible for their structure, one is struck with the vastarray of potential rules. The ADICO framework enables the analyst toclassify institutional statements by using their syntax to determine thedifference between strategies, norms, and rules. Now we need to ask,
What is a useful classiﬁcation based on the semantics of rules?

The Policy Analyst’s Need to Understand How to Reform Situations
Policy analysts must be able to use the semantics of rules. Elected ofﬁcials,
the staff members of national, state, local governments and citizen interest
groups, and many social scientists are asked what rules should be changed
to solve a particular kind of problem. The questions may be in regard to
problems as global as how to change the incentives facing users of carbon
so as to reduce the likelihood and magnitude of global warming or as
local as how to change the parking regulations in a small town so as
to make it a more attractive location for residents in the region to shop
downtown.
The policy analyst stands in relationship to “repairing” single andlinked action situations as a doctor stands in relationship to a sick patient,a mechanic in relation to a car that does not run, or a computer technician
in relationship to a computer system producing strange symbols on a
printer.2 In attempting to solve all these problems of poor performance,
the “Doc” has to dig under the surface, begin to think which subsystemor linkage among systems is most likely to be causing the problem, andbegin to do tests to conﬁrm these initial speculations. For physicians andmechanics, the systems they need to understand are primarily biophysicalsystems and subsystems. The mechanic learns how to take the system
totally apart and reconstruct it subsystem by subsystem. The general physician does not have the freedom to take his or her patient’s system apartbut certainly learned in medical school how to dissect other living beings,
witnessed many surgical operations and autopsies, and studied a cumulative body of knowledge about how various parts of the human bodyshould work. The computer technician has both a mechanical system toWHY CLASSIFY GENERIC RULES?

181

try to understand as well as multiple layers of software languages. He or
she must know what each of the major types of commands does in themost frequently used languages and which types of commands are likely
to be compatible or incompatible.
The policy analyst’s task is closer to that of the computer technician
than to the auto mechanic or physician. A very large proportion of what
the policy analyst must understand is language-based. When one is studying rules, after all, the content is entirely language-based. Yet, it is alsoessential to know key aspects of the relevant biophysical and social world
to propose changes that are likely to improve outcomes. When we areasked to propose new rules, however, the request is for something thatwill be expressed in language, as Vincent Ostrom (1987, 1993, 1997) has
so often articulated.

Moving beyond Slogan Words to Describe Institutions
The embarrassment that we face is that policy analysis has yet to develop
a coherent understanding of how our subject matter should best be expressed, how rules ﬁt or don’t ﬁt together to shape observable behavior
and outcomes. All too often, slogan words, such as “privatization,” “centralization,” and “decentralization,” are used as substitutes for carefulanalysis. What are the speciﬁc rules that we are talking about when wetalk about a privatization or decentralization policy? What changes in the
incentives of participants will occur if we propose a particular set of newrules versus other potential sets? The lack of a disciplined language tobe able to analyze, dissect, and propose better reforms was dramaticallyillustrated for the world after the collapse of the former Soviet Union.
Western scholars were asked to help Russia and the other “newly industrializing countries” to create a vigorous and productive private market
economy. Many of our proposals were actually accepted and efforts madeto implement them. But, instead of an open, competitive market, the rules
proposed by policy analysts generated commercial monopolies, massive
corruption, and little economic growth. And, this is not the only time thatthe recommendations of policy analysts were accepted and yet did not
produce the predicted results. There is obviously a lot of work to be done.

Coping with the Immense Diversity by Identifying Generic Rules
At the same time as I am committed to digging into and revealing an
underlying universality in the components of human action, I am repeatedly challenged and amazed by the multiplicity of rules that colleagues
and I have recorded in ﬁeldwork (see chapter 8) and that other scholars

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have used in their analyses of situations.3 If one focuses on only the detailed aspects of rules, it is hard to see how they are similar and how theydiffer. It is then hard to analyze carefully how changes in a particular typeof rule (as contrasted to changes in several different types of rules) affectthe resulting situations. This recognition and appreciation of variety leads
one to have a substantial wariness related to the capacity of humans todesign optimal systems without a substantial trial-and-error process so as
to learn what works in a particular biophysical environment. I will returnto this issue in chapter 8.
The puzzle has been how to develop a language to express rules thatenables one to capture in a consistent and cumulative manner the mostgeneral characteristics of the basic rules while providing a structure thatenables scholars to enumerate and test the impact of changes in a rule
type. I remember my surprise when I was working on a draft of my Presidential Address to the Public Choice Society (E. Ostrom 1986) and foundthat Charles Plott was unaware that his work on “default conditions”(Grether, Isaac, and Plott 1979) was an excellent empirical study related
to earlier work by Niskanen (1971) and Romer and Rosenthal (1978) on“reversion levels.” I had asked Plott to review my draft paper where I
refer to that earlier paper, in which Plott and his colleagues had used theterm “default condition” to refer to an ingenious method they had devised
to examine what would happen if participants in an experiment were notable to come to an agreement using a unanimity rule.
I thought their “default condition” was the same as the concept referred
to as a “reversion level” by Niskanen (1971) and by Romer and Rosenthal(1978). In chapter 7, we discuss the need for all aggregation rules to include a condition stating what decision will hold if multiple participants
do not reach an agreement. We call that rule a “lack of agreement” rule.
What was neat, I thought, was that Plott and his colleagues had actuallytested the impact of changing this rule in the laboratory. They found thatthe particular “lack of agreement” rule they used “literally determines theoutcomes in processes such as these” (Grether, Isaac, and Plott 1979, V7). Their experimental results thus strongly conﬁrmed Romer and Rosenthal’s argument in an earlier “theoretical debate” between them andNiskanen about the likely results when agents did not agree to a proposal
under different “reversion levels.”4 Even with Plott’s substantial acuity
and awareness of the importance of studying rules, he was surprised andpleased when I pointed out the similarity. What I dream of is a method
for classifying rules that enables scholars to know when they are talking
about the same “variety” of a rule—in this case, the default condition ofan aggregation rule.
To illustrate what I mean by a generic rule, let us take an everyday
example of the wide variety of a speciﬁc rule used in practice—sale ofWHY CLASSIFY GENERIC RULES?

183

alcoholic beverages. Most state legislatures have passed legislation related
to the sale of liquor—particularly related to who can legally hold theposition of a Buyer of alcoholic beverages. Using the ﬁrst generic entry
rule enumerated in chapter 7, one would specify a generic entry rule forbuying liquor as:
Persons with attributes a1 , a2 , . . . an , Permitted to hold the position of a Buyerof alcoholic beverages subject to conditions, c1 , c2 , . . . cn .

What attributes and conditions are used varies widely from one place
to another. To buy liquor at a liquor store, the attributes usually relate toage and the kind of identity card that may be required. The conditions
relate primarily to the days of the week and the hours that liquor storesare authorized to be open. To even enter a bar, as contrasted to a liquor
store, in some jurisdictions, the attributes of a Buyer must also be held byanyone who enters the facility. Younger siblings can accompany older
brothers or sisters when buying liquor at a grocery store, but not at a
local pub. If one were to enumerate all of the variations in attributes andconditions just for holding the position of being a Buyer of major types ofcommodities (bread, prescriptions, pork, liquor, cars, etc.), the list would
become incredibly long very fast. For some purposes, such as trying to
improve policies related to the issuing of food stamps or reducing adolescent alcoholism, one could identify an important subset of all of theserules to analyze where they have been used, what other factors affect thedesired outcomes, and what kind of performance has been achieved.
The speciﬁcity of the rules that one studies depends on the question one
wants to ask. The generic rules are not speciﬁc in terms of identifying theparticular attributes or conditions that affect a type of rule. When a research team at the Workshop in Political Theory and Policy Analysis wasstudying the rules used to regulate appropriation from common-pool resources around the world and how they affected behavior and outcomes,
we were interested in developing a full enumeration of the speciﬁc attributes and conditions described in the case studies that we read and coded.
As discussed in chapter 8, we did ﬁnd a shockingly large number of attributes and conditions used in entry rules. For example, for the ﬁrst entry
rule discussed above, we identiﬁed seven ascribed attributes, two acquired
attributes, four residency attributes, and thirteen conditions that are used
individually or in combination to make an individual eligible to access acommon-pool resource (see table 8.1).
The purpose of classifying generic rules in chapter 7, however, is not to
be exhaustive in regard to detailed attributes and conditions. No generalpurpose language for classifying rules can do this! A key task is to provide
the equivalent of the components of a general recipe for creating situations. A good cook knows, for example, that to make a cake requires

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some proportion of ﬂour, sugar, fat, spices, a leavening agent, and time
and temperature at which a baking pan should remain in an oven. Thatis the “general recipe” for making cakes. Pies have quite a different “general” recipe. Grandmother’s secret recipe for Chocolate Surprise Cake,on the other hand, lists speciﬁc quantities of particular ingredients, thetype of pan that should be used, and the temperature and time of baking.
What is “secret” is the particular type of spices or sugar, some combination of ﬂours, something about the timing and process of mixing, or anyof a wide variety of other details that makes her cake something special
that her grandchildren ﬁght over. The purpose of the classiﬁcation systemoutlined in chapter 7 is not to get to this level of detail, but rather toprovide a general classiﬁcation system that can be used by multiple scholars who are analyzing a speciﬁc question and examining rules in order toaddress this question.
By identifying the generic structure of entry rules, all of the many ascribed and acquired characteristics that could be used in a speciﬁc entry
rule are considered together as a diverse set of attributes. Similarly, all of
the various relevant events—such as the results of a prior situation—aresimply considered as “conditions.” Thus, the classiﬁcation proposed inchapter 7 identiﬁes the most general structure of a rule conﬁguration.

The Role of Rules as Information Transformation Mechanisms
Once we have developed a way of expressing rules in a systematic fashion,
we can begin to address a number of quite important and exciting questions. One of these has to do with the generative capacity of rules—theirproductive and reproductive capacities. If we consider institutional rules
to have a broad similarity to grammatical rules, questions concerning theinformation-processing capabilities of institutional rules are similar to
those of a grammar.
Any mechanism that transmits information about how to produce
something (a protein, a sentence, or an action situation) is itself subject
to noise, to random error, and to distortion. Institutional rules are probably more vulnerable to these problems than either grammatical rules(since humans are motivated to try to make themselves understood andthus to follow grammatical rules) or genetic codes (since these “instructions” do not rely on humans themselves to carry them out, and biologists
have ascertained that the mutation rate is actually very low).
Rules that are repeatedly found in many different types of situations
are apt to be more reliable “building blocks” than rules that are only
infrequently included among the set of rules constructing social arenas.
Once we have a systematic way to classify generic rules, we can study

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185

diverse institutional arrangements in a manner to identify those rules thatare components of a large number of situations but that have a surface
appearance of being different. That may begin to give us some clues concerning the use of redundancy and of the iteration and reiteration of arule to make much more complex structures.

An Underlying Universality?
Another major reason for this effort is my deep conviction, as I state inchapter 1, that underlying the immense variety of surface differences, allrepetitive situations faced by human beings are composed of nested layers
composed from the same set of elements.
Game theory has already provided us a formal language for expressing
the structure of relatively simple and unambiguous action situations.
Some of the essential working parts of an action situation—such as positions (as distinct from players) and outcomes (as distinct from payoffs)—
are frequently not acknowledged when a game theory textbook lays out
the working parts of a game. Expanding these parts—as was accomplished in chapter 2—enables us to be more conﬁdent that we have identiﬁed the components of a wide diversity of situations found in all sectorsof organized life. This improves analysis in several ways. First, it requires
the analyst to make hidden or implicit assumptions overt and explicit.
Second, it provides clear elements that need to be generated by rules (orby physical laws). By not requiring that all situations must be expressed
as a formal game, the concept of an action situation expands the rangeof situations that can be compared using the same structural variables.
The attempt to ﬁnd a way of expressing the most basic rules that generateaction situations will help us illustrate that the generic rules used to constitute a market come from the same set of generic rules used to constitutea legislature, a hierarchy, a self-organized resource governance system, or
any of a wide diversity of situations. Having now discussed in some depth
the “why,” I think it is important to deﬁne generic rules. Let us move onto the task of classifying rules by their AIM.

SevenClassifying Rules
E L I N O R O S T R O M A N D S U E C R AW F O R D

THE PURPOSE of this chapter is to develop a useful system for classifying
or naming rules. In our effort to group rules into useful classiﬁcations, werecognize that no single classiﬁcation can ever be useful for all purposes.
One strategy that is commonly used is to order rules according to thejurisdiction that created them. All national rules are classiﬁed together,state or provincial rules are a second group, and local rules are a thirdgroup. This is a useful ﬁrst cut when one is studying the similarities ordifferences among multiple domains of a legal system, but does not address how to change action situations within a jurisdiction. Another strategy examines how a rule came into being. De jure rules that have beenformally authorized by the legitimate form of government in a particularjurisdiction are considered as different from de facto rules that are actually used by participants. Alternatively, some scholars distinguish between
rules that have evolved versus those that have been designed. As institutional analysts, however, we need to devise a method that draws on thegeneral Institutional Analysis and Development (IAD) framework to help
link rules to the action situations they constitute.
Thus, the core goal that we seek in developing a useful classiﬁcation
system for rules is to devise a nested set of rule-concepts that facilitatebuilding a cumulative body of theoretically and empirically tested research about human behavior and outcomes in diversely structured situations. Current tendencies to use various dichotomies (government versus
market, public versus private, formal versus informal) instead of systematic development of empirically supported theories of human behavior indiversely structured situations is, we strongly believe, inadequate to thetask.1 We do not pose our classiﬁcation system as a universally applicablesystem, but rather as a useful system for those interested in linking rules
and the action situations (games) created by rules, the biophysical world,and communities (see Burns and Gomolińska 1998 for a related effort).
In this chapter, we posit two conceptual approaches that we have foundmost useful in classifying rules. The ﬁrst approach—and the one on which
we will focus in this chapter—uses the direct AIM of a rule (as deﬁned inchapter 5) as a method for classifying rules. This method focuses on one

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187

level of action—operational, collective choice, or constitutional choice—and classiﬁes rules by the part of the relevant action situation that is mostdirectly affected. We refer to this as the horizontal approach. The second
layer focuses on the level of authority involved in an analysis, which werefer to as the vertical dimension. Here, we are initially inspired by thework of John R. Commons ([1924] 1968), who distinguished between
authorized and authoritative relationships. As discussed in chapter 2, theIAD framework further divides authoritative relationships into those withthe authority to affect operational action situations, called collectivechoice rules, and those with the authority to affect collective-choice situations, called constitutional-choice rules (see ﬁgure 2.3).
While analyzing rule sets at multiple levels (the vertical approach) helps
us to uncover important authority relationships, to get to a diagnosis fora particular policy situation, we need to sort the rules at any one level
into basic categories that clarify the links between speciﬁc rules at thatlevel and the structure of the resulting situation (the horizontal approach).
Although this chapter focuses on the ways in which rules directly inﬂuence
the structure of an action situation, we need always to bear in mind thatthe rules operate alongside the biophysical world and the attributes of thecommunity and that rules can have indirect effects. So, while the discussion in this chapter hones in on the direct link between rules and actionsituation components for the sake of sorting rules by their inﬂuence on
different parts of the action situation, the emphasis on rules should not
be taken to imply that rules alone determine these structures.

The Horizontal Approach: Classifying by the AIM of a RuleAs we develop in chapter 5, all regulatory rules have the generalsyntax of:
ATTRIBUTES of participants who are OBLIGED, FORBIDDEN, OR PERMITTED to ACT (or AFFECT an outcome) under speciﬁed CONDITIONS,OR ELSE.

Using this syntax as the basis for classifying rules still leaves multiple ways
to sort rules. One could, for example, use the DEONTIC element (must,
must not, or may) as the foundation for a classiﬁcation system. All therules forbidding something would be classiﬁed together. Given that the
deontic operators are interdeﬁnable, however, there does not appear to be
any particular usefulness to be derived from such a classiﬁcation system. If
a rule were originally classiﬁed as a forbidden rule, it could be rewritten
using the permitted or obligated operator.

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CHAPTER SEVEN

Another possibility would be to classify by the “OR ELSE” part of arule. For some purposes, this would be useful. One could learn rapidly
what kind of rule infractions in a particular jurisdiction carry a life imprisonment sentence, a ten-year sentence, or a one-year sentence. For many
purposes, however, classifying by the OR ELSE component of a rule does
not get at the substance of the rules.
It would also be possible to organize a classiﬁcation system using the
ATTRIBUTES or CONDITIONS sections of a rule. This would again be
useful for some purposes. Young people could potentially look up andﬁnd out what they are allowed to do or not do. One could ﬁnd out whatrules apply if you own property versus renting it, or the rules that apply
during the period between a party-nominating convention and the generalelection. The CONDITION does play an important role in assessing
which rules apply in a given action situation. Consequently, attention to
the CONDITION can help analysts sort out those rules relevant to a given
action situation from those that are not. However, it still leaves us with amyriad of relevant rules that need further sorting. It does not get us far
enough to solve the policy analyst’s need to know how to repair broken
action situations!
If one wishes to use the syntax as a foundation, this leaves one with the
AIM element of a rule to be used. And this is our plan. Although (as wenote in chapter 5) the syntax ﬁts regulatory rules better than generative
rules, generative rules still do have an AIM, so a sorting mechanism thatuses the AIM works for generative rules too. And, it works for all threelevels of the IAD framework.
Chapter 2 identiﬁed the components of action situations that areused to construct a wide variety of analytical models of markets, families, hierarchies, legislatures, corporations, neighborhood associations,common-property regimes, as well as all formal games. The elements
are participants, positions, actions, outcomes, information, control, andcosts/beneﬁts. They are related together in the following manner:
Participants and actions are assigned to positions.
Outcomes are linked to actions.
Information is available about action-outcome linkages.
Control is exercised over action-outcome linkages.
Costs and beneﬁts are assigned to action-outcome linkages.

Participants, who can either be individuals or any of a wide diversity oforganized entities, are assigned to positions. In these positions, theychoose among actions in light of their information, the control they haveover action-outcome linkages, and the beneﬁts and costs assigned to actions and outcomes.

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CLASSIFYING RULES

Boundary
Rules

PARTICIPANTS

Information
Rules

Aggregation
Rules

INFORMATION
about

CONTROLoverassigned to
Position
Rules

POSITIONS

assigned to
ChoiceRules

ACTIONS

Linked to

POTENTIALOUTCOMES

Scope
Rules

NET COSTS
AND BENEFITS
assigned to

Payoff
Rules

Figure 7.1 Rules as exogenous variables directly affecting the elements of an action situation.

The relationships among the various parts of the action situation are
represented within the square on ﬁgure 7.1. When an analyst takes eachof these working parts as givens, no further inquiry is made as to the cause
or source of a particular element. The analyst predicts the likely outcomesbased on the model of individual behavior assumed and potentially evaluates the expected pattern of actions and outcomes using such criteria as
efﬁciency, equity, and error proneness.
The analyst examining a one-shot social dilemma among participants
who cannot communicate will predict low levels of cooperation (or noneat all). To dig under that situation, however, to think about changing it,
one needs to know a lot about the underlying structure leading to thesocial dilemma. Are the participants a set of prisoners who are being held
apart by a prosecutor trying to get them to confess? So long as the prosecutor does not use illegal methods and allows the prisoners access to an
attorney, what is inefﬁcient for the prisoners is likely to be socially efﬁcient. Better to leave the social dilemma as it stands.
If the participants are harvesters from a common-pool resource, on theother hand, and are led by isolation and individual incentives to overharvest, the policy analyst needs a coherent scientiﬁc language to begin
to think about rules that would change this situation. The recommendation simply to create “private property” (Demsetz 1967; R. Smith 1981;
L. Anderson 1995) does not tell an analyst anything speciﬁc about whichrules could be changed to make the situation one of private property (see190

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Rose 2002 and Tietenberg 2002 for good analyses that dig into this question). Do new positions need to be created—owner versus outsider, forexample? How are individuals chosen to become owners? What actions
may an owner take, and what actions are forbidden? These are the kindsof questions that we ask at the end of this chapter regarding how to transform the Snatch Game into a more productive game by assigning property
rights. It turns out that we have to change seven rules in order to modify
this very simple game of nature from one without any rules to one withsome property rights deﬁned. Focusing on the direct link between the
AIM of a rule and the affected component of the action situation classiﬁes
rules in a way that facilitates analysis of structural change.
The action situation mechanism for classifying rules groups rules according to the element in the action situation that they most directly impact. Many rules also indirectly affect other components. Figure 7.1
shows the names that we have given to the types of rules based on thiscoding scheme. The arrows identify the element of the action situation
directly affected by that type of rule. We also need to remember that twoother major categories of exogenous variables (the biophysical world andattributes of community) also shape these action situation components.
Using the AIM of a rule for classiﬁcation leads to the speciﬁcation ofseven broad types of rules: position, boundary, choice, aggregation, information, payoff, and scope.2 Position rules create positions (e.g., memberof a legislature or a committee, voter, etc.). Boundary rules affect how
individuals are assigned to or leave positions and how one situation islinked to other situations. Choice rules affect the assignment of particularaction sets to positions. Aggregation rules affect the level of control that
individual participants exercise at a linkage within or across situations.
Information rules affect the level of information available in a situation
about actions and the link between actions and outcome linkages. Payoff
rules affect the beneﬁts and costs assigned to outcomes given the actions
chosen. Scope rules affect which outcomes must, must not, or may beaffected within a domain.
The direct relationships among rules and the components of an action
situation are shown in ﬁgure 7.1 as the set of arrows connecting rules tospeciﬁc parts of an action situation. We can also think of a general verb
type that links AIMs directly to different parts of the action situation.
These basic verbs assist in sorting out the various types of rules. A ﬁrst
assessment in sorting the rules that affect an action situation can be todetermine the general verb that most closely resembles the speciﬁc verb
in the AIM of the parsed rule. The basic verb can also be used to translate
diverse speciﬁc verbs in rules to general verb types for the sake of uncovering similarities in rules that, on the surface, look different. Two bound-191

CLASSIFYING RULESTABLE 7.1
The AIM component of each type of rule
Type of rule

Basic AIM verb

Regulated component of
the action situation

Position
Boundary
ChoiceAggregation
Information
PayoffScope

BeEnter or leave
Do
Jointly affectSend or receive
Pay or receive
Occur

Positions
Participants
Actions
Control
Information
Costs/Beneﬁts
Outcomes

ary rules from two different action situations, for example, might usevery different language but may ultimately be identical mechanisms forregulating how a participant exits the position “voting member.” Table
7.1 lists the basic AIM verb associated with each type of rule.
The discussion of classifying rules by their AIM follows the order ofthe list on table 7.1, which starts with position rules and ends with scope
rules.3 These categories simultaneously sort rules for comparative analysis
and identify the immediate link between the rule and a component of theaction situation. However, rules operate together as a conﬁguration. The
clean arrows from one type of rules to one part of the action situation inﬁgure 7.1 should not be taken as an assumption that these rules operate
independently of one another, or that boundary rules inﬂuence only whoenters positions, or that payoff rules inﬂuence only net costs and beneﬁts.
While the AIM may hit one element of the action situation, other components of the rules—particularly the CONDITION and the OR ELSE component—may impact another component.4 And, one rule may eventuallyaffect other components of the action situation in addition to the component directly affected.
An example of a boundary rule that inﬂuences many other action situation components of an “urban taxi game” is a local medallion ordinance.
Such a boundary rule requires that a taxicab display a purchased medallion
from the city before it can legally use the city streets to attract customers.
It has been used in New York City for decades. When a city limits thenumber of medallions it will authorize, the entry costs for putting a taxi on
the streets rise substantially, but if the medallions are strictly limited, thepotential returns per unit of time also increase substantially. And the timethat a traveler has to wait before getting a taxi ride may increase substantially. Thus, one rule can have very substantial impact on the entire structure of resulting action situations through its direct impact on one of the192

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working components of an action situation. This example also illustrates
another advantage of a systematic method for classifying rules, namely,
to help in identifying rules that are similar in function in widely diverse
settings. The medallion entry rule is a speciﬁc example of a generic boundary rule used in a variety of action situations to limit entry. As we will
discuss in chapter 8, it is one of the entry rules used in some locations toregulate ﬁsheries—requiring ﬁshing boats to purchase ﬁshing permits.
In fact, all rules indirectly impact net costs and beneﬁts since all rules
include DEONTICS that translate into deltas and OR ELSES that generally have payoff consequences. This complexity does not invalidate theanalytical goal of sorting rules by types in order to get a better handle on
the inﬂuence of rules on the action situation components. It does, however, mean that the initial sorting does not identify all of the rules thatindirectly inﬂuence each component. Finding the rules that indirectly inﬂuence each component involves a next step of looking at the other rulecomponents to ﬁnd ATTRIBUTES, CONDITIONS, or OR ELSES thatlink to that particular component.
Thus, classifying rules by their direct effect on an AIM offers an important tool for sorting rules into basic types based on their immediate
impact on the action situation, but it does not cleanly classify all rules
into mutually exclusive categories based on the component of the action
situation that is eventually affected. Since rules can often inﬂuence morethan one part of an action situation, this simply cannot be done at thislevel of sorting. This classiﬁcation scheme sorts rules into useful categories
for policy analysis by focusing on the AIM ﬁrst, while recognizing thatfurther sorting may be necessary for more complete speciﬁcation of therules linked to particular components of the action situation.
With caveats aside, we now turn to an overview of each of these seventypes of rules. We concentrate here on identifying the most general aspectsof each type of rule since so many speciﬁc examples of each type of rule
are found in practice. We thus focus on what appear to be the most basic
or generic forms of each type of rule affecting diverse action situations.
The language of rules is, like all of the analytical concepts discussed inthis book, hierarchical in structure. The generic rule forms are special
cases of the seven basic types of rules, and ﬁner rules are elaborations andspecial cases of these generic rule forms that we lay out here. Chapter 8
provides examples that illustrate the immense variety of speciﬁc rules bydrawing on empirical studies of rules related to common-pool resources
to elaborate the immense variety of speciﬁc rules found to exist in a studyof the governance and management of irrigation, inshore ﬁsheries, andforest institutions around the world.

CLASSIFYING RULES

193

Position Rules
An initial building block of an action situation is the set of positions oranonymous slots that are ﬁlled by participants and to which speciﬁc action sets are assigned at junctures in a decision process. Position rules
create these positions. Position rules are often not by themselves intrinsically interesting, as they merely create “holders” for participants to enterand for the speciﬁcation of actions that participants in positions can takeat speciﬁc nodes in a process.5 As discussed in chapter 2, positions are theconnecting link between participants and authorized actions. Thus, theycreate the scaffolding for a wide diversity of situations that can vary signiﬁcantly in terms of the number of positions and the relative authority
assigned to each position.
A minimal position rule names a single position or slot—such as “member”—as the most inclusive position to be held by all participants in asituation. In the initial Commons Dilemma experiments described inchapter 3, the experimenters generated only one position, even though
there were eight participants holding that position. A situation with one
and only one position held by all participants is an internally egalitariansituation even though entry into the position may be difﬁcult. Most situations contain more than a single position, and sets of rules assign different
kinds of authority to those in different positions. The Trust games also
explored in chapter 3 all had at least two positions: Investor and Trustee.
Differentiation of authority to act or to effect outcomes depends upon theestablishment of multiple positions within a situation.
Number of Participants in a Position
A position rule may also state whether there is a deﬁned number, no limit,
a lower limit, or an upper limit on the number of participants who holda position. If all positions in a situation have a deﬁned number of participants, the maximum number of participants is thus also deﬁned. A deﬁned number of participants in positions is used in most recreational
games or competitive sports. It is not legal to play with more or less thanthe deﬁned number of participants in each position on the ﬁeld. The position of a member of a jury must be ﬁlled by a deﬁned number of participants (deﬁned by law, it is frequently twelve jurors, but it varies fromjurisdiction to jurisdiction and according to the type of case involved).
Sometimes a rule states only a lower bound or an upper bound on thenumber of participants in a position. Either bound may be left undeﬁned.
When the lower bound is deﬁned and the upper bound is left unspeciﬁed,

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a minimum number of participants must be present, but not a maximum.
Most quorum rules deﬁne a minimum number of participants who must
occupy a particular position before particular actions must be taken.
When a lower bound is not deﬁned, action can occur without any participants in a particular position. When the upper bound is deﬁned and thelower bound is left unspeciﬁed, a “lid” is placed on the total number ofparticipants that can hold a particular position. An operational exampleof this type of rule is found in the authorizations given to many publicagencies that they can hire up to a speciﬁed number of participants in aparticular position (state trooper, for example). Such rules may or may
not specify whether there is a lower bound. When both bounds are speciﬁed, action may not take place until at least the minimum participants
are in their position and no more than the maximum are present. Most
position rules do not specify conditions, which means that the position
rule holds under any of the likely conditions to be found in the situations.
It is certainly possible that there may be conditions added to a speciﬁc
position rule. For example, a position rule in a school district may raise
the number of teacher aides who may be hired by a school in the condition
that the number of students per teacher rises above twenty.
Where position rules do not exactly specify the number of participants,
it is entirely the operation of the boundary rules over time in conjunction
with the type of goods and attributes of a community that affect the number of participants, their characteristics, and their ease of entry or exit.

Boundary Rules
Boundary rules—frequently called entry and exit rules—deﬁne (1) who iseligible to enter a position, (2) the process that determines which eligible
participants may enter (or must enter) positions, and (3) how an individual may leave (or must leave) a position. Some entry rules, then, specify
the criteria to be used to determine whether an actor is eligible to ﬁll aparticular position. Ascribed and acquired attributes are frequently usedin this type of entry rule. Individuals may have to meet certain physicalstandards, such as height and weight. They may have to meet a certain
wealth standard or pay an entry fee. Individuals may be required to possess a certain range of experiences, to be above a minimum age, to have
graduated from certain schools, to be the descendants of a particulargroup, to possess certain abilities, or to live in certain geographic areas.
Public employment under civil service systems and patronage systems differs substantially in the entry rules that are applicable.
When those crafting a rule hope to increase the skills and knowledge
held by those in a position, they may list one or more acquired characteris-CLASSIFYING RULES

195

tics, such as holding a college degree or passing a test. Exclusive country
clubs may require that a family own a fortune in liquid assets and havesent their children to Ivy League schools to be eligible to be members.
Immense and important issues have and will continue to erupt overwhether one or another attribute should be included in a rule. Whether
race should be used in an entry rule—biased either for, or against, individuals of a particular race—has been a major controversy in the UnitedStates for multiple decades (see Greve 2001).
First-order boundary rules deﬁne the eligibility of individuals to holdthe position of member. These rules include a set of transformations
that partition a deﬁned set of individuals, usually bounded in space andtime, into subsets of individuals who are eligible and ineligible to holdthe position of member. A simple rule to partition the set is that a participant must be eligible if the set of ATTRIBUTES of the participant equals
the required set of ATTRIBUTES and the participant meets speciﬁed
CONDITIONS. The ATTRIBUTES that may be speciﬁed can include ascribed or acquired characteristics (see chapter 8 for examples). An alternative formulation of the same rule is that it is forbidden to keep a person
who has certain deﬁning ATTRIBUTES and has met required CONDITIONS from being eligible to be a member. Since the deontic operators
are interdeﬁnable, the same rule can be stated with any of the deontic
operators. Alternatively, one can state that an individual must not be eligible unless they have certain ATTRIBUTES and have met deﬁned CONDITIONS. An example of a rule stated in this manner is the rule in the U.S.
Constitution (article 1, section 2, paragraph 2) regarding the eligibility of
citizens to be a Member of Congress: “No Person shall be a Representative who shall not have attained to the age of twenty ﬁve Years, and been
seven Years a Citizen of the United States, and who shall not, whenelected, be an Inhabitant of that State in which he shall be chosen.”
Second-order entry rules deﬁne how the set of eligibles are further partitioned into subsets of position-holders and nonholders. Nonholders here
are narrowly deﬁned to include those individuals who would be eligible
to hold the position of a member in a particular situation but do nothold that position. Boundary rules are open when they allow eligibles fullcontrol over the decision whether or not they wish to hold a position.
Most election laws within the United States, for example, are open andallow eligible voters (those who are above a deﬁned age and have registered, etc.) to decide whether or not to come to the polls to vote. Alleligible voters who appear at designated places and times are authorized
to participate in an election. Variables such as the length of residency
required for eligibility, registration procedures, absentee voting rules, andnumber of hours allowed for voting on election day combine to make the196

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act of voting more or less expensive in terms of the amount of time, effort,
and knowledge required to be legally registered prior to election day.
Boundary rules are invitational when they authorize holders of a position to select future holders from the set of those who are eligible. Thus,
eligibles are divided into two sets—those who have been invited and those
who have not been invited. Entry requires an invitation. Many private
clubs use a rule of this type. Most businesses and public bureaus also use
rules of this type with invitations taking the form of job offers.
Closely related to an invitational entry rule is a competitive rule whereparticipants are selected as an outcome of another action situation inwhich potential members compete against each other to gain entry. In a
competitive election, entry requires receiving the most votes (or someother deﬁnition, depending upon the aggregation rule used in the election
situation). The selection of legislators is normally the outcome of a separate election in which members are selected by voters from among candidates who are running in this election.
Boundary rules are compulsory when eligibles have no control overwhether they ﬁll a position or not. When focusing on broad jurisdictions,the most inclusive class of individuals within a jurisdiction is its subjects—
those subject to its rules. The status of being a subject is not conferred bychoice when the jurisdiction is a general-purpose governmental unit suchas a city, county, state, or province, or the largest, general-purpose jurisdiction, a nation. Subjects are nonvoluntary members of a public instrumentality. A voluntary member of an organization in the private domainof life exercises choice concerning whether or not to join (subject to eligibility criteria) but is subject to the rules of the arrangement while remaining a member.
A suspect arrested by the police has no choice as to whether to participate or not in court proceedings concerning charges made by the state.
The suspect has limited choices: what to plead, whether to hire a lawyer,
whether or not to testify on his or her own behalf. A suspect in a criminalcase cannot take any action independently to exit from the process.6 Compulsory rules oblige anyone who meets certain CONDITIONS to hold aposition. Being drafted into the army or subpoenaed to serve on a jury
involves the selection of a member by a formal process outside the control
of any individual draftee. A person selected as a defendant in a criminal
trial has entered this position through a compulsory process. Under universal compulsory entry rules, participation is required for all those whoare eligible to participate. All eligible taxpayers, for example, must complete tax forms and pay any taxes they owe. A compulsory entry rule isparticular when only a subset of eligibles must become members at any
one time period. Both the draft and jury duty are particular entry rules.

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197

Boundary rules may also assign fees for entry and/or exit. Entry rulesthat are open, invitational, or competitive may assign a fee or inducement
to any eligible individual who wishes to enter a position. An example isan application fee or reward. Poll taxes are fees that eligible voters had
to pay in order to vote at an earlier juncture in the United States. An
example of the second is a membership fee or reward. Many competitions
also require a fee before entry.
Boundary rules may deﬁne high licensing fees or large bonding requirements for entry or exit. These rules also interact with the physical world
and attributes of the community to shape the dynamics of entry and exit
into positions. A fee of $1 per hour to use a tennis court may be considereda low entry cost in a middle- or upper-class community, while in a poor
community such a fee may preclude the entry of many potential users.
The level or strictness of entry and exit costs is relative to the availabilityof an attribute or a resource in a community.
Governmental jurisdictions can also increase entry and exit costs via
other kinds of boundary rules such as certiﬁcation procedures with manyrequirements imposed on all potential entrants, or a limitation on thenumber of enterprises licensed (which makes the license itself a very valuable good like a liquor license or taxi cab medallion). So, one way of
increasing strictness involves more stringent requirements. However,entry costs are also affected by the production technology of particular
goods. When a high ﬁxed investment is required to produce any quantityof the good at all, entry costs are relatively high, and the conditions of acompetitive market are rarely met.
Rules Related to Multiple Positions
When situations involve more than a single position, a mix of position
and boundary rules together deﬁne relationships between those positions.
Some rules create multiple positions (position rule), require each participant to hold one of them (boundary rule), and forbid the holding of morethan one position (boundary rule). Such a rule set is used in many recreational sports. Positions such as pitcher and hitter are deﬁned. Each position is ﬁlled by at least one member. All members hold one, and only one,
position. Such a rule set covers and partitions the set of participants inthe situation. Another rule set is used in many committee settings to assign
one and only one member a unique position, such as the chair of thecommittee, and to assign all other members to the other position. The
U.S. Constitution provides an example in article 1, section 6, paragraph
2: “No Senator or Representative shall, during the Time for which he waselected, be appointed to any civil Ofﬁce under the Authority of the UnitedStates, which shall have been created, or the Emoluments whereof shall

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have been encreased during such time; and no Person holding any Ofﬁce
under the United States, shall be a Member of either House during his
Continuance in Ofﬁce.”
Alternatively, the rule set may assign a member to at least one positionand permit members to hold multiple positions. Such a rule is used inmany organizations when members may hold several different positions
simultaneously. A member of a ﬁrm may hold a position as a particular
worker (secretary, foreman, president) as well as a position related totenure in ofﬁce (a probationary versus permanent staff member). This
rule set covers, but does not partition, the set of participants.
Succession Rules
Boundary rules may also deﬁne eligibility for entry to positions in termsof rules that deﬁne who is eligible to move from one position to anotherand what criteria must be met, often called succession rules. Civil service,seniority, and patronage institutional rules differ primarily in the procedures used and criteria applied in regard to succession of individuals intohigher-level positions. In a civil service or “merit” system, those who are
already employees must serve speciﬁed periods of time at lower-level positions and pass examinations in order to be placed on an eligible list for
promotion to higher-level positions. When a seniority rule is used, individuals who have been in a particular position for the longest period of time
are selected to move into higher-level positions when vacancies occur. Decisions about upward mobility in a patronage system are made by individuals who hold the position of “patrons.”
Exit Rules
While entry rules deﬁne who is eligible to enter a position and who has
control over entry, exit rules deﬁne the conditions under which a participant must, must not, or may leave a position. In two-player repeatedsocial dilemma games, the capability to exit—leave the situation and theposition of player—has consistently been shown to make a big differencein the rate of cooperation reached over time (Orbell, Schwarz-Shea, andSimmons 1984; Schuessler 1989; Vanberg and Congleton 1992). Exit
allows a participant to extract themselves from having to make a choice
between defecting on someone else or being a sucker when the other participants defect. Defendants in a criminal trial or a prisoner may not leavesuch a position at their own initiative. The results of a trial may be torelease the defendant (allowing the participant in this position to get outof this position). After a deﬁned period of time has passed, a prisoner may
be released from this position by a parole board or may have simplyCLASSIFYING RULES

199

served a set term and be automatically released. A citizen of a nation
may not have full control over leaving this position. Many countries haveplaced severe constraints on the capabilities of citizens to exit voluntarily.
Rules sometimes set ﬁxed terms of ofﬁce with stringent rules concerning
the eligibility of a past position holder to be eligible to hold the same
position again. Governors of some states and mayors in some cities may
hold these positions for one term only or may not be eligible to succeed
themselves (even though they may later be eligible after someone else hasserved in the ofﬁce). Under such circumstances, the person in the position
has no control over retaining the position after the ﬁxed term has expired.
For most elected positions, voters have full control over a participant’s
continuation in ofﬁce. Positions like those of judges, however, may be forlife, subject only to removal for illegal or immoral behavior. Participantsin such positions are assigned very high levels of control over when andunder what conditions they leave ofﬁce.
The rules related to many positions, however, give both the occupantand others partial control over whether the occupant continues. Except
under slavery or imprisonment, occupants of most positions are allowed
to exit or resign from positions at their own initiative.7 (Particular rules
may set a limit on the amount of time that must elapse from announcing
a decision to leave and actually leaving or may set a charge associatedwith leaving a position prior to fulﬁlling some aspects of a contract.) The
capability of a participant to leave a position is a fundamental limit on
the power that other participants can exert over a participant.
In regard to civil proceedings, the plaintiff has an initial voice regarding
the instigation of legal proceedings and also a choice concerning theirtermination. If the plaintiff wishes to terminate the case by dropping thecharges, the plaintiff will probably have to pay court charges, attorneyfees, and other costs, but can otherwise freely exit. Upon termination ofmany civil cases, one or all of the parties are usually assigned court andattorney fees as part of the costs of the use of the institutional arrangements. The costs of exit here may be very high. Litigants may use thepotential costs of a trial as a threat to reach a negotiated settlement in
the shadow of the court. When there is sufﬁcient ambiguity as to theoutcome of the trial, both sides may be willing to negotiate outside thecourtroom in order to reduce the possibility of losing in court and paying
the exit costs.
While the holder of a position in most situations may be able to exit
voluntarily, others may also have greater or lesser control over whether
the person continues in or leaves the position. Prior to the establishmentof civil service legislation in many states, appointed local public employees could be easily removed from ofﬁce by elected ofﬁcials. Changes in
the party of locally elected ofﬁcials frequently meant that public employ-

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ees hired by the other party were ﬁred and new workers loyal to the incoming party were hired. Civil service legislation changed the relative“rights” of public employees to their positions. No longer could they beﬁred at will or for lack of political loyalty and activity. After an initialprobationary period had expired, a public employee could not be ﬁred
except for “cause.”
Collective bargaining agreements also affect the relative control thatvarious participants have over exit from positions. Under such contracts,
the terms and conditions of employment and ﬁring for an entire set of
positions are negotiated at the same time. Grievance procedures may beinstituted to provide a forum and procedure for a participant who wishes
to appeal an involuntary termination. A participant holding the positionof a boss may be forced to reemploy an employee (or provide compensation) if a termination is not considered by the grievance panel to have been
within the power of the boss. Contracts often specify rights to positions
according to seniority, which limits the power of a boss to select which
employees will be terminated during times of ﬁnancial restrictions. Under
seniority rules, the last person hired into a position is the ﬁrst to be laidoff, regardless of work performance.

Choice Rules
Choice8 rules specify what a participant occupying a position must, must
not, or may do at a particular point in a decision process in light of conditions that have, or have not, been met at that point in the process. The
actions that participants must, must not, or may do are dependent bothon the position they hold, prior actions taken by others and/or themselves,
and attributes of relevant state variables.
Rules with action AIM’s partition possible actions in an action situation into required, permitted, and forbidden acts dependent upon the pathof past actions taken by participants and others and readings on relevant
state variables. Since the classiﬁcation system focuses on the most direct
link between the AIM of a rule and the components of an action situation,
not all rules with action AIMs fall into the choice rule category. Whenthe action of an AIM relates directly to entry or exit from a position,
giving or receiving information, joint control over a decision, or giving
or receiving payoffs, then the rule is a boundary rule, information rule,
aggregation rule, or payoff rule, respectively. Choice rules partition allother actions that do not ﬁt into those speciﬁc parts of the action situation.
In complex situations structured by complex systems of rules, however, asystem of choice rules may not completely partition all other possible
actions into required, permitted, or forbidden actions. The partitioningCLASSIFYING RULES

201

of actions can also be complicated by complex sets of rules that may
be inconsistent in their ordering of actions with different rules assigning
different DEONTICS to the same action. One rule may forbid an action,
while another rule requires that same action.
Reference to a jurisdiction usually occurs in either the CONDITIONof the rule that states when a particular action is forbidden, required, or
permitted or in the ATTRIBUTES of a rule that deﬁne to whom a particular rule applies. CONDITIONS may specify where and at what time actions taken by individuals are considered to be within the boundaries ofa particular jurisdiction with the attendant beneﬁts or sanctions thatmight result. If a carpenter injures a hand while hammering a nail on thejob, the carpenter may be eligible for Worker’s Compensation because of
the institutional arrangements encompassing the activities of the workplace. Other individuals have a duty to pay the worker for his injury. The
same injury inﬂicted at home on an evening hobby project will not entitle
the carpenter to any compensation. Similarly, the penalty for some acts,
such as theft, varies radically from jurisdiction to jurisdiction. Convictions for stealing property owned by the federal government in the UnitedStates may carry a much stiffer sentence than conviction for stealing private property.
In many bureaucratic action situations, no one participant is authorized
to take particular positive actions unless speciﬁc state variables are abovesome minimum or below some maximum. A power-plant employee, forexample, may not be authorized to open a turbine unless water levels
are above a minimum. A social worker cannot authorize food stamps or
welfare payments unless an applicant’s income is below some deﬁned level
given the size of the family and other conditions. Further, speciﬁc procedures must be completed prior to any determination of the eligibility ofa family for welfare payments of any kind.
By widening or narrowing the range of actions assigned to participants,
choice rules affect the basic rights, duties, liberties, and exposures of members and the relative distribution of these to all. Choice rules may allocate
to positions high levels of control over many different state variables; inother words, authorize powerful positions. Choice rules empower, but thepower so created can be distributed in a relatively equal manner or agrossly unequal manner. Choice rules thus affect the total power createdin action situations and the distribution of this power.
One particular type of choice rules, agenda control rules, proves to bequite important in legislative games. Such agenda rules limit or expand
the authority of participants in particular positions to propose particularactions (see Shepsle 1979b; Plott and Levine 1978). A closed agenda control rule limits the number of alternative actions that can be decided upon.
An open rule, on the other hand, allows any feasible action to be consid-

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ered. A “germaneness rule” restricts alternatives to those that affect thesame set of state variables (see Shepsle 1979b for further discussion ofthese rules).

Aggregation Rules
Aggregation rules determine whether a decision of a single participant orof multiple participants is needed prior to an action at a node in a decision
process. In many social games, particularly board games, each participant
is authorized to make a move when it is his or her turn. The player’s
action set at that juncture includes the speciﬁc physical moves to be made.
While no single player fully controls the ﬁnal outcome, individual players
do control the decisions to be made at individual nodes. However, in legislative and other group action situations, multiple participants jointly control which actions will be taken at nodes in the decision tree. The decision
whether to amend or not amend a bill is subject to the joint control ofthe members of a legislature. Individual participants affect that decisionby casting votes that are then aggregated by an aggregation rule. No single
participant has full control over the move to amend or not amend the bill.
Aggregation rules are necessary whenever choice rules assign multiple
positions partial control over the same set of action variables. The problem that aggregation rules must clarify for a group is “who is to decide”
which action or set of activities is to be undertaken. Thus, in any action
situation in which multiple members could each potentially have partial
or total control over the selection of an action at a decision node, aggregation rules are used to determine who will participate in the choice, howmuch weight each participant will have relative to others, and the speciﬁcformula to be used in adding up the contribution of each person’s decision
to a ﬁnal decision about the action. There are many different types ofaggregation rules. Levin and Nalebuff (1995) identiﬁed sixteen distinct
methods for aggregating individual decisions into ﬁnal electoral decisions.
Three major generic forms of these rules are nonsymmetric aggregation
rules, symmetric aggregation rules, and rules that deﬁne outcomes in casesof nonagreement.
Nonsymmetric Aggregation Rules
All nonsymmetric aggregation rules treat the participants in a situationdifferently in regard to some decision to be made at some point in a decision process (Strafﬁn 1977). Some named individual or named subgroupis designated as the participant(s) who make the decision for the group.
Karotkin and Paroush (1994) analyze nonsymmetric aggregation rules
for a group of four participants and analyze six different nonsymmetricCLASSIFYING RULES

203

rules that vary in regard to the weights given to players. When only asingle person is assigned full authority to select the action, the person
can be called an “expert” (or a dictator) for that decision. The dictator
picks which action will be taken by the group. Such a rule gives a single
named individual the capacity to select any of the feasible actions as wellas to avoid any of the feasible actions—full active and blocking capacity.
The individual holding that position can act or make an authoritativedecision without gaining the prior agreement of others. An individual
holding such a position may, of course, consult with others prior to action.
Unless regular expectations have been established about the rules used toaggregate the expressed preferences of others, such consultation is not
required by the aggregation rule. A single player with veto power may
not be able to direct choice as fully as a player with expert power, but thepower of such a participant is very substantial (Tsebelis 2002; Herzberg
and Ostrom 2000).
A subgroup may be named from the full group and assigned the capacity to make a decision about actions for the entire group. The subgroupwill need an aggregation rule of its own in order to make its decision.
Such a decision rule may be called an oligarchy rule. The full set of participants may participate in the decision, but each individual participant may
be assigned a “weighted vote.” This type of nonsymmetric aggregation
rule is used in some types of special districts where each member of acouncil votes, but each is assigned a set of votes depending upon someformula. A subgroup may be named from the full group and a decisionmust be agreed to by this subgroup, as well as by the full group, using
one or more aggregation rules. Such a rule would be associated with many“committee” arrangements wherein committees must approve legislation
before the full body gets a chance to approve the legislation. Members ofsuch a committee have greater voice in determining group actions.
On the other hand, an aggregation rule may grant certain positions lessvoice in group decisions. In most legislative action situations, a presidingofﬁcer is not given the authority to vote on most decisions. Thus, the actof voting is not among the presiding ofﬁcer’s set of permitted actions atmany points in a decision process, and so his or her vote does not countin the aggregation of a regular voting process. However, if a tie occurs ina vote by regular members, the presiding ofﬁcer is then authorized to votein order to break the tie.
Symmetric Aggregation Rules
Symmetric aggregation rules assign joint control over an action to multiple participants so that all are treated alike. One symmetric aggregation
rule is that of unanimity—everyone must agree prior to action. A unanimity rule may be built into a process in such a manner that participants do

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not self-consciously “vote,” but each is required to agree before an action
can be taken. A bank clerk, for example, is not authorized to open safety
deposit boxes unless the owner of the box or an authorized agent signs aregistration form and produces a second key to ﬁt the box. The dual andequal authority and unanimous aggregation rule are built into the locking
mechanism that requires two keys assigned to different individuals toopen the box. Similar conjunctive authority to act and unanimous aggregation rules occur in the military when the results of action could be
extremely serious for national security. In addition to the necessity ofreceiving positive approval from positions higher in the military hierarchy, taking some actions—such as launching an intercontinental missile—cannot physically be undertaken unless multiple persons are present andall agree.
Once the votes are cast, rules specify what proportion of the total must
be in agreement before an authoritative decision can be made and what
happens if the minimal agreement is not reached. For votes that are
weighted equally, it is possible to conceptualize a simple voting rule asranging from allowing any one member of those given joint authority tomake the decision for the collectivity (the anyone rule—used in callingout public emergency vehicles) to requiring all those given joint authority
to agree prior to a decision (the unanimity rule) (Buchanan and Tullock1962). Between the two extremes of the anyone rule and the unanimity
rule lies a variety of other speciﬁc rules, the most familiar being the requirement that 50 percent plus 1 person agree (majority rule) or somelarge percentage, such as two-thirds or three-quarters (an extraordinary
majority rule). The array of decision rules between these two extremes
can be thought of as the proportions of the persons in the group required
to agree prior to a decision with the most common being majority rule.
Scholars have puzzled why participants in some jurisdictions or legislative bodies have used a simple majority voting rule to create a new situation—for example, to approve or not approve a municipal bond—whileothers use a supramajority vote rule such as two-thirds of those voting.
Messner and Polborn (2004) attack this problem using the example ofbond issues and show that the voters who may form a simple majority attime one may not be in the majority ﬁve years, ten years, or twenty years
later. Voters who expect a change in population composition and preferences related to policy issues over time may well use their simple majority
vote at one time to ensure that the size of the winning coalition in the
future requires a supramajority, precisely because this is a more conservative voting formula. Looking ahead, they fear that their preferences will
be overwhelmed in future votes on costly bond issues if a simple majority
rule is retained.

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205

Lack of Agreement Rules
The formula for determining a joint decision (for either symmetric ornonsymmetric rules) must also include a “no agreement” condition stating what decision will happen if no agreement is reached under a rule.
Whenever a decision depends on the approval of more than one participant, the possibility of no agreement is always present. The no agreement
condition is the reference point for the proposed decision. It states what
will happen if a certain proportion of the participants does not agree toa proposed action.9
Several types of no agreement rules are possible. One type continues
the status quo distribution of outcome variables. A second presumes thatno one receives any outcome variables if participants cannot agree (allrelevant outcome variables are reduced to zero). A third no agreement
rule is to assign state variables randomly. A fourth type of rule is to apply
some external rule (or turn to some external decision maker) to allocate
outcome variables.
As mentioned in chapter 6, Grether, Isaac, and Plott (1979) used three“no agreement rules”—which they called default conditions—in a seriesof laboratory experiments simulating the allocation of landing slots at
airports. The ﬁrst rule was the continuation of the status quo allocationof slots. The second rule was a random assignment of slots. The third rule
was the application of an external rule that would take slots from those
who had the most and give them to those who had none or only a few (aRobin Hood rule!). These three no agreement rules were combined witha unanimity rule. What is interesting about the ﬁndings from a set ofexperiments using each of these no agreement rules, combined with unanimity, is that the results of the experiment were strongly determined bywhich no agreement rule was in use. The committee outcomes were substantially inﬂuenced by the speciﬁc lack of agreement rules used in anexperiment (V-7).
That the speciﬁc lack of agreement rules that Grether, Isaac, and Plottused had such an important impact on the outcomes achieved is anotherillustration of how one small part of a rule conﬁguration can strongly
affect how the other rules impact the action situations. Scholars need to
study the effects of a full rule conﬁguration rather than assume they canstudy the impact of one rule at a time—while assuming that the other rules
are “randomly distributed.” A considerable literature has been generated
about the likely effects of using unanimity rules in different situations.
Very few theorists have explicitly stated the no agreement rule they are
assuming. Yet, one might speculate that once an analyst assumes a unanimity rule, that the most important assumption driving analysis is theaggregation no agreement rule presumed in operation. Implicit assump-

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tions about rules, rather than the explicit rules studied, may have been
the most important drivers of the results in earlier analyses of institutionalarrangements.

Information Rules
An important part of any action situation is the information available
to participants about the overall structure of that situation, the current
state of individual state variables, the previous and current moves of otherparticipants in positions, and their own past moves. Information rules
affect the level of information available to participants. Information
rules authorize channels of information ﬂow among participants, assign
the obligation, permission, or prohibition to communicate to participantsin positions at particular decision nodes, and the language and form inwhich communication will take place. Information rules are particularly
important in generating information about past actions of participantsso that other participants can know who is, or is not, trustworthy (seeJanssen 2004).
Channels of Information Flow
Rules concerning the establishment of information channels relate to theset of all possible channels connecting all participants in a situation. The
connections can be represented as a perfectly connected polygon of whatever dimension equals the number of participants. If there are ﬁve participants, there are nine possible connections between these participants. Information rules partition this set of possible connections into subsets ofrequired (a channel must exist), forbidden (a channel must not exist), andpermitted (a channel may exist). In a paper by Mueller, Chanowitz, andLanger (1983), for example, they conduct several experiments (at the action situation level) of communication patterns under different structuresconnecting ﬁve subjects.
Given that the number of potential communication channels among
any large group is very large and the set of possible rules requiring, permitting, or forbidding channels is also very large, it will be necessary to identify speciﬁc types of channel rules if one wishes to devise a ﬁner classiﬁcation scheme for this aspect of information rules.
Frequency and Accuracy of Communication
In addition to specifying which channels of communication may or may
not exist between positions in a situation, information rules also regulate
the frequency of exchange of information and the accuracy of informa-CLASSIFYING RULES

207

tion. In many action situations, regular reports must be ﬁled containing
certain types of information on either a regular basis or at any time thata participant wishes to obtain certain actions or rewards from others. A
person who is on probation is supposed to report to a probation ofﬁcer
on a regular basis and provide a report about their conformance or nonconformance to a set of rules about the actions that they can or must not
do. Most bureaucratic life is ﬁlled with requirements to complete regular
reports about recurrent events in these organized settings. The accuracy
rules affect what type of indicators may or must be used as evidence about
the state of the world. Rules establishing audit procedures are intended
to enforce the accuracy of ﬁnancial information available to top management and shareholders of a ﬁrm.
Subject of Communication
Information rules often limit the topics that can be discussed among participants. In a courtroom, a witness is forbidden to refer to “hearsay” evidence. In industrial meetings, participants are not supposed to discuss
price-setting decisions. In many laboratory experimental sessions, rules
frequently limit the subjects that participants can discuss with instructions
like the following: “Some participants in experiments like this have foundit useful to have the opportunity to discuss the decision problem you face.
You will be given 10 minutes to hold such a discussion. You may discuss
anything you wish during your 10-minute discussion period, with the following restrictions: (1) you are not allowed to discuss side payments (2)you are not allowed to make physical threats (3) you are not allowed tosee the private information on anyone’s monitor” (E. Ostrom, Gardner,
and Walker 1994, 150–51).
Ofﬁcial Language
Information rules also often specify the ofﬁcial language for communication in a situation. These types of rules are quite familiar to us in international settings where there is always an ofﬁcial language in which thebusiness of an international organization or conference will be conducted.
All nations also have their ofﬁcial languages. But organizations also establish ofﬁcial languages, including coding systems assigned to products, customers, order numbers, invoices, and the like.

Payoff Rules
Payoff rules assign external rewards or sanctions to particular actions thathave been taken or to particular readings on outcome state variables. An

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example of a set of payoff rules is the pay schedule that is used by agovernment agency or by a private ﬁrm to assign salaries to participantsin particular positions. This payoff schedule will vary in terms of the variables taken into account and the complexity of the schedule. Hourly wage
payoff rules frequently are very simple and specify a computation of awage for a certain number of hours considered to be the ofﬁcial working
hours of an employee during a set period. Someone being paid according
to piecework will, on the other hand, be paid by formula, attaching aweight to a quantity of intermediate or ﬁnal goods attributed to the workof the participant. Performance contracts for corporations frequently arevery much more complex. A contract may state that a corporation will
receive x amount if some physical transformation in the world (like aparticular apartment building) is completed to someone else’s satisfaction
by a set date. If the time period is greater than x, then the payment isreduced according to a formula including the amount of time of delay. In
the ﬁeld, many payoff rules that involve costs assigned to a forbidden
action (sanctions) involve a low cost for the ﬁrst infraction, a higher costfor the second, and further graduated sanctions leading to large costs
assigned for repeated infractions.
Payoff rules have an AIM that involves paying or receiving somethingof potential value. As ﬁgure 7.1 shows, payoff rules directly impact the net
costs and beneﬁts of action or outcomes for actors in an action situation.
However, payoff rules often are not the only rules that shape costs andbeneﬁts. One could discuss payoff consequences of boundary rules tied
to assignment of actions to positions, payoff consequences of information
rules, as well as payoff consequences tied to choice and scope rules.

Scope Rules
The above sections have focused on the rules that affect the deontic statusof the actions that could physically be taken by a participant in a situation.
Scope rules affect a known outcome variable that must, must not, or may
be affected as a result of actions taken within the situation. Scope rulesdeﬁne this set, affect the width of the outcome space (number of statevariables affected), and specify the range on each outcome variable included in that space. The AIM component in scope rules describes an
outcome rather than an action. Since our classiﬁcation scheme focuses on
the most direct link between the AIM and a component of the action
situation, rules with outcome AIMs directly tied to positions, boundaries,
information, payoffs, or aggregation would be classiﬁed as position,boundary, information, payoff, and aggregation rules, respectively, leaving all other rules with outcome AIMs in the scope rules category. Thus,CLASSIFYING RULES

209

the scope rule category and the choice rule category both work as “allother” categories. If a rule is not a position, boundary, information, payoff, or aggregation rule, then it is either a choice rule (if the AIM is anaction) or a scope rule (if the AIM is an outcome). Heiner (1990) focusesentirely on rules that place constraints on the production possibility frontier and not on actions in his “Rule-Governed Behavior in Evolution and
Human Society.” Thus, implicitly his analysis of rules constraining therange of outcomes relates to what we are calling here scope rules.
Because scope rules affect action sets through their effect on outcome
variables, they do not directly enumerate action sets. Rules related to theoperations of cable television stations have frequently listed upper andlower bounds on particular state variables that a station is forbidden toexceed. In some situations, the physical world or attributes of the community may make monitoring of actions more difﬁcult or sensitive than monitoring outcomes, which would then likely make scope rules more appropriate than choice rules. The attributes of community may stressautonomy and liberty, which may make it easier to gain legitimacy forscope rules than choice rules.
For example, strong norms and rules governing academic freedom render many university rules governing speciﬁc teaching and research activities suspect, while rules that base promotions on outcomes for professors
may be seen as much more legitimate. Rules generally tie tenure and promotion most heavily to outcome measures (quantity and quality of publications, quality of teaching overall), with less evaluative attention given
to choices of actions. Similarly, gaining legitimacy for government regulation from business communities with strong antiregulation views may beeasier with scope rules that allow ﬁrms to choose how to comply.
A recent workshop was held in Washington, D.C., to examine whenscope rules are most likely to be effective regulatory tools. The organizers
identiﬁed two basic types of rules that can be used in regulating industry:“Regulators can direct those they govern to improve their performance inat least two basic ways. They can prescribe exactly what actions regulated
entities must take to improve their performance. Or they can incorporate
the regulation’s goal into the language of the rule, specifying the desired
level of performance and allowing the targets of regulation to achieve thatlevel” (Coglianese, Nash, and Olmstead 2003, 706).
The ﬁrst alternative focuses on regulation via choice rules. The second
alternative—and the one that was examined in some depth at the workshop described above—is via scope rules. Even though considerable emphasis in Washington has been on using scope rules rather than choicerules,10 Coglianese, Nash, and Olmstead point out that there has been
relatively little empirical study of the impact of setting performance targets. This may be related to lower numbers of scope rules in the real world

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CHAPTER SEVEN

because of the difﬁculty that regulators face in measuring performance
rather than in determining whether forbidden actions were indeed taken.
Many of the agency ofﬁcials participating in the discussion were attracted
to the idea of performance-based regulation, but pointed out that it was
difﬁcult to move away from regulation focusing on permitted or forbidden actions.
In problematic situations, the most immediate questions raised frequently focus on the behaviors (the actions) that need to change in orderto improve the situation, and so choice rules would tend to be the mostobvious direct response. However, growing emphasis on outcome measurement in industries, in foundation grants, and in government programs
may well lead to more attention to reforms that use scope rules. Experience in the ﬁeld and in formal theory has focused less on scope rules thanon choice rules.11 At this point, we ﬁnd many fewer instances of explicit
scope rules and thus less need to further divide scope rules into speciﬁctypes. If outcome regulation continues to expand, and thereby increases
the need for policy scholars to study scope rules, more attention will need
to be paid to the types of scope rules that exist.

Default Conditions: What Happens if No Rules Exist Related
to Components of an Action Situation?
Rules do not always exist related to all elements of an action situation.
What should a participant—or an observer—deduce about the structureof a game in the absence of rules affecting all seven parts of an action
situation? This is particularly important due to the conﬁgurational natureof rules. One needs to know the basic rules related to a full rule conﬁguration, rather than to a single rule, to infer both the structure of the resulting
situation and the likely outcome of any particular rule.
In chapter 5, we identify the default conditions for the internal components of a rule. A rule that does not list any speciﬁc attribute or condition,for example, applies to all attributes and conditions that the physicalworld and the community make possible. It is also useful—and even necessary—to deﬁne the default rules for each of the seven types of rules. The
notion of default conditions allows us to address the question of what
happens if no rules exist related to a speciﬁc component of an action
situation (see E. Ostrom, Gardner, and Walker 1994 for an earlier exploration of this question).
The seven default conditions that we specify in table 7.2 are those thatwould be used by a participant or an observer in a general legal systemthat presumed general freedom unless a rule speciﬁcally prohibited or
mandated an act or event. These are the broadest default conditions that

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CLASSIFYING RULESTABLE 7.2
Default conditions
Default Position Condition

One position exists.

Default Boundary Condition

Anyone can hold this position.

Default Choice Condition

Each player can take any physically possible
action.

Default Aggregation Condition

Players act independently. Physical relationships present in the situation determine
the aggregation of individual moves into
outcomes.*Default Information Condition

Each player can communicate any information via any channel available to the player.

Default Payoff Condition

Any player can retain any outcome that theplayer can physically obtain and defend.

Default Scope Condition

Each player can affect any state of worldthat is physically possible.

* If a rule conﬁguration contains only a default choice condition, the default aggregation
condition must be present.

would be used in a common-law legal system as contrasted to a Roman
Law system that presumes that most things are forbidden unless speciﬁcally permitted. These default conditions are listed in table 7.2.12 If one
were to analyze a situation where there were no rules—and thus the rule
conﬁguration would contain only the default conditions—the resulting
conﬁguration constitutes a Hobbesian state of nature.
One way of representing such a game of nature is the Snatch Game
presented in chapter 2 (ﬁgure 2.2). The predicted equilibrium of this gameis grossly inefﬁcient—no exchanges. If the players do adopt internal
norms, as discussed in chapter 4, they may be able to achieve an efﬁcient
outcome of an exchange. But the value of the negative delta parameter
assigned to snatching the offered goods has to be larger than the value ofthe goods snatched. Otherwise, even individuals who hold norms against
snatching goods from others may end up doing so when the value ofgoods is very high.
Some analysts might respond that it is easy to ﬁx this situation—simply
impose private property rights! But the question is, How does one create
private property? What rules need to be changed to create “private property”? If all the households in a region were to get together and create alegal system deﬁning their ownership of goods and the punishment forstealing, they would need to establish at least four kinds of rules: position,boundary, choice, and aggregation. A brief summary of one possible setof these rules is shown in table 7.3.

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CHAPTER SEVENTABLE 7.3
Rules changed to create elementary property rights for agricultural commodities
Position Rules

There exist two positions: (1) an eligible exchange participant and (2) a judge.

Boundary Rules(1) All farmer households are permitted to become exchange participants or else those refusing their entry may
be punished.
(2) The judge must be selected on the basis of merit andintegrity by the households in the community or else theother rules will not be in effect.

Choice Rules(1) All exchange participants are permitted to offer to exchange goods they own for goods owned by others or else
those forbidding the exchange may be punished.
(2) If a household’s goods are snatched, the household
can report to a judge or else those preventing the report may
be punished.
(3) If a judge ﬁnds that a household has snatched goods
illegally,* the judge must ensure that the illegal household
returns the goods and forfeits its own commodities or else
the judge will be sanctioned.

Aggregation Rules

All parties to an exchange must agree before a legal exchange can occur or else the exchange does not occur.

* Now we could use the term stealing instead of simply snatching.

With these multiple changes of seven rules from their default status
to the simple rules stated above, combined with the remaining default
conditions, a new operational-level game—shown in ﬁgure 7.2—is created. In this game, a new move has been created. If Household 2 snatches
Household 1’s goods, Household 1 has the option of going to a judge(who is not modeled here as a player since this simple game assumes thejudge complies with the rule that requires him to return the commodities
to their rightful owner and to make the household who stole the goods
forfeit their own commodities as well). Given this option now made available to Household 1, Household 2 is unlikely to snatch the goods sinceHousehold 1 would be motivated to use this option if they reached thispart of the game. Instead, Household 2 will select the exchange option
where both players are better off.
Of course, this is a highly simpliﬁed version of both the action situationand the rule conﬁguration that could be adopted to avoid the deﬁcient
outcome of the Snatch Game in a State of Nature.13 However, even in thishighly simpliﬁed example, several important points have been illustrated.
These are:

213

CLASSIFYING RULES(10, 0)(5, 20)Do Nothing

Go to Judge(15, 15)
Household 1(10, 10)
ExchangeSnatchNo Action

Household 2(10, 10)Do Not Offer

Offer
Household 1
Figure 7.2 The Snatch Game with minimal property rights.

1. In the absence of any rule directly affecting an element of an action situation, the relevant rule in place can be described by a default rule.
2. When all rules are in their default, the attributes of the physical world
generate all aspects of the structure of the action situation. This is the Hobbesian “state of nature.”
3. Rules operate together with the attributes of a physical world to create astructure.

To change the game from the one shown in ﬁgure 2.2 requires changes
in at least seven rules. If the choice rule mandating the action of the judge
had only given the judge permission to return goods to those from whomthey were stolen and to conﬁscate the goods of an offender, a different
predicted outcome would be likely. It would depend on whether Household 2 thought there were any factors that would affect the likelihoodthat the judge would not impose the punishment if they snatched thegoods. If the head of Household 2, for example, was the brother of thejudge, and the choice rule of the judge stated only a permission to impose
a punishment rather than a requirement, a different calculation would
be made by Household 2. And, it needs to be stressed, the default conditions jointly create the structure of the situation along with the rules (aswell as the relevant characteristics of the biophysical world and relevant
community).
To keep the initial analysis as simple and clear as possible, the Snatch
Game has been represented as a game between households living in thesame region. The problem of gaining a commitment that goods—once

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CHAPTER SEVEN

presented for trade—would not be snatched is a widespread problem underlying the development of commercial relationships in general and longdistance trade in particular. When the presumption is made that you solve
this problem by creating property rights and then judges and police toenforce these rights, the judges and police are viewed as outside the situation (as shown above by not viewing the judge as a player). These areautomatic agents assigned to do a job reliably and not viewed as strategic
actors in many analyses.
A key problem is that once you have created private property, markets,and the role of a judge or “ruler,” how is the holder of this position going
to undertake the assigned duties?14 The judge or ruler will need to obtainfunds to organize the court, keep records, maintain a police force to provide patrols in the marketplace, search the storehouse of merchants suspected of stealing, and so forth. One way to do this is to impose a fee on
the merchants who want to use a market. Both rulers and merchants havea mutual interest in the evolution of a safe place for trading goods. The
greater the number of merchants that come to a market and the morediverse the goods they bring with them, the more valuable the market
becomes both for the merchants and for the ruler. This is especially so if
merchants from far away can be lured who bring goods that are not locally available. Now the ruler faces a substantial temptation to pledge
security to traders from other regions and get them to come. Once a lucrative trade is established, however, a ruler also faces a temptation to renege
on that costly promise or even use his or her own military forces to obtainthe valuable goods for the ruler’s household.15 So life goes on. No change
in a set of rules is ever sufﬁcient to solve the next set of problems createdby new opportunities and constraints that continually arise in an evolving
human community.

The Vertical Approach: Operational, Collective-Choice,
and Constitutional-Choice Levels of Analysis
In the IAD framework, authoritative relationships occur in collectivechoice and constitutional-choice situations. Using the IAD framework,
we focus on operational rules that affect authorized relationships andon collective-choice rules and constitutional-choice rules (both of which
Commons and Hohfeld considered to be authoritative relationships). Thisvertical approach recognizes that rule sets are themselves nested in hierarchical levels. The participants in operational situations are directly affected by the operational rules structuring what they must, must not, or
may do. These rules were crafted in a collective-choice situation structured by collective-choice rules (which participants, in what positions,CLASSIFYING RULES

215

chosen how, given information, and an assessment of beneﬁts and costs
can make operational rules). The collective-choice rules were themselves
crafted in a constitutional situation.
Vertical arrangements do not exist just within government. These relationships also exist in the contractual arrangements among private individuals as well as within a variety of governmental decision-making arrangements for determining, enforcing, and altering authorized legal
relationships (operational decisions). Under contractual arrangements,
people may participate in a mutually agreeable arrangement in redeﬁning
and altering legal relationships in order to accomplish the objectives ofsome undertaking of mutual interest. A large literature drawing on thework of Coase (1937), Milgrom and Roberts (1992), Williamson (1985,
2000), and Schmid (2004) has explored a variety of conditions underwhich individuals voluntarily move from horizontal to vertical relationships. Governmental arrangements, inherent in the operations of courts,
executive agencies, and legislative bodies, enable people to sustain efforts
to determine, enforce, and alter legal relationships. Each particular decision-making structure or decision-making arrangement, then, involves acomplex set of rules regarding the variety of participants who may wish
to pursue their strategic opportunities in order to realize some outcomesthat may be made through those decision structures.

Using Rules as Tools to Change Outcomes
Changing the Snatch Game into a game with property rights demonstrates
how even a relatively simple policy reform requires multiple rule changes.
The AIM classiﬁcation system introduced here provides a rough mechanism for simplifying the complex array of rules in any situation. Even
with this system, complexity remains. We cannot just assume that boundary rules affect only who ﬁlls positions, for example, without examining
how restricting or opening entry affect the other components of an action
situation. So, while the tools in chapters 5 and 7 allow us to sort institutional statements and deﬁne default conditions, there is still much to belearned from the diversity of institutional structures in ﬁeld settings. Whatkinds of institutional structures do participants in ﬁeld settings use? How
do these participants use rules to change the structure of the situations
that they face? Chapter 8 demonstrates the widely diverse rules that resource appropriators use to craft institutions for governing and managing
common-pool resources.

Part III
WORKING WITH RULES

Eight
Using Rules as Tools to Cope with the Commons

IN CHAPTER 7, we described the seven generic rules that individuals use
when establishing or changing action situations they confront in everyday
life. Chapter 7, hopefully, provided the reader with a useful overview of
the tools that individuals use in creating structure in the multiple action
situations they face in life. Chapters 8 and 9 will apply these tools, andthe framework developed in the earlier chapters of this book, to a focusedstudy of common-pool resource problems.
Common-pool resource problems are among the core social dilemmas
facing all peoples (see discussion in chapters 1 and 3). Collective action
is required to establish and enforce rules limiting the appropriation ofwater, ﬁsh, forest products, pasturage, and other resource products. These
problems are portrayed in many contemporary policy textbooks as relatively simple problems that can be solved analytically to achieve optimal
outcomes. Empirical research over the past decades has led to a realizationof their complexity. Applying the Institutional Analysis and Development(IAD) framework developed in the ﬁrst part of this book helps one torecognize the source of this complexity. The many variables of the biophysical/material world, the communities involved, and the rules-in-use
combine to affect the structure of appropriation situations, the patternsof interactions among appropriators from a common-pool resource, andthe outcomes achieved. Those who try to solve these problems have tocope with complexity as well as coping with the commons.
In the ﬁrst section of this chapter, we will brieﬂy review the empiricalliterature that documents many successes as well as failures of the diverse
ways that resource appropriators have coped with the commons. In lightof a general overview of empirical evidence, we will then apply the classiﬁcation system developed in chapter 7 to examine the diversity of rulesthat are used in efforts to govern and manage common-pool resources allover the world.
After explicating the speciﬁc rules discovered in extensive ﬁeld researchby many scholars, the third section of this chapter will examine the disjuncture that exists between the policy recommendations frequently madeto overcome commons dilemmas and the types of interventions that appropriators themselves have adopted. Three broad assumptions underlie

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CHAPTER EIGHT

many contemporary policy recommendations that are challenged by evidence from the ﬁeld. One is that resources are so interconnected that they
all need to be managed centrally. A second of the challenged beliefs is that
resource appropriators are not themselves capable of designing rules tosustain resources over time. Somehow, public ofﬁcials are presumed tohave the capabilities that the resource appropriators themselves lack.
Third, it is presumed that designing rules to improve outcomes is a relatively simple analytical task that is best done by objective analysts.
In light of the diversity of rules actually used to cope with commonpool resource problems, one must conclude that conducting an analyticalsearch for the optimal combination of rules is an impossible task for appropriators, ofﬁcials, or policy analysts. No one can do a full analysis of
the combination of rules potentially available and how they might interact
with attributes of the biophysical world and the relevant community. Allefforts to solve resource problems need to be viewed as experiments basedon partial analyses of speciﬁc problems. Theory and evidence play a keyrole in increasing the probability of selecting rules leading to better as
contrasted to worse outcomes. Theory cannot, however, eliminate theneed to view all policies as ongoing experiments that need to be monitored, evaluated, and adapted over time.
What factors lead appropriators to think about experimenting withrules and what calculations do they use in making such decisions? Mostof the policy literature is silent on these questions since the generalpresumption is made in this literature that making policies is what government ofﬁcials, rather than those who are directly affected by problems,do. Fortunately, many ﬁeld researchers have identiﬁed variables that are
related to local efforts to self-organize and craft better rules related to
local settings. In light of this research, the last section of the chapter
examines the attributes of resources and of appropriators that affect thelikelihood of collective action and develops a theoretical structure for analyzing how appropriators may be led to change one or more of the rules
they use in relation to a resource. The analysis does not lead to the conclusion that if left alone, resource appropriators will always, or even mostfrequently, undertake the difﬁcult task of experimenting with rules. Instead, we will posit a set of conditions of a resource and of the resource
appropriators that are most conducive to self-organization. The absenceof these attributes leads to situations that are the least conducive to selforganization.
Local self-organization can also be dominated by local elites to use rules
as tools to advantage themselves. That problem leads to a discussion in
the ﬁnal chapter of the book of the design principles that characterize
robust institutions for governing and managing common-pool resources,
the factors that threaten the long-term sustainability of any such system,USING RULES AS TOOLS TO COPE

221

and the need for polycentric institutions at multiple levels to cope withpoorly performing systems at any level through redundant, multitier governance systems.

Field Research on Common-Pool Resources
A large number of ﬁeld studies have found that local groups of resource
users, sometimes by themselves and sometimes with the assistance of
external actors, have managed to create viable institutional arrangements for coping with common-pool resource problems.1 These empiricalstudies document successful self-organized resource governance systems
in diverse sectors in many parts of the world. At the same time, somecommons dilemmas have continued unabated (see, for example, Finlayson and McCay 1998; Berkes et al. 2001), and some common-property
institutions have experienced drastic negative changes during the last century (Seixas and Berkes 2003). Further, some common-property institutions have been converted by local leaders into private property institutions enforced by governmental ofﬁcials (Ensminger and Knight 1997;
Mwangi 2003).
Another important set of ﬁndings is that national governmental agencies have been notably unsuccessful in their efforts to design effective anduniform sets of rules to regulate important common-pool resources acrossa broad domain (Ascher 1995). The harmful effects of nationalizing forests that had earlier been governed by local user-groups have been well
documented for Thailand (Feeny 1988), Africa (Shepherd 1992; Thomson 1977; Thomson, Feeny, and Oakerson 1992), Nepal (Arnold andCampbell 1986), and India (Gadgil and Iyer 1989; Grafton 2000; Jodha
1990, 1996). Similar results have occurred in regard to inshore ﬁsheries
taken over by state or national agencies from local control by the inshoreﬁshers themselves (Cordell and McKean 1992; Cruz 1986; Dasgupta
1982; Higgs 1996; Pinkerton 1989).
Many developing countries nationalized their land and water resources
between the 1950s and 1970s. The institutional arrangements that local
resource users had devised to limit entry and use lost their legal standing.
The national governments that assumed these new and difﬁcult tasks
lacked adequate funds and personnel to monitor resource use effectively.
They frequently turned to private forestry ﬁrms to gain revenue from theseresources. Governments in these countries wanted to convert commonpool resources to a de jure government-property regime, but their actions
frequently resulted in de facto open-access regimes (Arnold 1998; Arnold
and Stewart 1991). The incentives of an open-access commons were accentuated since local users had speciﬁcally been told that they would not

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CHAPTER EIGHT

receive the long-term beneﬁts of their own costly stewardship efforts.
Johnson and Forsyth (2002) illustrate the longer-term problem that theseearlier interventions generated by examining the efforts of the Thai government to establish communal rights to forest access through legislation.
The potential effectiveness of the legislation has, however, been effectively
undermined by the earlier state interventions that supported commercial
interests against local interests.
Tang (1992), Lam (1998), and Joshi et al. (2000) have all found thatlarge-scale government irrigation systems do not tend to perform at thesame level as smaller-scale, farmer-managed systems (see also Mehra
1981; Levine 1980; Bromley 1982; Hilton 1992). In a study of over one
hundred irrigation systems in Nepal, Lam (1998) found that in terms ofcropping intensity and agricultural yield, crudely constructed irrigation
systems using mud, rock, timber, and sticks signiﬁcantly outperform systems built with modern concrete and iron headworks operated by national agencies.
Extensive empirical research has thus found resource appropriators potentially capable of self-organizing to manage common-pool resources
sustainably while many centralized government systems have performed
less effectively than presumed according to much of the policy literature.
One cannot assert, however, that all local efforts work well and all largescale efforts work poorly. From the empirical literature only one conclusion is tenable: averting the overuse and destruction of common-pool resources used by many individuals is a challenge. It cannot be assumed
that these problems will be solved by an automatic process. Overcoming
commons dilemmas is always a struggle (Dietz, Ostrom, and Stern 2003).

What Rules Are Found in Self-Organized Common-Pool
Resource Regimes?
A team of researchers at the Workshop in Political Theory and Policy
Analysis has read and archived much of the extensive case study literature
on local common-pool resources written by anthropologists, agricultural
economists, ecologists, historians, political scientists, and sociologists (see<http://www.indiana.edu/~workshop/wsl/wsl.html>; Hess 1999). Using
the Institutional Analysis and Development (IAD) framework elucidated
in chapters 1 and 2, we developed structured coding forms to help us
identify the speciﬁc kinds of action situations faced in the ﬁeld as well as
the types of rules that users have evolved over time to try to govern andmanage their resource effectively (E. Ostrom et al. 1989; E. Ostrom,
Gardner, and Walker 1994). In order to develop standardized coding
forms, we read hundreds of cases describing how local common-pool re-USING RULES AS TOOLS TO COPE

223

sources were or were not regulated by a government agency, by the users
themselves, or by a nongovernmental organization (NGO).
This section provides an overview of the diversity of speciﬁc rules that
we recorded using the general classiﬁcation described in chapter 7. I do
this for several purposes:
1. To illustrate the immense creativity of individuals coming from all stationsof life living in all parts of the world. Their creativity and entrepreneurship are
frequently unrecognized since many resource appropriators engaged in problem
solving are not well educated and have only local reputations.
2. To show the incredible diversity of rules that individuals have adopted in
one or more settings.
3. To challenge the assumption frequently made by policy analysts that it isroutinely feasible to conduct complete analysis of a problem and develop “theoptimal” set of rules for solving that problem.2
4. To illustrate the beneﬁt of classifying rules by their AIM. While we cannot
point to an optimal set of rules usable in most commons dilemma situations,
we can now use a common language for identifying rules that is functional for
academics as well as those directly involved in solving problems.

To understand the tools that appropriators use in the ﬁeld, let us examine
the speciﬁc boundary, position, choice, and payoff rules used in commonpool resource situations around the world initially identiﬁed in a metaanalysis conducted by Arun Agrawal, William Blomquist, Edella Schlager,
Shui Yan Tang, and myself (see E. Ostrom et al. 1989). These four clustersof rules are the major tools used everywhere to affect commons dilemmas.
Information, scope, and aggregation rules are additional tools used to complement changes induced by these four types of rules.
Affecting the Attributes of Users through Boundary Rules
As discussed in chapter 7, boundary rules deﬁne the attributes and conditions required of those who enter a position in an action situation. In ﬁeld
settings, many action situations are involved but I will focus attention
on the appropriation situation: Who appropriates (harvests) how many
resource units from which common-pool resource? Boundary rules, thus,
deﬁne who has a right to enter and use a resource as an “authorized appropriator”—the term we will use for this most general position that exists in multiple settings. Boundary rules affect the types of participants
with whom other participants will be interacting related to a particular
resource.
If contingent cooperation is perceived to be a possibility, then an important way to enhance the likelihood of using reciprocity norms is toincrease the proportion of appropriators who are well known in a com-

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CHAPTER EIGHTTABLE 8.1
Attributes and conditions used in boundary rules to deﬁne who is authorized to appropriate
from a common-pool resource
Attributes
Residency or Membership
National
Regional
Local community
Organization (e.g., co-op)

Personal characteristics
Ascribed
Age
Caste
Clan
Class
Ethnicity
GenderRace
Acquired
Education level
Skill test

Conditions
Relationship with resource
Use of speciﬁed technology
Continued use of resourceLong-term rights based on
Ownership of a proportion ofannual ﬂow of resource
units
Ownership of land
Ownership of nonland asset(e.g., berth)Ownership of shares in a
private organization
Ownership of a share of theresource system
Temporary use-rights acquired
throughAuctionPer-use fee
LicensesLottery
Registration
Seasonal fees

munity. These participants have a long-term stake in that community andwould ﬁnd it costly to have their reputation for trustworthiness harmedin that community. Reducing the number of users, but opening the resource to strangers willing to pay a license fee, as is frequently recommended in the policy literature, introduces appropriators who lack a longterm interest in the sustainability of a particular resource. Using licenses
to regulate entry increases the number of strangers using the resource andmay reduce the level of trust among participants and their willingness to
use reciprocity and thus increase enforcement costs substantially.
From our initial reading and our own ﬁeldwork, we expected to ﬁnd
boundary rules that focused on local residency as a way of increasing theopportunity for reciprocity, and these rules were used extensively. Whatamazed us, however, as we read the extensive number of case studies describing ﬁeld settings, was the variety of attributes and conditions used
to deﬁne who could be an authorized appropriator from diverse inshoreﬁsheries, irrigation systems, and forests. As shown in table 8.1, we identiﬁed twenty-three attributes of individuals and thirteen conditions de-USING RULES AS TOOLS TO COPE

225

scribed by case-study authors as having been used in at least one commonpool resource somewhere in the world (E. Ostrom et al. 1989). Whilesome systems use only a single attribute or condition, many use two or
three of these rules in combination.
Boundary rules that are used in the ﬁeld can be broadly grouped inthree general classes related to how individuals gain authority to enterand appropriate resource units from a common-pool resource. The ﬁrst
type of boundary rule focuses on residency or membership requirements.
These include an individual’s citizenship, residency, or membership in aparticular organization. Many forestry and ﬁshery user groups require
members to have been born in a particular location. A second broadgroup of attributes relates to ascribed or acquired personal attributes.
User groups may require that appropriation depends on age, ethnicity,clan, caste, and/or education. A third group of boundary rules relates tothe conditions of use relating an appropriator with the resource itself.
Using a particular technology or acquiring appropriation rights throughan auction, a lottery, or purchases of land or livestock are examples ofthis type of condition.
For the forty-four case studies of inshore ﬁsheries in many parts of
the world for which sufﬁcient information existed, Schlager (1994, 258)
identiﬁed thirty-three user groups as having at least one boundary rule
regarding the use of the resource. All thirty-three groups depended onsome combination of fourteen attributes or conditions. None of thesegroups relied on a single attribute or condition. Thirty out of thirty-threegroups limited ﬁshing to those individuals who lived in a nearby community, while thirteen groups also required membership in a local organization. Consequently, most inshore ﬁsheries organized by the users themselves restrict ﬁshing to those individuals who are well known to eachother, have a relatively long-term time horizon, and are connected to one
another in multiple ways (see Taylor 1982; Singleton and Taylor 1992;
Berkes et al. 2001).
After residency, the next most frequent attribute or condition, used in
two-thirds of the organized subgroups, is that appropriators use a particular type of technology. These rules are often criticized by policy analysts,
since gear restrictions are thought to reduce the “efﬁciency” of ﬁshing.
Gear restrictions, however, have many other consequences as well. Usedin combination with choice rules that assign ﬁshers that use one type ofgear to one area of the grounds they use, and ﬁshers using a second typeof gear to a separate area, these rules solve conﬂicts among incompatible
technologies. Many gear restrictions also place a reduced load on theﬁshery itself and thus help to sustain longer-term use of the resource(Acheson 2003; Corson 2002).

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CHAPTER EIGHT

Consider other boundary rules in use. A scattering of groups used ascribed characteristics (age—two groups; ethnicity—three groups; race—
ﬁve groups). Three types of temporary use rights included government
licenses (three groups), lottery (ﬁve groups), and registration (fourgroups). Seven groups required participants to have purchased an asset
such as a ﬁshing berth, while three groups required ownership of nearby
land as a condition of appropriation. Schlager (1994) did not ﬁnd that any
particular attribute or condition was correlated with higher performancelevels, but she did ﬁnd that the thirty-three groups, who had at least one
boundary rule, tended to be able to solve common-pool problems more
effectively than the eleven groups who had not crafted boundary rules.
In a study of forty-three small- to medium-sized irrigation systems managed by farmers or by government agencies, Tang (1992) found that thevariety of attributes or conditions used in irrigation was smaller thanamong inshore ﬁsheries. The single most frequently used boundary rule,used in thirty-two of the forty-three systems (74 percent), was that an
irrigator must own land in the service area of an irrigation system (84–85). All of the government-owned and -operated irrigation systems reliedon this attribute and only this attribute. Many of the user-organized systems relied on other attributes and conditions or land ownership combined with other rules. Among the other rules used were ownership of a
proportion of the ﬂow of the resource (e.g., ﬁsh, water, forest products),
membership in a local organization, and a per-use fee.
Tang found a strong negative relationship between reliance on land as
the sole boundary requirement and performance (87). Over 90 percent of
the systems using other boundary rules or a combination of rules including land ownership were rated positively in the level of maintenanceachieved and in the level of rule conformance, while fewer than 40 percentof those systems relying solely on land ownership were rated at a higher
performance level (p = .001).
Many of the boundary rules used by appropriators in the ﬁeld are amechanism to ensure that appropriators will interact with others who live
nearby and have a long-term interest in sustaining the productivity of theresource. One way that the rules devised by appropriators increase thesustainability of a resource is to change the composition of the group thatuses a common-pool resource. Their rules tend to increase the proportionof participants who have a long-term interest in the resource, who are
more likely to use reciprocity, and who can be trusted. Central governments tend to use a smaller set of rules. Some of these may open up a
resource to strangers without a long-term commitment to the resource,
create too large a geographic domain, generate conﬂict among users, andlead to an unwillingness to abide by any rules.3

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227

Position Rules Creating Monitors
In the discussion above, we focused on how boundary rules create thegeneral position of authorized appropriator. In some self-organized resource governance systems, a second position of guard or monitor is also
created. Many different names are used.
Among self-organizing forest governance systems, creating and supporting a position as guard is frequently essential when resource units
are highly valuable and a few hours of stealth generates substantial illicit
income. Monitoring rule conformance among forest users by ofﬁciallydesignated and paid guards may make the difference between a resource
in good condition and one that has become totally degraded. In a studyof 279 local forest councils in the Kumaon region of India, Agrawal and
Yadama (1997), for example, found that the number of months a guard
was hired was the most important variable affecting forest conditions.
The other variables that affected forest conditions in their study included
the number of meetings held by the forest council (this is usually a time
when infractions are discussed) and the number of residents in the village.
It is evident from the analysis that the capacity of a forest council to monitor
and impose sanctions on rule-breakers is paramount to maintaining the forest
in good condition. Nor should the presence of a guard be taken simply as aformal mechanism that ensures greater protection. It is also an indication of
the informal commitment of the panchayat and the village community to protect their forests. Hiring a guard costs money. The funds have to be generated
within the village and earmarked for protection of the resource. If there wasscant interest in protecting the forest, villagers would have little interest in setting aside the money necessary to hire a guard. (455)Many self-organized ﬁsheries rely on self-monitoring more than the creation of a formal position of guard. Most inshore ﬁshers now use shortwave radios as a routine part of their day-to-day operations allowing aform of instant monitoring to occur. An ofﬁcial of a West Coast Indian
tribe reports, for example, that “it is not uncommon to hear messages
such as ‘Did you see so-and-so ﬂying all that net?’ over the short-wave
frequency—a clear reference to a violation of speciﬁed gear limits” (citedin Singleton 1998, 134). Given that most ﬁshers will be listening to theirshort-wave radios, “such publicity is tantamount to creating a ﬂashingneon sign over the boat of the offender. Such treatment might be preceded
or followed by a direct approach to the rule violator, advising him toresolve the problem. In some tribes, a group of ﬁshermen might delegate
themselves to speak to the person” (134).
Whether irrigation systems create a formal position as guard depends
both on the type of governance of the system and on its size. Of the ﬁfteen

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CHAPTER EIGHT

government-owned irrigation systems included in Tang (1992), twelve(80 percent) have established a position of guard. Stealing water was a
problem on most government-owned systems, but it was endemic on thethree government systems without guards. Of the twenty-eight farmerorganized systems, seventeen (61 percent) utilize the position of water
distributor or guard. Eleven farmer-organized systems do not employ aguard. Farmers are vigilant enough in monitoring each other’s activities
on ﬁve systems (45 percent) that rule conformance is high. This means,of course, that self-monitoring is not high enough on the other six systems
to support routine conformance with their own rules.
An earlier study by Romana de los Reyes (1980) of ﬁfty-one communal
irrigation systems in the Philippines illustrates the effect of size. Of thethirty systems that were less than ﬁfty hectares, only six (20 percent) had
established a position as guard; of the eleven systems that served between
ﬁfty to one hundred hectares, ﬁve (45 percent) had established guards;
and of the ten systems over one hundred hectares, seven (70 percent) had
created guards. In a survey of over six hundred farmers served by thesecommunal irrigation systems, she also found that most farmers also patrolled their own canals even when they were patrolled by guards accountable to the farmers for distributing water. Further, the proportion of farmers who reported patrolling the canals serving their farms increases to 80
percent on the largest self-organized systems compared to 60 percent on
the smallest systems.
Considerable variation thus exists in the kind of monitoring positions
created in self-organized systems. Regardless of the rules creating such
positions, we have consistently found that higher levels of local monitoring are positively related to resource conditions (see Gibson, Williams,
and Ostrom 2005).
Affecting the Set of Allowable Actions through Choice Rules
Choice rules are also a major type of rule used to regulate common-poolresources. Some irrigation systems allocate water simply on the basis of
the crops that a farmer grows.4 Some rules involve a simple formula as away of devising how many resource units appropriators may obtain. Others simply choose the resource for a deﬁned period(s) and then allow harvesting during a particular season. Many forest resources, for example,
are closed to all forms of harvesting during one portion of the year andopen for extraction by all who meet the boundary rules during an open
season. Most choice rules, however, have two components: an allocation
formula and the assets on which the formula is based.
In table 8.2, the eight allocation formulas used in the ﬁeld are shownin the left column. A ﬁsher might be assigned to a ﬁxed location (a ﬁshing

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USING RULES AS TOOLS TO COPE

TABLE 8.2
Choice rules used to allocate common-pool resources
Allocation formula for appropriation rights

Basis for allocation formula

Percentage of total available units per period
Quantity of resource units per period
Appropriate only from a speciﬁc location
Appropriate only from a speciﬁc time slot
Rotate in time or space
Appropriate only during open seasons
Appropriate only resource units meeting
criteria
Appropriate whenever and whereverAmount of land held
Amount of historical use
Location of appropriator
Quantity of shares of resource owned
Proportion of resource ﬂow owned
Purchase of periodic rights at auction
Rights acquired through periodic lottery
Technology used
License issued by a governmental authority
Equal division to all appropriators
Needs of appropriators (e.g., type of crop)
Ascribed characteristic of appropriator
Membership in organization
Assessment of resource conditionspot) or to a ﬁxed rotational schedule, a member of the founding clan
may be authorized to cut timber anywhere in a forest, while an irrigator
might be assigned to a ﬁxed percentage of the total water available during
a season or to a ﬁxed time slot. In addition to the formula used in achoice rule, most also attach a condition as a basis for the assignment.
For example, a ﬁsher might be assigned to a ﬁxed location based on anumber drawn in a lottery, on the purchase of that spot in an auction, oron the basis of his or her historical use.5 An irrigator might be assigned
to a ﬁxed rotation based on the amount of land owned, the amount ofwater used historically, or the speciﬁc location of the irrigator.
If all of the conditions were equally likely to be combined with all of
the formula, there would be 112 different choice rules (8 allocation formulas × 14 bases). A further complication is that the rules for one productmay differ from those of another product harvested from the same resource. In regard to forest resources, for example, children may be authorized to pick fruit from any tree located in a forest so long as it is for theirown consumption, women may be authorized to collect so many headloads of dead wood for domestic ﬁrewood and certain plants for making
crafts, while shaman are the only ones authorized to collect medicinal
plants from a particular location in a forest (Fortmann and Bruce 1988).
Appropriation rights to ﬁsh are frequently related to a speciﬁc species. A
still further complication is that the rules may regularly change over thecourse of a year depending on resource conditions. Thus, the exact number of rules that are actually used in the ﬁeld is difﬁcult to compute.

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CHAPTER EIGHT

Schlager (1994, 259–60) found that user groups included in her study
frequently assigned ﬁshers to ﬁxed locations using a diversity of bases
including technology, lottery, or historical use. Seven groups allocatedﬁshers to ﬁshing spots using a rotation system, and seven other groups
allowed ﬁshing locations to be used only during a speciﬁc season. Fourgroups allocated ﬁshing spots for a particular time period (a ﬁshing day
or a ﬁshing season). On the other hand, nine user groups required ﬁshers
to limit their harvest to ﬁsh that met a speciﬁc size requirement.
An important ﬁnding—given the puzzles addressed in this chapter—isthat the choice rule most frequently recommended by policy analysts (seeL. Anderson 1986; Arnason and Gissurarson 1999; Copes 1986) was not
used in any of the coastal ﬁsheries included in Schlager’s study. In noneof the ﬁsheries coded by Schlager was an attempt made to regulate thequantity of ﬁsh harvested per year based on an estimate of the yield. “Thisis particularly surprising given that the most frequently recommended
policy prescription made by ﬁshery economists is the use of individualtransferable quotas based on estimates on the economically optimal quantity of ﬁsh to be harvested over the long run” (397).
In an independent study of thirty traditional ﬁshery societies, Acheson,
Wilson, and Steneck also noted the surprising absence of quota rules: “Allof the rules and practices we found in these 30 societies regulate ‘how’ﬁshing is done. That is, they limit the times ﬁsh may be caught, the locations where ﬁshing is allowed, the technology permitted, and the stage ofthe life cycle during which ﬁsh may be taken. None of these societies limits
the ‘amount’ of various species that can be caught. Quotas—the single
most important concept and tools of scientiﬁc management—is conspicuous by its absence” (1998, 397; see Wilson et al. 1994). Many local inshore ﬁshers, when allowed to self-organize, appear to use rules that differ
substantially from those recommended by advocates of scientiﬁc management (Berkes et al. 2001, 177–79). Fishers have to know a great deal
about the ecology of their inshore region including spawning areas,
nursery areas, the migration routes of different species, and seasonable
patterns just in order to succeed as ﬁshers. Those inshore ﬁsheries thathave survived the threat of rapid technological change have learned
how “to maintain these critical life-cycle processes with rules controlling
technology, ﬁshing locations, and ﬁshing times. Such rules in their vieware based on biological reality” (Acheson, Wilson, and Steneck 1998,
405). Lobe and Berkes (2004) also illustrate how a combination ofthese three types of rules sustains contemporary coastal shrimp ﬁsheriesin Kerala, India.
In the irrigation systems studied by Tang (1992, 90–91), three types ofchoice rules are used most frequently: (1) a ﬁxed time slot is assigned toeach irrigator (nineteen out of the thirty-seven cases for which data are

USING RULES AS TOOLS TO COPE

231

available, and in ten out of twelve government-owned systems), (2) a ﬁxed
order for a rotation system among irrigators (thirteen cases), and (3) aﬁxed percentage of the total water available during a period of time (ﬁvecases). A variety of conditions were used in these rules such as “amountof land held, amount of water needed to cultivate existing crops, numberof shares held, location of ﬁeld, or ofﬁcial discretion” (Tang 1994, 233).
Three poorly performing systems with high levels of conﬂict had notcrafted any choice rule at all. Farmers also do not use rules that assign a
speciﬁc quantity of water to irrigators other than in the rare circumstanceswhere they control substantial amounts of water in storage (see Maass
and Anderson 1986).
Fixed time slot rules allow farmers considerable certainty as to when
they will receive water without an equivalent certainty about the quantityof water that will be available in the canal. Fixed time allocation systems
are criticized as inefﬁcient since water is not allocated to the farmers withthe highest productivity. This condition does, however, economize greatly
on the amount of knowledge farmers have to have about the entire systemand on monitoring costs. Spooner (1974) and Netting (1974) describedlong-lived irrigation systems in Iran and in Switzerland where there wasfull agreement on the order and time allotted to all farmers located on asegment of the system, but no one knew the entire sequence for the system
as a whole.
In a study of 248 irrigation systems in Nepal, colleagues associatedwith the Irrigation Management Systems Study Group (IMSSG) at theInstitute of Agriculture and Animal Sciences in Rampur, Nepal, found a
substantial variety of choice rules used depending largely on the elevationof the district in which the system was located and the time of year involved. Thus, not only is a variety of rules used—but the rules chosen
by farmers tend to depend on ecological conditions. As shown in table
8.3, the systems included in the study were located in two districts of
Nepal: (1) Chitwan—a district in the ﬂatlands of Nepal that has larger
river systems with substantially higher volume of water during themonsoon seasons than (2) Tanahun—a district in the middle hills withfew large rivers.6 Since water is more abundant during the monsoon season in Chitwan, a larger proportion (43 percent) of the systems in this
district relies on a “free ﬂow rule” that authorizes a continuous supply inchannel than in Tanahun (33 percent). In Tanahun during the water
surplus, monsoon season, the most frequently used rule is “rotational”(52 percent), but this rule is hardly used in Chitwan during the monsoon
season (6 percent).
Rules are different in both districts during the spring—or the waterdeﬁcit time—of the year. The proportion of systems that allow farmers
to appropriate whenever they desire in the dry months is less than 10

232

CHAPTER EIGHTTABLE 8.3
Water allocation rules in Chitwan and Tanahun districts

Free ﬂow/continuous supply
Demand/ﬁrst in time
Rotational
No speciﬁc methods (consensus)
Number of irrigation systems

Water surplus time of year

Water deﬁcit time of year

ChitwanTanahunChitwanTanahun38 (43%)43 (49%)5 (6%)
2 (2%)
88

52 (33%)
4 (3%)
83 (52%)35 (22%)160

1 (1%)
0 (0%)
79 (90%)8 (9%)88

14 (9%)
4 (3%)103 (64%)
39 (24%)160

Sources: Adapted from Shukla et al. 1993; Poudel et al. 1994.

percent in both districts (see table 8.3). In the water deﬁcit time of theyear, nine out of ten systems in Chitwan and over six out of ten systems
in Tanahun use some form of a rotation system. This movement backand forth between rules depending on the season is very typical for selforganized resource systems around the world.7
Tang (1992) also found that many irrigation systems use different setsof rules depending on the availability of water. During the most abundantseason, for example, irrigators may be authorized to take water wheneverthey need it. During a season when water is moderately available, farmers
may use a rotation system where every farmer is authorized to take water
for a ﬁxed amount of time during the week based on the amount ofland to be irrigated. During scarcity, the irrigation system may employ a
special water distributor who is authorized to allocate water to those
farmers who are growing crops authorized by the irrigation system andare most in need.
In addition to devising choice rules specifying how resource units may
be harvested, many systems also have to devise rules for how resources
will be mobilized. These types of choice rules specify duties as contrasted
to rights. As discussed in chapter 9, robust common-property regimes
tend to rely on a close match between the formulas used for harvesting
and the formulas used for input requirements. In regard to irrigation,
farmers may even craft different rules related to maintenance according
to the part of the canal needing attention—such as the headworks, themain canal, secondary canals, or areas that need emergency repair. In
Chitwan, most systems tend to rely on mobilizing labor for repairing theheadworks on an irrigation system on a per household basis (also for
emergency repair anywhere on the system) but use the amount of land
owned and served by a particular part of a canal in cleaning of weeds andother impediments to the ﬂow of water in the main or secondary canals(see summary of these ﬁndings in Shivakoti and Ostrom 2002, 14–15).
In Tanahun—where the systems tend to be much smaller than in Chitwan,USING RULES AS TOOLS TO COPE

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and thus smaller differences between farmers located at the head and thetail—different rules tend to be used. About half of the 160 systems forwhich the IMSSG group of researchers gathered data relied on landholding anywhere in the system and a per household basis for mobilizing regular repairs on all parts of the system.
The diversity of choice rules devised by users greatly exceeds the few
rules recommended in textbook treatments of this problem. Appropriators thus cope with the commons by crafting a wide variety of rules affecting the actions available to participants and thus their basic set of strategies. Given this wide diversity of rules, it is particularly noteworthy thatrules assigning appropriators a right to a speciﬁc quantity of a resource
are used so infrequently in inshore ﬁsheries and irrigation systems. They
are used more frequently when allocating forest products where the quantity available, as well as the quantity harvested, are much easier to measure (Agrawal 1994). To assign an appropriator a speciﬁc quantity of a
resource unit requires that those making the assignment know the total
available units. In water resources, only when water is stored from one
season to another in a groundwater basin or dam, and reliable information about the quantity of water is available, are rules that allocate aquantity of water to an authorized appropriator utilized (Blomquist 1992;
Schlager, Blomquist, and Tang 1994).
Affecting Outcomes through Payoff and Position Rules
One way to reduce or redirect the appropriations made from a commonpool resource is to change payoff rules so as to add a penalty to actions
that are prohibited. Many user groups also adopt norms that those whoare rule breakers should be socially ostracized or shunned, and individual
appropriators tend to monitor each other’s behavior rather intensively.
Three broad types of payoff rules are used extensively in the ﬁeld: (1) the
imposition of a ﬁne, (2) the loss of appropriation rights, and (3) incarceration. The severity of each of these types of sanctions can range from very
low to very high and tends to start out on the low end of the scale.
Inshore ﬁsheries studied by Schlager relied heavily on shunning andother social norms and less on formal sanctions. Thirty-six of the fortythree irrigation systems studied by Tang used one of these three rules andalso relied on vigorous monitoring of one another’s behavior and shunning of rule breakers. The seven systems that did not self-consciously punish rule infractions were all rated as having poor performance. Fines weremost typically used (in twenty-one cases) and incarceration the least (inonly two cases). Fines tend to be graduated depending on the seriousnessof the infractions and the number of prior infractions. The ﬁnes used fora ﬁrst or second offense tend to be very low.

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Once a position of a paid guard is created, payoff rules must alsochange so as to mobilize resources to remunerate a guard. Several formulas are used. On government-owned irrigation systems, guards are normally paid a monthly wage that is not dependent on the performance ofa system or farmers’ satisfaction. Agrawal (2005) describes four different
payment methods for forest guards that users select: by each householdin kind (grains, services), by each household in cash, by the local forest
user organizations out of general funds, and by the local forest organization out of central government distributed funds. In South India, Wade
(1994) describes self-organized systems where the water distributor-guard
is paid in kind based on a proportion of the yield. As the harvest is reaped,
the guard must go to each farmer to collect his share based on the harvest
level and the amount of land owned by the farmer. Sengupta (1991, 104)
describes another system where immediately after appointment, theguards “are taken to the temple for oath taking to remain impartial. With
this vow, they break a coconut. They are paid in cash at the rate of Rs
10 per acres . . . per month by the cultivators. The neerpaichys (guards)
themselves collect the money.” Having the farmers pay the guards directlyin kind or in cash enables the farmers to “monitor” the monitor more
effectively and ensure that there is a rough proportionality between beneﬁts received and the costs of the system.
Boundary and choice rules also affect how easy or difﬁcult it is to monitor activities and impose sanctions on rule infractions. Closing a forest or
an inshore ﬁshery for a substantial amount of time, for example, has multiple impacts. It protects particular plants or ﬁsh during critical growing
periods and allows the entire system time to regenerate without disturbance. Further, during the closed season, rule infractions are highly obvious. Any person in the resource during the closed season is almost certainly breaking the rules. Similarly, requiring appropriators to use aparticular technology may reduce the pressure on the resource, help tosolve conﬂicts among users of incompatible technologies, and also makeit very easy to ascertain if rules are being followed. Many irrigation systems set up rotation systems so that only two persons need to monitor
actions at any one time. The farmers whose “turn” it is watch to be surethe next farmer does not start a turn early, and the next farmer watches
to be sure the turn-taker stops at the speciﬁed time. This keeps monitoring
costs low.
In general, self-organized governance systems need to match the rulesthat impose costs in a rough proportion to the likely positive payoffs thatappropriators are likely to obtain over time. If the appropriators do notview the requirements placed on them to provide funds, time, or materials
to be equitable, they are much less willing to conform. Since self-governed

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systems must rely more on willing consent as contrasted to coerced contributions, they need to pay more attention to the relationship between positive and negative payoffs than systems that can easily mobilize police toextract contributions from participants.
Affecting Outcomes through Changes in Information, Scope,and Aggregation Rules
Information, scope, and aggregation rules tend to be used in ways thatcomplement boundary, position, choice, and payoff rules. Individual systems vary radically in regard to the mandatory information that they require. Many smaller and informal systems rely entirely on a voluntary
exchange of information and on mutual monitoring. Where resource
units are very valuable and the size of the group is larger, more and more
requirements are added regarding the information that must be kept byappropriators or their ofﬁcials. Blomquist (1992, 1994) examined the information rules for eight groundwater basins in southern California. He
found that the groundwater basins that had overcome severe overdraft
problems had information rules that differed from those used in the one
basin that had not overcome these problems. For example, in the “successful” basins, all groundwater producers were required to report theamount of water they produced each year, and various methods were
implemented to verify the accuracy of these records. Producers were entitled to receive annual reports providing data about the activities of theirmonitors, basin conditions, and the production records of others. The
basin that still faced overdraft conditions did not have the same information rules (Blomquist 1994, 292).
Scope rules are used to limit harvesting activities in some resources bycreating refugia, where actions that are permitted in other areas of a resource are forbidden in the refugia. By not allowing any appropriationfrom these locations, the regenerative capacity of a system can be enhanced. Sacred groves are probably the most well-known form of refugia.
The ancient sacred groves of India, Africa, and the Mediterranean are
well documented in history. Modern sacred groves are known to exist inmodern times in China, Ghana, India, Mexico, Nepal, Thailand, Uganda,
and Zimbabwe (Gadgil, Hemam, and Reddy 1998, 37; Gombya-Ssembajjwe 1995). Many locally controlled ﬁsheries also protect breeding
grounds as off-limits for harvesting to enable the eggs and small ﬁsh tomature successfully before being subject to harvest (see Folke, Berkes, andColding 1998).
Aggregation rules are used extensively in collective-choice processes
and less extensively in appropriation situations. One aggregation rule that

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is found in diverse resource systems is a requirement that harvesting activities be done in teams. This increases the opportunity for mutual monitoring and reduces the need to hire special guards. We will return to a discussion of aggregation rules later in this chapter when we analyze the factors
affecting appropriators’ choice of rules. The aggregation rules used in
making collective choices are crucial rules affecting whose interests aretaken into account when decisions are made to change the operationalrules of an appropriation situation.
It is important to note that we have not yet found any particular rules
to have a statistically positive relationship to performance across resource types, ecological zones, and communities. On the other hand, theabsence of any boundary or choice rule is consistently associated withpoor performance. Relying on the use of a single type of rule for an entire
set of common-pool resources in a large region is also negatively related
to performance.

Contemporary Approaches to Resource Policy
Instead of studying the literature describing the successful and unsuccessful efforts of local users or public ofﬁcials to devise rules for coping with
common-pool resource problems, which is brieﬂy presented above, many
students of public policy read only textbooks that elucidate an approachthat can broadly be called “the scientiﬁc management of natural resources.” This approach teaches future policy analysts to consider ﬁsheries, forests, pasture lands, and water resources as relatively homogeneous
systems that are closely interrelated across a vast domain (see Sherman
and Laughlin 1992). Irrigation systems are interlinked along watersheds
of major river systems. Fish and wildlife species tend to migrate over alarge range. By implication, uniform systems of rules are usually prescribed as the best solution. Acheson, Wilson, and Steneck (1998, 391–
92) describe this approach as applied to ﬁsheries management:
For those trained in scientiﬁc management, it is also an anathema to manage aspecies over only part of its range. From the view of ﬁsheries scientists andadministrators, it is not rational to protect a species in one zone only to haveit migrate into another area where it can be taken by other people due to adifference in regulations. As a result, the units to be managed range along hundreds of miles of coast and can only be managed by central governments withjurisdiction over the entire area. . . . From the point of view of the National
Marine Fisheries Service, it makes sense to have a set of uniform regulations
for the entire US coast rather than one for each state. (italics added)

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Many policy analysts share a belief in the feasibility of central authorities to design close-to-optimal rules for governing and managing commonpool resources existing in a large domain.8 Since common-pool resources
are viewed as relatively homogeneous and interlinked and since simple
models have been developed of how they are thought to work (Gordon
1954; Heal 1998), ofﬁcials acting in the public interest are considered capable of devising uniform and effective rules for an entire region.
Recommendations calling for central governments to impose uniform
regulations over natural resources within a country’s boundary are frequent and strident. After reviewing the problems of deforestation in many
countries, Grainger (1993, 224) urges the different departments whose
policies affect forests to come together and “agree on an integrated land
use policy for the whole country.” As his ﬁrst priority in controlling deforestation, Grainger recommends directing “more funds into forestry departments so they have sufﬁcient personnel and technology to monitor
forests and logging operations, prevent illegal deforestation by improving
protection, and ensure that logging operations take place in accordancewith government regulations” (225). As Blomquist and Ingram (2003)
point out, some analysts even call for central administrative control overtransboundary resources. Fortunately, there are now some strong voices
challenging the presumed superiority of central authorities to solve
smaller-scale appropriation problems (see Karkkainen 2001/2; Holling,
Gunderson, and Ludwig 2001).
As an alternative to central control, other policy analysts call for theimposition of a market system related to resources at various scales. Carson, Marinova, and Zilberman (1999, 1), in addressing transboundarywater problems in the Middle East, conclude that “the current water allocation structure has proven inadequate. It should be replaced with someform of a water market.” Further, they declare that considerations of different ways to create markets are irrelevant and that the ﬁrst step for adramatic change is: “Replace the current water institutions and allocation
structure with a water market” (1).
Challenging Three Basic Assumptions of Contemporary
Policy Analysis
Based on evidence, it is important to challenge three basic assumptions
underlying the study of contemporary resource policies: (1) the view that
resource appropriators are helpless to overcome their temptations to harvest excessively from a resource; (2) the assumption that designing rules
to change the incentives of participants is a relatively simple analyticaltask; and (3) the view that organization itself requires central direction.

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The ﬁrst foundation that can be disputed is the model of the humanactor. Resource users are explicitly thought of as rational egoists whoplunder local resources so as to maximize their own short-term beneﬁts.
Government ofﬁcials are implicitly depicted, on the other hand, as seeking
the more general public interest, having the relevant information at hand,
and the capability of designing optimal policies. As discussed in chapter
4, the rational egoist used in conventional noncooperative game theory is
an appropriate model to use for all participants in open competitive markets and other settings where participants are relatively anonymous andhave little opportunities to develop norms and longer time horizons. Outside of these settings, however, one needs to assume a mix of participants
ranging from those with strong norms of reciprocity to those with weakor few shared intrinsic values. Assuming a multiplicity of orientations ismore appropriate in these settings in which individuals can communicate
and come to know and potentially trust one another. Rational egoists may
come to dominate in any situation in which conﬂicts are left unresolved
and participants lose trust in one another.
One should not, however, presume that all government ofﬁcials are“saints” while assuming that all resource users are “sinners.” Nor should
we presume that ofﬁcials have all the relevant knowledge to manage complex dynamic systems while local appropriators are ignorant. The knowledge base of government ofﬁcials may not, in reality, be better than thatof local appropriators who have used a particular resource for years andknow its characteristics in considerable detail. Even when the knowledge
base is similar, no guarantee exists that government ofﬁcials (or the researchers who advise them) will use available information to make efﬁcient and/or sustainable decisions.
For example, Moxnes (1998) conducted a series of experiments examining the capacity of eighty-two subjects who were Norwegian ﬁshers,ofﬁcials working for government resource agencies, or researchers familiar with resource problems to make economically efﬁcient and resourcesustaining decisions related to a simulated dynamic model of a ﬁshery. All
subjects were assigned the equivalent of private property rights over aﬁshery and asked to make decisions about the purchase of vessels andharvesting rates over a twenty “vertical” year period. On average, all subjects substantially overinvested in the ﬁshery. Many subjects reported
using heuristics of the type: “Things seem to be going well; I’ll order another vessel” (1241) similar to the heuristic used in the commons dilemma
experiments with no communication described in chapter 3. Government ofﬁcials and researchers did no better than the ﬁshers in solving thisproblem.
A second foundational belief of contemporary resource policy is thatdesigning rules to change the incentives of participants is a relatively sim-

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ple analytical task best done by objective analysts not speciﬁcally related
to any speciﬁc resource. As Acheson, Wilson, and Steneck (1998) describe
above, analysts frequently view most resources in a particular sector as
relatively similar and sufﬁciently interrelated that they need to be governed by the same set of rules.
It should now be obvious that the search for rules that improve theoutcomes obtained in commons dilemmas is an incredibly complex task
involving a potentially inﬁnite combination of speciﬁc rules that could be
adopted. To ascertain whether one has found an optimal set of rules toimprove the outcomes achieved in a single situation, one would need toanalyze how diverse rules affect the components of such a situation andas a result, the likely effect of a reformed structure on incentives, strategies, and outcomes. Since multiple rules directly or indirectly affect eachof the seven components of action situations, conducting such an analysis
would be an incredibly time- and resource-consuming process.
If only ﬁve changes in rules per component were considered, for example, there would be 57 or 75,525 different situations to analyze. This is agross simpliﬁcation, however, since some of the important rules used inﬁeld settings include more than twenty-ﬁve rules (in the case of boundary
rules) and even over one hundred variants (in the case of choice rules).
Further, how these changes affect the outcomes achieved in a particularlocation depends on the biophysical characteristics of that location andthe type of community relationships that already exist. No set of policy
analysts (or even all of the game theorists in the world today) would ever
have sufﬁcient time or resources to analyze over 75,000 combinations ofrule changes and resulting situations, let alone all of the variance in thesesituations due to biophysical community differences.
Those directly involved would also not be able to do a complete analysis. They would know a lot about local biophysical processes, but notnecessarily how that resource system might be linked with biophysicalprocesses occurring at a somewhat larger scale or even how complex biophysical systems were operating. They would also know a great deal
about local community norms and the distribution of resources and interests within a community. Given the nonlinearity and complexity of manyaction situations, it is challenging to predict the precise effect of a change
in a particular rule.
For example, a change in a boundary rule to restrict who is authorized
to enter and harvest from a resource reduces the number of individuals
who are entitled to appropriate from a resource. It also reduces the number of individuals who are interested in monitoring what is happening orcontributing funds toward hiring a guard (Agrawal 2000; Agrawal and
Goyal 2001). Thus, the opportunities for rule breaking may increase. Further, the cost of a rule infraction will be spread over a smaller group of

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appropriators. Thus, the harm to any individual may be greater. Assessing
the overall effects of a change in boundary rules is a nontrivial analyticaltask (for examples, see Weissing and Ostrom 1991a, 1991b). Instead of
conducting such a complete analysis, appropriators are more apt to use
their intuitive understanding of the resource and of one another’s norms
and preferences to experiment with different rule changes and assess theeffects of rules with which experiment until they ﬁnd a combination that
seems to work in their setting.
Local appropriators would receive feedback from their own experiments over time and could then improve how their rules worked overtime. If they had good communication with other communities who had
experience with multiple rules related to similar resources, they would be
able to make informed judgments about the likely impact of some rules,
but certainly not all of the rules that they might contemplate adopting orchanging. Greater attention to the ways that local experiments are monitored and the mechanisms available for sharing information could improve the likelihood of improving performance over time.
The third foundational belief of much contemporary policy analysis isthe view that organization itself requires central direction. Consequently,
the multitudes of self-organized resource governance systems are viewed
as mere collections of individual agents each out to maximize her own
short-term returns. The groups who have actually organized themselves
are invisible to those who cannot imagine organization without rules andregulations issued by a central authority (see, for example, Lansing 1991).
A more appropriate foundation is to assume that governments at multiple
levels could, but do not always, adopt policies that enhance effective
problem-solving and resource sustainability. Instead of central direction,
what is needed are policies that enhance the accuracy and reliability of
information, that provide low-cost conﬂict resolution, and that develop
the authority to govern resources at multiple levels.
Useful (but Partial) Analyses of Rule Conﬁgurations
I do not wish to argue that analysts as well as participants are unableor should not examine interesting combinations of rules under speciﬁed
conditions. Analyzing speciﬁc situations and how diverse rules would affect the likely incentives, behavior, and outcomes of participants is anextremely important and useful endeavor that social scientists should anddo perform.
Scholars at the California Institute of Technology, for example, took
on an important assignment for the Civil Aeronautics Board (CAB) to
examine how several rules they were considering for the allocation of
airport slots would work (Grether, Isaac, and Plott 1979, 1981). Not only

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did they develop a formal analysis of how alternative rules would affectthe incentives of airline carriers and the likely resulting behavior, they also
implemented a version of the decision setting in an experimental laboratory. They were interested in predicting the level of efﬁciency likely fromalternative decision rules at multiple levels, but also the responsivenessthat was likely due to changed economic conditions and the susceptibilityof alternative rules to collusion. In light of their modeling and their experimentation, they recommended the establishment of a “one-price sealed
bid auction” as the best method they could recommend for allocating
landing slots. Their recommendations came after they developed a modelof this speciﬁc situation and after they had undertaken a trial run process
in an experimental lab to see if their theory did predict behavior in thisparticular situation.9
For a paper that I presented at the Annual Bank Conference on Development Economics (called the ABCDE conference!), I also undertook acomparative analysis of a set of rules as they would affect the structureof an action situation—in this case, a formal game—and the net beneﬁts
that would be likely at the equilibria of such a game. In that paper(E. Ostrom 1996), I assumed an environment of ten farmers who all
owned about the same amount of land on a relatively ﬂat but rich alluvial
plain. The farmers were hypothesized to be interested in constructing andthen managing their own small irrigation system. I explored a series ofgames that would be created by using several combinations of rules forallocating beneﬁts and costs. Given my assumptions about the farmers,
the environment, and the costs of maintenance, I could show that thefarmers would gain the most out of two different combinations of rules.
The ABCDE paper was useful for several reasons. First, it was fun. It
is a delight to work with formal worlds and be able to demonstrate clearly
how rules would affect the structure of a formal game. Second, I was able
to demonstrate overtly the link between changes in the set of rules andhow these affect the structure of the action situation itself (see also
E. Ostrom, Gardner, and Walker 1994, chap. 4, where we undertake similar efforts). Since I think there is a strong connection between rule conﬁgurations and action situations, it is always helpful to demonstrate thelinkage between rules and games clearly. Third, the paper demonstrates
that in order to undertake such a formal analysis of rules and game structures, I had to make multiple assumptions about the participants and theenvironment they were in. These included:
Assumptions about the participants:
1. the number of farmers (ten);
2. the relative equality of their holdings;
3. the value of the water for farming to all of the farmers;

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4. the engineering knowledge and skills of the farmers;
5. the rights of the participants to use the source of water without contest
by other farmers;
6. the rights of the farmers to organize for joint beneﬁt and create rules
which they themselves would enforce; and7. the rational egoistic behavior of farmers.
Assumptions about the environments:
1. the existence of a ﬂat plain;
2. the existence of a nearby water source that was not currently used byother farmers;
3. relatively similar soil conditions across the plain;
4. the layout of the parcels; and5. the feasibility of two different canal layouts.

Further, I considered the impact of only a total of seven rules—an extremely small set, when one considers the multiplicity of rules arrayedabove. Any major change in the variables that I assumed could have led
to an entirely different outcome. Thus, the exercise in no way demonstrated an optimal rule conﬁguration at a more general level. Any effort
to examine the impact of alternative rules on some speciﬁc kind of action
situation will always have to make a large number of assumptions as I
did in this effort. We should not fool ourselves into thinking that theresults of these useful exercises are a full analysis showing one rule conﬁguration producing more net beneﬁts than others.

Coping with Complexity: A General Problem
The complexity we have found in regard to common-pool resources inthe ﬁeld is not in any way unique to natural resources. For far too long,social scientists have viewed the physics of static, simple systems as themodel of science we should try to emulate. Those who want to emulate
the science of static, simple systems are grossly out-of-date when it comesto understanding contemporary science and particularly contemporary
engineering. The engineers responsible for the design of airplanes andbridges—and now computers—have long coped with complex dynamic
systems. The Boeing 777, for example, has 150,000 distinct subsystems
that are composed, in some instances, of highly complex components.
Design engineers of complex systems long ago gave up hope of everdoing complete analyses of all combinations of subsystems under all
combinations of external environmental conditions. Obviously, they invest heavily in trying out diverse design elements under a variety of conditions. Testing designs by building models, using wind tunnels and com-

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puter simulations, increases the likelihood that engineers can produce a
viable combination of design elements that are robust under many conditions. They also invest in complex backup systems that enable these designed systems to achieve a high degree of robustness—meaning the capacity to maintain some desired system characteristics under changing
circumstances. All such robust systems are, however, fragile to a varietyof small perturbations (Carlson and Doyle 2002). Small, rare disturbances can cause a disastrous cascade of failure in any highly complex
designed system.
Instead of assuming that designing rules that approach optimality, oreven improve performance, is a relatively simple analytical task that canbe undertaken by distant, objective analysts, we need to understand thepolicy design process as involving an effort to tinker with a large numberof component parts (see Jacob 1977). Those who tinker with any tools—
including rules—are trying to ﬁnd combinations that work together more
effectively than other combinations. Policy changes are experiments basedon more or less informed expectations about potential outcomes and the
distribution of these outcomes for participants across time and space(Campbell 1969, 1975). Whenever individuals decide to add a rule,change a rule, or adopt someone else’s proposed rule set, they are conducting a policy experiment. Further, the complexity of the ever-changing biophysical and socioeconomic world combined with the complexity of rulesystems means that any proposed rule change faces a nontrivial probability of error.

Changing Rules as an Adaptive Process
Given the logic of combinatorics, it is not possible for anyone to conduct
a complete analysis of the expected performance of all the potential rulechanges that could be made in an effort to improve the outcomesachieved. When we study rules used by appropriators from common-poolresources in the ﬁeld, we can think of appropriators trying to understandthe biophysical structure of their resource and how they can develop a setof rules consistent with the time and place exigencies and the norms
shared in their community. Instead of being given a set of instructions
with a fully speciﬁed transformation function (as is the case for subjects
in experimental settings discussed in chapter 3), appropriators in the ﬁeld
have to explore and discover the biophysical structure of a particular resource. It will usually differ on key parameters from similar resources inthe same region. Further, they have to cope with considerable uncertainty
related to the weather, complicated growth patterns of biological systems
that may be characterized by multiple equilibria, and external price ﬂuc-

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tuations affecting the costs of inputs and value of outcomes (see Baker
2005; Wilson et al. 1994; Wilson 2002). One of their ﬁrst challenges will
be to convince those who doubt that the resource is limited or that they
will beneﬁt from cooperation, and thus that they need to constrain use ina manner that they agree is workable and fair (Gibson 2001).
Ofﬁcials and/or the appropriators themselves may try to improve performance by changing one or more rules in an adaptive process. Participants adapt the rules, norms, and strategies of their parents and elders aswell as those who are viewed as highly successful in a particular culture.
They learn about neighboring systems that work better than theirs andtry to discern which rules are helping their neighbors to do better. Humanagents try to use reason and persuasion in their efforts to devise better
rules, but the process of choice from the vast array of rules they mightuse always involves experimentation. Self-organized resource governancesystems use many types of decision rules to make collective choices ranging from deferring to the judgment of one person or elders to using majority voting to relying on unanimity.
Scholars familiar with the results of ﬁeld research do substantiallyagree on a set of variables that enhance the likelihood of appropriatorsorganizing themselves to try to avoid the social losses associated with
open access or rules that are not yet working well.10 Considerable consensus exists that the following attributes of resources and of appropriators
are conducive to an increased likelihood that self-governing associations
will form.
Attributes of the Resource
R1. Feasible improvement: Resource conditions are not at a point of deterioration such that it is useless to organize or so underutilized that little advantage
results from organizing.
R2. Indicators: Reliable and valid indicators of the condition of the resource
system are frequently available at a relatively low cost.
R3. Predictability: The ﬂow of resource units is relatively predictable.
R4. Spatial extent: The resource system is sufﬁciently small, given thetransportation and communication technology in use, that appropriators candevelop accurate knowledge of external boundaries and internal microenvironments.
Attributes of the Appropriators
A1. Salience: Appropriators depend on the resource system for a major
portion of their livelihood or the achievement of important social or religious
values.
A2. Common understanding: Appropriators have a shared image of how theresource system operates (attributes R1, 2, 3, and 4 above) and how their actions affect each other and the resource system.

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A3. Low discount rate: Appropriators use a sufﬁciently low discount rate inrelation to future beneﬁts to be achieved from the resource.
A4. Trust and reciprocity: Appropriators trust one another to keep promises
and relate to one another with reciprocity.
A5. Autonomy: Appropriators are able to determine access and harvesting
rules without external authorities countermanding them.
A6. Prior organizational experience and local leadership: Appropriators
have learned at least minimal skills of organization and leadership through participation in other local associations or learning about ways that neighboringgroups have organized.

Many of these variables are affected by the larger regime in which a
resource and its appropriators are embedded. Larger regimes can facilitate
local self-organization by providing accurate information about natural
resource systems, providing arenas in which participants can engage indiscovery and conﬂict-resolution processes, allowing for autonomy, andproviding mechanisms to back up local monitoring and sanctioning efforts. The probability of participants adapting more effective rules inmacroregimes that facilitate their efforts over time is higher than in regimes that ignore resource problems entirely or, at the other extreme,presume that all decisions about governance and management must be
made by central authorities.
A Rule Change Calculus
Now, why are these attributes of a resource and of appropriator likely to
be associated with solving collective-action problems? These attributes
combine to affect the perceived net beneﬁts of a set of appropriators (A)using a resource under a particular set of rules. The beneﬁts and costs
involved in this calculus involve both extrinsic and intrinsic valuations as
discussed in chapter 4. Each appropriator i (i ∈ A) has to compare his or
her perception of the expected net beneﬁts of harvesting while continuing
to use an old set of rules (Rold) to the beneﬁts he or she expects to achievewith a new set of rules (Rnew). Each appropriator i must ask whether hisor her incentive to change (Γi) is positive or negative.
Γi = Rnew − Rold.
If Γi is negative for all appropriators, no one has an incentive to change.
If Γi is positive for some appropriators, they then need to estimate three
types of costs:
C1—the up-front costs of time and effort spent devising and agreeing upon
new rules;

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C2—the short-term costs of adopting new appropriation strategies; andC3—the long-term costs of monitoring and maintaining a self-governed system over time.

If the sum of these expected costs for each appropriator exceeds the
incentive to change, no appropriator will invest the time and resources
needed to create new institutions. Thus, if
Γi < (C1i + C2i + C3i)
for all i ∈ A, no change occurs.
In ﬁeld settings, appropriators are not likely to expect the same costs
and beneﬁts from a proposed change. Some may expect positive net beneﬁts. Others may expect net losses from the same proposed rule change.
Consequently, the collective-choice rules used for changing operationalrules related to appropriation affect whether an institutional change favored by some and opposed by others will be adopted. For any collectivechoice rule, such as unanimity, majority, ruling elite, or one-person rule,a minimum coalition of appropriators, M,A, must agree prior to theadoption of new rules. If for all coalitions,Γm ≤ (C1m + C2m + C3m),
no new rules will be adopted. And if for at least one coalition M,A,
Γm > (C1m + C2m + C3m),
for all members of M, a new set of rules may be adopted. If there areseveral such coalitions, the question of which coalition will form, andthus which rules will result, depends on the relative resources of the coalitions and their bargaining strength (see J. Knight 1992; Ensminger andKnight 1997). This analysis is applicable to a situation where a set of
appropriators starts with only default conditions—open access—and contemplates adopting its ﬁrst rules limiting access. Appropriators considering changing operational rules over time would also use such a generalcalculus.11
The collective-choice rule used in ﬁeld settings varies from reliance on
the decision of one chief or a few members of an elite, to a reliance on
majority or supermajority vote, all the way to reliance on consensus orclose to unanimity (Buchanan and Tullock 1962). If there are substantial
differences in the perceived beneﬁts and costs of appropriators, it is possible that M appropriators will impose a new set of rules on the A–M otherUSING RULES AS TOOLS TO COPE

247

appropriators that strongly favor those in the winning coalition and impose losses or lower beneﬁts on those in the losing coalition (Thompson,
Mannix, and Bazerman 1988). If expected beneﬁts from a change in institutional arrangements are not greater than expected costs for many appropriators, however, the costs of enforcing a change in institutions will
be much higher than when most participants expect to beneﬁt from achange in rules over time.
Where the enforcement costs are fully borne by the members of M,
operational rules that beneﬁt the A−M other appropriators lower the
long-term costs of monitoring and sanctioning for a governing coalition.
Where external authorities enforce the rules agreed upon by M appropriators, the distribution of costs and beneﬁts are more likely to beneﬁt M
and may impose costs on the A−M other appropriators (Ensminger andKnight 1997; Mwangi 2003).
RESOURCE ATTRIBUTES AND CALCULATING COSTS AND BENEFITS

The attributes of a resource (listed above) affect both the beneﬁts andcosts of institutional change. Linking these attributes of the biophysicalworld to the perception of appropriators enables one to develop an underlying metric of perceived net beneﬁts to explain why self-organization
occurs in some locations and not in others. If resource units are relatively
abundant (R1), little reason exists for appropriators to invest costly timeand effort in organizing. If the resource is already substantially destroyed,
the high costs of organizing may not generate sufﬁcient beneﬁts. Self-organization is likely to occur only after appropriators observe substantial
scarcity. The danger here, however, is that exogenous shocks leading to achange in relative abundance of the resource units may occur rapidly, andappropriators may not adapt quickly enough to the new circumstances(Libecap and Wiggins 1985; Baker 2005).
The presence of reliable indicators about the conditions of a resource(R2) affects the capacity of appropriators to adapt relatively soon tochanges that could adversely affect their long-term beneﬁt stream (Moxnes 1996). A resource ﬂow that is highly predictable (R3) is much easierto understand and manage than one that is erratic (Schlager, Blomquist,
and Tang 1994). In the latter case, it is always difﬁcult for appropriators(or, for that matter, for scientists and government ofﬁcials) to judge
whether changes in the resource stock or ﬂow are due to overharvestingor to random exogenous variables.12 Unpredictability of resource units insmaller locations, such as private pastures, may lead appropriators tocreate a larger common-property unit to increase the predictability ofresource availability somewhere in the larger unit (Netting 1972;

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Wilson and Thompson 1993). The spatial extent of a resource (R4) affects
the costs of deﬁning reasonable boundaries and then of monitoring themover time.
APPROPRIATOR ATTRIBUTES AND CALCULATING COSTS AND BENEFITSAppropriators’ attributes also affect expected extrinsic and intrinsic beneﬁts and costs. If appropriators do not obtain a major part of their income
from a resource or value it highly for some other purpose (A1), the highcosts of organizing and maintaining a self-governing system may not beworth their effort (Lawry 1990; Gibson 2001). If appropriators do notshare a common understanding of how complex resource systems operate(A2), they will ﬁnd it extremely difﬁcult to agree on future joint strategies.
As Libecap and Wiggins (1985) argue, asymmetric private information
about heterogeneous assets may adversely affect the willingness of participants to agree to a reduction in their use patterns before considerable
damage is done to a resource. Given the complexity of many commonpool resources—especially multispecies or multiproduct resources—understanding how these systems work may be counterintuitive even for
those who make daily contacts with the resource.
Appropriators with many other viable and attractive options, who thus
discount the importance of future income from a particular resource (A3),may prefer to “mine” a resource without spending resources to regulate
it. In light of his study of many ﬁsheries, Berkes (1985, 201) noted that“community control over the ﬁshing effort appears to be very difﬁcult toachieve in commercial ﬁsheries in general.” He was pessimistic enoughabout the likelihood of local organization to reﬂect that if a “given stock is
not overexploited, this is probably related to insufﬁcient market demandrather than to community-level controls” (201). We will discuss a more
optimistic picture in chapter 9, however, of the Maine lobster ﬁshery.
Maine lobster ﬁshers are embedded in a polycentric system where smallscale commercial interests are centered in communities that have considerable autonomy to craft rules that have enabled the ﬁshery to ﬂourish. In
many other cases, commercial ﬁshing ﬁrms simply move on to anotherresource once one is destroyed, assuming there will always be other resources available to them. Berkes and colleagues (2001) also discuss
how comanagement strategies involving organizing at a local level alongwith an active role for a larger-scale government are challenging to create
but are an effective way to slowly increase the time horizon of ﬁshers inthese systems.
Appropriators who trust one another (A4) to keep agreements and use
reciprocity in their relationships with one another face lower expected
costs involved in monitoring and sanctioning one another over time. Ap-

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propriators who lack trust at the beginning of a process of organizing
may be able to build this form of social capital (Coleman 1988; E. Ostrom
and Ahn 2003) if they initially adopt small changes that most appropriators follow before trying to make major institutional changes. Autonomy(A5) tends to lower the costs of organizing. A group that has little autonomy may ﬁnd that those who disagree with locally developed rules seek
contacts with higher-level ofﬁcials to undo the efforts of appropriators toachieve regulation.13 Prior experience with other forms of local organization (A6) greatly enhances the repertoire of rules and strategies known by
local participants as potentially useful to achieve various forms of regulation. Further, appropriators are more likely to agree upon rules whose
operation they understand from prior experience, rather than rules thatare introduced by external actors and are new to their experience. Given
the complexity of many ﬁeld settings, appropriators face a difﬁcult task
in evaluating how diverse variables affect expected beneﬁts and costs overa long time horizon.
Attributes of the resource also affect the attributes of appropriators.
In highly variable resources (R3), for example, it may be particularly difﬁcult to understand and to sort out outcomes stemming from exogenous
factors and those resulting from the actions of appropriators (McKean
2000). Brander and Taylor (1998) have argued that when the resourcebase itself grows very slowly, population growth may exceed the carrying
capacity before participants have achieved a common understanding of
the problem they face (see also Reuveny and Maxwell 2001; Decker andReuveny 2005). Rolett and Diamond (2004) identify nine biophysicalvariables that are signiﬁcant predictors of the historical deforestation of
Paciﬁc islands, irrespective of the culture and traditions of the pre-European settlers.
Many aspects of the macroinstitutional structure surrounding a particular setting affect the perceived costs and beneﬁts. Thus, external authorities can do a lot to enhance the likelihood and performance of self-governing institutions (Shivakumar 2005). The availability of open and faircourts for resolving conﬂicts is one important facility that larger governance units can provide to increase the capability of smaller units. The
actions of external authorities can also seriously impede these developments as well. Further, when the activities of one set of appropriators, A,
have “spillover effects” on others beyond A, external authorities can either facilitate processes that allow multiple groups to solve conﬂicts arising from negative spillovers or take a more active role in governing particular resources themselves.
Appropriators in the ﬁeld rarely face a setting that generates clear-cut,
expected beneﬁt-cost ratios. The collective-choice rules in some settingsgive a small elite substantial power to block suggested changes that may

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generate positive gains for most appropriators but some losses for those inpower. Consequently, we cannot conclude that most appropriators using
common-pool resources will undertake self-governed regulation. Many
settings exist where the theoretical expectation should be the opposite:
Appropriators will overuse the resource unless efforts are made to change
one or more of the variables affecting perceived costs or beneﬁts. Given
the number of variables that affect these costs and beneﬁts, many pointsof external intervention can enhance or reduce the probability of appropriators’ agreeing upon and following rules that generate higher social
returns. Both social scientists and policymakers have a lot to learn about
how these variables operate interactively in ﬁeld settings and even how
to measure them so as to conduct well-crafted empirical studies to test
the warrantability and usefulness of this calculus.
Researchers and public ofﬁcials need to recognize the multiple manifestations of these theoretical variables in the ﬁeld. Appropriators may behighly dependent on a resource (A1), for example, because they are in aremote location and few roads exist to enable them to leave. Alternatively,
they may be located in a central location, but other opportunities are notopen to them due to lack of training or a discriminatory labor market.
Appropriator’s discount rates (A3) in relation to a particular resource
may be low because they have lived for a long time in a particular locationand expect that they and their grandchildren will remain in that location
or because they possess a secure and well-deﬁned bundle of property
rights to this resource (see Schlager and Ostrom 1992).
Reliable indicators of the condition of a resource (R2) may result from
activities that the appropriators themselves do—such as regularly shearing the wool from sheep (see Gilles and Jamtgaard 1981) or because ofefforts to gather reliable information by appropriators or by external authorities (Blomquist 1992). Predictability of resource units (R3) may result from a clear regularity in the natural environment of the resource or
because storage has been constructed in order to even out the ﬂow ofresource units over both good and bad years. They may have autonomy
to make their own rules (A5) because a national government is weak andunable to exert authority over resources that it formally owns, or because
national law formally legitimates self-governance—as is the case with Japanese inshore ﬁsheries.
When the beneﬁts of organizing are commonly understood by participants to be very high, appropriators lacking many of the attributes conducive to the development of self-governing institutions may be able to overcome their liabilities and still develop effective agreements. Libecap(1995, 166) reﬂects that “the larger the expected aggregate gains, themore likely an acceptable share arrangement can be devised.” The crucial
factor is not, however, whether all attributes are favorable but the relativeUSING RULES AS TOOLS TO COPE

251

size of the expected extrinsic and intrinsic beneﬁts and costs they generate
as perceived by participants. All of the resource and appropriator variables listed above affect their expected beneﬁts and costs. It is difﬁcult,however, particularly for outsiders to estimate their speciﬁc impact on
expected beneﬁts and costs given the difﬁculty of making precise measuresof many of these variables and weighing them on a cumulative scale.

Theoretical Puzzles
In addition to the growing consensus concerning the variables most likely
to be associated with self-organization, many unresolved theoretical issues still exist. Two major theoretical questions relate to the effect of thenumber of appropriators involved and their heterogeneity on the likelihood of self-organization and the type of rules designed.
Size
Many theorists argue that the size of a group is negatively related tosolving collective-action problems in general. Many results from gametheoretical analysis of repeated games conclude that cooperative strategies are more likely to emerge and be sustained in smaller rather thanlarger groups (see synthesis of this literature in Baland and Platteau 1996).
Scholars who have studied self-organized resource systems in the ﬁeld
point to the increased transaction costs of larger groups and tend to conclude that success will more likely happen in smaller groups (see, for example, Barker et al. 1984; Cernea 1989; Wilson and Thompson 1993;
Meinzen-Dick, Raju, and Gulati 2002). Libecap (1995) reﬂects that thecommon-pool resource experiments without communication, discussed
in chapter 3, are closer to his experience of studying large groups of oil
producers, ﬁshers, and orange producers than the experiments with
communication.
On the other hand, Tang (1992, 68) did not ﬁnd a statistical relationship within the 37 farmer-governed systems he studied (which varied from
7 to 300 farmers) between the number of appropriators or the amountof land being irrigated and performance variables. In Lam’s (1998, 115)
analysis of the performance of a much larger set of irrigation systems in
Nepal ranging in size up to 475 irrigators, he also did not ﬁnd any signiﬁcant relationship between either the number of appropriators or theamount of land included in the service area with any of the three performance variables he studied. On the other hand, in a systematic study offorest institutions, Agrawal (2000) found a curvilinear pattern. Bothsmaller and much larger forest user groups were not as able to undertake

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the level of monitoring needed to protect forest resources as moderately
sized groups.
One of the problems with a focus on size of group as a key determining
factor is that many other variables change as group size increases (Chamberlin 1974; R. Hardin 1982). If the costs of providing a public good
related to the use of a common-pool resource, say a sanctioning system,
remain relatively constant as group size increases, then increasing thenumber of participants brings additional resources that could be drawn
upon to provide the beneﬁt enjoyed by all (see Isaac, Walker, and Williams
1994). Marwell and Oliver (1993, 45) conclude that when a “good has
pure jointness of supply, group size has a positive effect on the probability
that it will be provided.” On the other hand, if one is analyzing the conﬂict
levels over a subtractable good and the transaction costs of arriving at
acceptable allocation formulas, group size may well exacerbate the problems of self-governing systems. Since there are trade-offs among various
impacts of size on other variables, a better working hypothesis is thatgroup size has a curvilinear relationship to performance.
Heterogeneity
Heterogeneity is also a highly contested variable. For one thing, groups
can differ along a diversity of dimensions including their cultural backgrounds, interests, and endowments (see Baland and Platteau 1996;
Platteau 2004). Each may operate differently. If groups coming from diverse cultural backgrounds share access to a common resource, the key
question affecting the likelihood of self-organized solutions is whether theviews of the multiple groups concerning the structure of the resource,
authority, interpretation of rules, trust, and reciprocity differ or are similar. New settlers to a region may simply learn and accept the rules of theestablished group, and their cultural differences on other fronts do notaffect their participation in governing a resource. On the other hand, newsettlers are frequently highly disruptive to the sustenance of a self-governing enterprise. They may not recognize the legitimacy of the local rules
and may place heavy demands on a resource.
When the interests of appropriators differ, achieving a self-governing
solution to common-pool resource problems is particularly challenging(Libecap 1995). Appropriators who possess more substantial economic
and political assets may have similar interests to those with fewer assets
or they may differ substantially on multiple attributes. When the more
powerful have similar interests, they may greatly enhance the probabilityof successful organization if they invest their resources in organizing agroup and devising rules to govern that group. Those with substantial
economic and political assets are more likely to be a member of a minimal

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winning coalition—and thus have a bigger impact on decisions about institutional changes. Mancur Olson (1965) long ago recognized the possibility of a privileged group whereby some of those possessing a large shareof political and economic assets were sufﬁciently affected to bear a disproportionate share of the costs of organizing to provide public goods (suchas the organization of a collectivity). On the other hand, if those withmore assets also have low discount rates (A3) related to a particular resource and lower salience (A1), they may simply be unwilling to expend
inputs or actually impede organizational efforts that might lead to theirhaving to cut back on their productive activities.
Appropriators may also design institutions that cope effectively withheterogeneities. In a study of eighteen forestry user groups in Nepal, Varughese and Ostrom (2001) found that wealth disparities and locational or
sociocultural differences had no impact on the measured level of collective
action and forest conditions. When groups adopted rules that allocate
beneﬁts using the same formulas used to allocate duties and responsibilities, appropriators who differ signiﬁcantly in terms of assets will tend toagree and follow such rules. Poteete and Ostrom (2004) reviewed theﬁndings from ﬁve studies conducted by scholars associated with the International Forestry Resources and Institutions (IFRI) research network,
who used the same research protocols to measure group and forest characteristics and the rules crafted by these groups. In these studies, heterogeneity was not consistently a negative factor affecting forest conditions. In
many cases, appropriators had designed rules that took into account thediverse forms of heterogeneity found in a user group. These ingenious
rules enabled a group to overcome the potential resentment and injustices
associated with heterogeneity. On the other hand, when heterogeneity isaccentuated by rules rather than counteracted by rules, Platteau (2003)
has documented how inequalities can lead to further inequalities.

Summing Up
Even in a group that differs on many variables, if at least a minimallywinning subset of M appropriators harvesting an endangered but valuable
resource are dependent on it (A1), share a common understanding of theirsituations (A2), have a low discount rate (A3), trust one another (A4),and have autonomy to make their own rules (A5), it is more likely thatthey will estimate the expected beneﬁts of governing their resource greaterthan the expected costs. Whether the rules agreed upon distribute beneﬁts
and costs fairly depends both on the collective-choice rule used and thetype of heterogeneity existing in the community. Neither size nor heterogeneity are variables with a uniform effect on the likelihood of organizing

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and sustaining self-governing enterprises. The debate about their effect isfocusing on the wrong variables. Instead of focusing on size or the various
kinds of heterogeneity by themselves, it is important to ask how thesevariables interact with other variables as they impact the beneﬁt-cost calculus of those involved in negotiating and sustaining agreements. Their
impact on costs of producing and distributing information (Scott 1993,
forthcoming) is particularly important.
For appropriators to cope with the complexity of experimenting withthe rules that they could use to sustain a common-pool resource, they
have to conclude that the expected beneﬁts from an institutional change
will exceed the immediate and long-term expected costs. When appropriators cannot communicate and have no way of gaining trust through theirown efforts or with the help of the macroinstitutional system within
which they are embedded, the prediction of an incapacity to extract themselves from a pattern of overuse is likely to be empirically supported.
Ocean ﬁsheries, the stratosphere, and other global commons come closest
to the appropriate empirical referents (E. Ostrom et al. 1999).
If appropriators can engage in face-to-face bargaining and have autonomy to change their rules, they may well attempt to organize themselves.
Whether they organize depends on attributes of the resource system andthe appropriators themselves that affect the beneﬁts to be achieved andthe costs of achieving them. Whether their self-governed enterprise succeeds over the long term depends on whether they can successfully experiment with a subset of the rules that are used to govern common-poolresources and a conﬁguration of rules that are easy to understand andmonitor, keep harvesting levels within bounds, and are considered equitable by most appropriators.
Once one adopts the view that one cannot create the perfect set of rules
and that all efforts at reform must be viewed as experiments, one recognizes that policy analysis can never ﬁnd “the” answer. We can analyze theeffect of rules in highly simpliﬁed game-theoretic analyses. We can certainly expand knowledge about the rich diversity of rules used in practice.
Appropriators in ﬁeld settings across time and space have already devised
an incredible richness in the rules they use. We need to learn more about
this heritage so as to be better facilitators of building adaptive institutional designs—in contrast to presuming we are the experts who can devise the optimal design to solve a complex problem. All analyses of potential institutional reforms are partial analyses. We can improve their
quality by carefully studying rules-in-use and the incentives, interactions,and outcomes they generate in light of the biophysical and social worldin which they exist.

Nine
Robust Resource Governance in Polycentric
Institutions

THE STUDY OF the rules actually used in many ﬁeld settings across theworld to regulate the use of common-pool resources leads to an unsettlingconclusion. We must conclude that those making rules in efforts to improve outcomes in this policy domain can undertake only partial analysesof a limited set of potential rules and their impact on actions and outcomes in speciﬁc environments. No one can undertake a complete analysisof all of the potential rules that they might use and analytically determine
which set of rules will be optimal for the outcomes they value in a particular ecological, economic, social, and political setting. One must recognizethat policies involving rule changes must be viewed as experiments. Further, since ecological, economic, social, and political settings are always
changing over time, no speciﬁc set of rules will produce the same distribution of beneﬁts and costs over time.
For some readers, this is a depressing lesson. They are looking for “the”
answer of how best to solve commons dilemmas and other policy problems. We all recognize that some efforts at designing or reforming rules
have had disastrous results. Developing a “sure-ﬁre” method to avoid
all disasters stemming from rules that generate perverse incentives in aparticular environment sounds like a great advance. I am sure that thedesigners of modern airplanes would also like to have a sure-ﬁre method
to test out all contingencies before sending planes into the air. Similarly,
the designers of high-speed computers and software would appreciate
having a method that would enable them to produce a “crash-proof”
computer system.
The contemporary levels of knowledge related to designing new institutions for governing complex resource systems, airplanes to ﬂy throughuncertain weather, and computers subject to diverse exigencies, are substantial, but not complete (H. Simon 1981). And, I am willing to predict
given the large number of components that combine in a nonadditive
fashion, that our knowledge of how to design these systems will continue
to grow but will never be complete. As soon as one design has proved itself
in one environment, innovations in strategies adopted by participants orchanges in the environment in which a humanly designed system is inoperation will produce unexpected results.

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We are not, however, helpless in ﬁnding ways to improve the performance of complex social-ecological systems. We cannot conduct fullanalyses of the consequences of changing all possible parts of a complex
system interacting itself with a complex and changing environment. Further, ofﬁcials and policy analysts who presume that they have the rightdesign can be dangerous. They are likely to assume that citizens areshort-sighted and motivated only by extrinsic beneﬁts and costs. Somehow, the ofﬁcials and policy analysts assume that they have different motivations and can ﬁnd the optimal policy because they are not directly involved in the problem (Moore 1995). They are indeed isolated from the
problems. This leaves them with little capability to adapt and learn inlight of information about outcomes resulting from their policies.1 All
too often, these “optimal” policies have Leviathan-like characteristics
to them.2
Continuing to presume that complex policy problems are simple problems that can be solved through the adoption of simple designs that are
given general names, such as private property, government ownership, orcommunity organization, is a dangerous academic approach. Dichotomizing the institutional world into “the market” as contrasted to “thestate” is so grossly inadequate and barren that it is surprising how thedichotomy survives as a basic way of organizing academic studies andpolicy advice. Oversimpliﬁcation of our design options is dangerous since
it hides more of the working parts needed to design effective, sustainableinstitutions than it reveals (Seabright 1993). And, it reduces our awareness of the need to monitor outcomes and improve them over timethrough better processes of learning and adaptation.
The language developed in this book to identify the working components of action arenas that exist everywhere (chapters 1 through 4); toanalyze the similarities and differences in rules, norms, and strategies(chapter 5); and then to group similar rules together by the componentof an action situation they directly affect (chapters 6 through 8), is undoubtedly more complex than many contemporary scholars would prefer.
This complexity of language has not been introduced lightly. A scholar
should also keep analysis as simple as possible—given the problems to beanalyzed. Just as important, however, is developing a mode of analysis
that enables scholars, policymakers, and participants in ongoing processes to grapple with the problems they face by digging through the layers of nested systems in which these processes exist. When one is analyzing
what is operationally a relatively simple system using a relatively simple
language for analysis, one may not need the full language system developed in this volume. Most common-pool resources, and many other policy ﬁelds, however, are complex systems and not simple systems. Thus, weneed a consistent, nested set of concepts that can be used in our analysis,

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research, and policy advice in a cumulative manner. The concepts developed in this book do, I hope, form the foundation for such an endeavor.
They are derived from a commitment to use theory to observe the institutional world and to learn from that observation and measurement. And,of course, given the central lesson just discussed, institutional analysts
will improve on these concepts over time as further research and policy
advice uses these concepts.
In chapter 8, after examining the kinds of rules used in the ﬁeld for
coping with common-pool resources in many settings, I stressed the impossibility of conducting a full analysis of all options available to ofﬁcials,
appropriators, scholars, and others interested in improving the performance of resource governance institutions. The question to be examinedin this ﬁnal chapter is whether methods exist that can be used to learn
more effectively from the experience of engaging in “reforms as experiments” (Campbell 1969). Are there ways that we can avoid some of thedisastrous results that have been produced by systems of governance inthe contemporary world? My answer is yes. While no “sure-ﬁre” methods
exist, I will argue that there are approaches to speed up and share thelearning that can result from tinkering with rules and gaining experience
with outcomes. E. Jones (2003), for example, developed a graphicalmethod for analyzing relationships in these complex systems.
First, instead of trying to search for the single set of rules that is the
optimal set for every type of problem, I will again urge the importance ofstudying the underlying designs of those real-world experiments that have
proved to be robust over time as I did in Governing the Commons (seeE. Ostrom 1990). In the ﬁrst part of this chapter, I will review what
we have learned since 1990 about design principles related to robust,common-pool resource institutions. In light of still further evidence about
the performance of self-organized systems that are consistent with theearlier derived design principles, we can conclude that there are ways oforganizing governance that increase the opportunities for adaptation andlearning in a changing and uncertain world with continuing advances inknowledge and technologies.
The design principles are not blueprints, however! They describe thebroad structural similarities among those self-organized systems that havebeen able to adapt and learn so as to be robust to the many social, economic, and ecological disturbances that occur over time. Threats always
challenge the robustness of any system—no matter how well it ﬁts thebest design principles known for a particular problem Thus, the second
topic of this chapter is a discussion of the threats that exist to any set ofself-organized, resource governance systems. Since one of the important
threats is the effort to impose uniform rules and large boundaries on systems so they are more comprehensible to academics and policymakers, I

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will conclude this chapter by urging readers to think more positively
about the complex, polycentric systems of governance that are created byindividuals who have considerable autonomy to engage in self-governance. Given the wide variety of ecological problems that individuals faceat diverse scales, an important design principle is getting the boundaries
of any one system roughly to ﬁt the ecological boundaries of the problem
it is designed to address. Since most ecological problems are nested fromvery small local ecologies to those of global proportions, following thisprinciple requires a substantial investment in governance systems at multiple levels—each with some autonomy but each exposed to information,
sanctioning, and actions from below and above (Low et al. 2003; Folke,
Berkes, and Colding 1998; Moran and Ostrom 2005).

Design Principles and Robust Social-Ecological Systems
The ﬁndings brieﬂy reviewed in chapter 8—that self-organized systems
have frequently solved many Commons Dilemmas—have surprised manyscholars. These ﬁndings differ from the grim predictions made in the
1970s and 1980s that individuals were trapped in unproductive situations
and could not themselves restructure their perverse incentives. One cannot, of course, now substitute for the earlier grim predictions a presumption of a uniformly successful conquering of collective-action problems.
As social scientists, we have to use one of our favorite slogans once
again—it depends!
In my earlier effort to understand the governance systems that had been
identiﬁed during the last half of the 1980s as long-surviving systems, I ﬁrst
tried to identify the speciﬁc rules used by the systems that had survived fora long period of time using Kenneth Shepsle’s (1989) deﬁnition of a robust
institution. Shepsle considered a system to be robust if it was long-lasting
and the operational rules had been devised and modiﬁed over time according to a set of collective-choice rules (which themselves might be modiﬁed more slowly over time within a set of constitutional-choice rules,
which were modiﬁed, if at all, very infrequently). The contemporary useof the term robustness in regard to complex systems focuses on adaptability to disturbances: “the maintenance of some desired system characteristics despite ﬂuctuations in the behavior of its component parts or its environment” (Carlson and Doyle 2002, 2538; see also Anderies, Janssen,
and Ostrom 2004).
Among the many governance systems that met Shepsle’s criteria forrobustness—as well as the criteria speciﬁed by Carlson and Doyle—thespeciﬁc operational and collective-choice rules that were observed varied
dramatically from one system to the next. It was frustrating that I could
not identify any particular rules consistently associated with robust gover-ROBUST RESOURCE GOVERNANCE

259

nance of common-pool resources. Instead of focusing on speciﬁc rules,
my effort turned to identifying eight underlying design principles thatcharacterized robust common-property institutions. No assertion wasmade that those crafting these institutions self-consciously used the designprinciples. Rather, it was my thought that robust systems had simply met
most of these principles and that those systems that had collapsed or were
performing ineffectively were not so structured.
Design principles derived from studies of long-enduring institutions for governing sustainable resources:
1. Clearly deﬁned boundaries. The boundaries of the resource system (e.g.,
irrigation system or ﬁshery) and the individuals or households with rights toharvest resource units are clearly deﬁned.
2. Proportional equivalence between beneﬁts and costs. Rules specifying theamount of resource products that a user is allocated are related to local conditions and to rules requiring labor, materials, and/or money inputs.
3. Collective-choice arrangements. Many of the individuals affected byharvesting and protection rules are included in the group who can modify
these rules.
4. Monitoring. Monitors, who actively audit biophysical conditions and user
behavior, are at least partially accountable to the users and/or are the users
themselves.
5. Graduated sanctions. Users who violate rules-in-use are likely to receive
graduated sanctions (depending on the seriousness and context of the offense)from other users, from ofﬁcials accountable to these users, or from both.
6. Conﬂict-resolution mechanisms. Users and their ofﬁcials have rapid access to low-cost, local arenas to resolve conﬂict among users or between users
and ofﬁcials.
7. Minimal recognition of rights to organize. The rights of users to devise
their own institutions are not challenged by external governmental authorities,
and users have long-term tenure rights to the resource.
For resources that are parts of larger systems:
8. Nestled enterprises. Appropriation, provision, monitoring, enforcement,
conﬂict resolution, and governance activities are organized in multiple layersof nested enterprises (based on E. Ostrom 1990, 90).

When I ﬁrst speculated about these design principles (E. Ostrom 1990),
I stressed the speculative nature of my efforts and urged others to test out
these tentative conclusions through further empirical research that would
help ascertain if these principles distinguished between robust and failed
systems. Since Governing the Commons was published, other scholars
have responded to the challenge and examined the relevance of these principles for helping to explain the performance of resource governance systems (such as ﬁsheries, irrigation systems, pastures, and forests) throughout the world (see Dietz, Ostrom, and Stern 2003).3

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Martin S. Weinstein (2000), for example, examined indigenous inshoreﬁshery institutions in Canada and Japan and found that these extremely
long-lived institutions were characterized to a large extent by the designprinciples that I had earlier proposed. Abernathy and Sally (2000) studied
nine small but long-surviving irrigation systems in the dry areas of Burkina Faso and Niger. They measured system performance using both physical and nonphysical factors and did not ﬁnd a single indicator that could
systematically be used to measure system performance. They found thatan average measure of performance based on six indicators, on the other
hand, was highly correlated with governance arrangements conforming
to the design principles listed above. Other studies of irrigation systems
that found systems that were characterized by the design principles toexhibit robustness include works by Crook and Jones (1999), Guillet(1992a, 1992b), Gupta and Tiwari (2002), and Merrey (1996).
Haley (2002) examined a somewhat different question using the designprinciples: the performance of the same private oil corporation—Arco—
related to the exploitation of two different oil ﬁelds located in indigenous
territories. Arco discovered oil in 1992 in the Pastaza Province in Eastern
Ecuador near a Quichua indigenous community (the Villano ﬁeld) and in1994 in the Colville Delta in northern Alaska on land owned by an Inupiat
Eskimo community (the Alpine ﬁeld). Both communities have received
beneﬁts from the oil revenues generated. Haley estimated that the indigenous community received around 3.2 percent of the total government andresource owner take for the Alpine ﬁeld, but less than 1 percent of thegovernment share (and not even the total return) related to the Villanoﬁeld. Haley appraised each case for the presence or absence of the designprinciples and found that the governance arrangements for the Alpine
ﬁeld were consistent with all of the design principles. For the ﬁrst decade,the arrangements for the Villano ﬁeld were not consistent with any of thedesign principles. Recently, the Villano ﬁeld has developed some ad hoc
arrangements that are consistent with two of the principles.
In light of the positive reaction to these design principles, let us brieﬂyreview some of the research that has focused speciﬁcally on each of them.
Well-Deﬁned Boundaries
The ﬁrst principle is that the boundaries of the resource system, as wellas the individuals or households with rights to harvest resource units, areclearly deﬁned. The problem that is addressed by systems that do deﬁnetheir boundaries is clearly free-riding. If a group of users can determine
their own membership—including those who agree to use the resource
according to their agreed-upon rules and excluding those who do notagree to these rules—the group has made an important ﬁrst step toward

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limiting access and developing greater trust and reciprocity. Using thisprinciple enables participants to know who is in and who is out of adeﬁned set of relationships and, thus, with whom to cooperate. Smaller
resource governance systems do not always have extensively developed
rule systems, but those that are robust do demark their boundaries (see,for example, Schlager 1994; Berkes et al. 2001).
Group boundaries are frequently marked by well-understood attributes, such as residing in a particular community or joining a speciﬁc
local cooperative (as shown in table 8.1 in chapter 8). Further, membership may be marked by various “tags”—symbolic boundaries—and involve complex rituals and beliefs that help solidify individual beliefs about
the trustworthiness of others. Contemporary developments in evolutionary theory applied to cultural systems and processes of adaptation help
to explain how these design principles work to help groups sustain andbuild their cooperation over long periods of time (Janssen and Ostrom
forthcoming b).
Just deﬁning the resource boundaries carefully, however, may not be
sufﬁcient in and of itself! In a study of irrigation systems in Nepal, Shukla(2002) found that almost all of these systems have well-demarked boundaries. A substantial difference exists, however, between the systems thathave been designed, built, and maintained by farmers as contrasted tothe systems designed by government engineers. On the farmer-designed
systems, the farmers themselves determine how large the area to be served
should be. The farmers who demark the boundary will also have to participate in the construction of the system and its maintenance by contributing time, materials, and potentially some funds. Thus, the boundaries ofirrigation systems developed by farmers tend to be conservative so thatthose who make the system work are more assured of getting water. Farmers on these systems—even at the tail end—tend to receive water in thedry season as a consequence of keeping their systems small and the otherelements of the physical and institutional structures they build.
The boundaries of those systems constructed by government agencies,
by contrast, are frequently demarked as part of donor-funded projects.
Irrigation engineers are strongly motivated to show a positive beneﬁt-cost
ratio. The more farmers placed within the service boundary of a system,
the higher the beneﬁts that can be reported in the plans submitted to donors for funding (Palanisami 1982; R. Repetto 1986). Once funding is
granted, few efforts are made to check the reliability of earlier estimates.
Farmers in the larger service area are promised water but may not receivea reliable supply. Farmers on these systems are more likely to steal waterand less likely to contribute resources to maintenance. Thus, appropriatordeﬁned boundaries tend to include a clear set of participants who knowthat they have mutual responsibilities as well as beneﬁts.

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Externally imposed boundaries may not be viewed as legitimate bythose who have cared for a resource for long periods of time. If imposedboundaries are enforced, they generate substantial costs for local peoples(Ghate 2003). On the other hand, the boundaries may not even be known
to local appropriators. Paper parks have been created in the capitals of
many countries that look clear on the ofﬁcial maps but are not demarked
or enforced locally (Hayes 2004; Dietz, Ostrom, and Stern 2003).
Some governmental reserves have had remarkable stability over time.
Vogt and colleagues (2005) have dug into archives and talked with older
residents and ofﬁcials to determine why remotely sensed images of forest
reserves in one region of Uganda show remarkably stable boundaries
when so many forest reserves have failed to reduce deforestation within
their boundaries. In this case, Vogt and colleagues determined that theboundaries were negotiated as part of the 1900 agreement between the
Regents of the Buganda Kingdom and the British colonial government—
and have thus had legitimacy for more than a century. Further, the boundaries have been clearly demarked with stone-covered cairns, and speciﬁctree species were planted in the cairns. Local clan elders and traditional
administrators participated in the original demarcation, and local residents continue to participate in the renewal of these boundaries every two
decades or so. Strict enforcement of the boundaries backed by legitimate
and well-known boundaries have led to a remarkable stability in a regionof Africa where many government forests have been extensively deforested by local residents (see also Dietz, Ostrom, and Stern 2003).
In a thoughtful analysis of the usefulness of the design principles foranalyzing why some donor-sponsored conservation projects have failed
while others have succeeded, Morrow and Hull (1996) pointed out thatmany donor projects formally met the ﬁrst design principle. Formal congruence with the ﬁrst principle is not enough, however, to enable appropriators to defend their borders from free riders. Morrow and Hull suggested a rephrasing for the ﬁrst design principle to be: “The resource itself
and the users of the resources are clearly deﬁned, and the appropriators
are able to effectively defend the resource from outsiders” (1643). Given
our own research on the importance of defending the boundaries that aredemarked, this rephrasing is a positive step forward (Dietz, Ostrom, andStern 2003; Gibson, Williams, and Ostrom 2005).
Proportional Equivalence between Beneﬁts and Costs
The second design principle is that the rules-in-use allocate beneﬁts proportional to inputs that are required. If a group of users is going to harvest
from a resource over the long run, they must devise rules related to how
much, when, and how different products are to be harvested. They also

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need to assess the costs of operating a system on users. When the rules
related to the distribution of beneﬁts are made broadly consistent withthe distribution of costs, participants are more willing to pitch in to keepa resource well-maintained and sustainable (see, for example, Nemarundwe and Kozanayi 2003). Relating user inputs to the beneﬁts theyobtain is a crucial element of establishing a fair system (Trawick 2001).
If some users get all the beneﬁts and pay few of the costs, others are notwilling to follow rules over time (Ensminger 2000). Thus, fairness is a
crucial attribute of the rules of robust systems (Chakraborty 2004).
The Chisasibi Cree have devised a complex set of entry and authority
rules related to the coastal ﬁsh stocks of James Bay, as well as the beaver
stock located in their deﬁned hunting territory. Fikret Berkes (1987, 87)
explains that these resource systems and the rules used to regulate them
have survived and prospered for so long because effective “social mechanisms ensure adherence to rules which exist by virtue of mutual consent
within the community. People who violate these rules suffer not only aloss of favour from the animals (important in the Cree ideology of hunting) but also social disgrace.” Fair rules of distribution help to build trusting relationships since more individuals are willing to abide by these rules
because they participated in their design and also because they meetshared concepts of fairness (Bowles 1998; Trosper 2002).
In long-surviving irrigation systems, for example, subtly different rules
are used in each system for assessing water fees used to pay for maintenance activities, but water tends to be allocated proportional to fees orother required inputs (Bardhan 2000; Bardhan and Dayton-Johnson
2002). Sometimes water and responsibilities for resource inputs are distributed on a share basis, sometimes on the order in which water is taken,
and sometimes strictly on the amount of land irrigated. No single set ofrules deﬁned for all irrigation systems in a region would satisfy the particular problems in managing each of these broadly similar, but distinctly
different, systems (Tang 1992; Lam 1998).
Collective-Choice Arrangements
The third design principle is that most of the individuals affected by aresource regime are authorized to participate in making and modifying
their rules. Resource regimes that use this principle are both better able totailor rules to local circumstances and to devise rules that are considered
fair by participants. As environments change over time, being able to craft
local rules is particularly important as ofﬁcials located far away do notknow of the change. When a local elite is empowered at the collectivechoice level, policies that primarily beneﬁt them can be expected (Platteau
2003, 2004; Ensminger 1990).

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In a study of forty-eight irrigation systems in India, Bardhan (2000)
ﬁnds that the quality of maintenance of irrigation canals is signiﬁcantly
lower on those systems where farmers perceive the rules to have been
made by a local elite. On the other hand, those farmers (of the 480 interviewed) who responded that the rules for their system have been craftedby most of the farmers, as contrasted to the elite or the government, havea more positive attitude about the water allocation rules and the rule
compliance of other farmers. Further, in all of the villages where a government agency decides how water is to be allocated and distributed, frequent rule violations are reported, and farmers tend to contribute less to
the local village fund. Consistent with this is the ﬁnding by Ray and Williams (1999) that the deadweight loss from upstream farmers stealingwater on government-owned irrigation systems in Maharashtra, India,
approaches one-fourth of the revenues that could be earned in an efﬁcientwater allocation and pricing regime.
Knox and Meinzen-Dick (2001, 22) note that property rights “are signiﬁcantly more likely to address the interests and needs of local people
when they are not imposed from the outside but rather are based on existing rights and reﬂect local values and norms.” As they point out, theserules take time and effort to develop, try out, modify, and then experimentwith again. Users who have been engaged in this process for some time
understand the rules that they have crafted, agree on why they are using
one rule rather than another, and tend to follow their own rules to agreater extent than those that are imposed on them. Sekher (2000) conducted a study of villages in Orissa, India, that varied in regard to theextent of participation of local villagers in making rules related to nearby
forests that they used. He found that the “wider the representation of thecommunity in the organization, the better are its chances of securing local
cooperation and rule conﬁrmation for managing and preserving the resource” (8).
In a comparative study of farmer-designed and governed irrigation
systems (FMIS), as contrasted to those designed and operated by engineers without involvement of the farmers in making rules to govern thesesystems, Shukla (2002, 83), a water engineer himself, is relatively criticalof the “unrealistic planning and design, incomplete development, a nonsystematic and inadequate maintenance program, deﬁcit operation, andlack of participation of the users” that characterized many of these systems in Nepal. Drawing on the earlier research of Pant and Lohani (1983),
Yoder (1994), Lam (1998), and Pradhan (1989), Shukla identiﬁes the following as the strengths of the farmer-designed systems: “(1) Their technical deﬁciencies are compensated by management inputs; (2) they are low
cost and based on local resources; (3) effective irrigation organizations

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exist in most FMIS; (4) most FMIS have well-deﬁned rules and roles forwater allocation, distribution, resource mobilization, and conﬂict resolution; and (5) the leaders of these systems are accountable to the users”(2002, 83).
Monitoring
Few long-surviving resource regimes rely primarily on endogenous levelsof trust and reciprocity among appropriators to keep rule breaking levels
down. Evidence of the consequence of inadequate monitoring is convincingly presented by Schweik (2000). It is obvious to most institutional analysts that rules must be enforced in some manner to achieve robust governance; the question of how rules will actually be enforced is frequently
ignored when proposed institutional changes are analyzed and a reform
is proposed. All too many “comanaged paper parks” have been draftedin the home ofﬁce of an overseas donor or even in a country’s capital city
only to be destroyed by illegal harvesting in the speciﬁed territory. While
many agree that rule enforcement is necessary for creating a sustainable
resource over time, a vigorous debate is raging about who should be themonitors (Bruner et al. 2001; Igoe 2004; Hockings and Phillips 1999;
Stevens 1997; Wells and Brandon 1992).
Most long-surviving resource regimes select their own monitors, who
are accountable to the appropriators or are appropriators themselves andwho keep an eye on resource conditions as well as on harvesting activities(design principle 4). By creating ofﬁcial positions for local monitors, aresource regime does not have to rely only on the norms of local appropriators to impose personal costs on those who break a rule. The community
creates an ofﬁcial position. In some systems, appropriators rotate into thisposition so everyone has a duty to be a monitor. In other systems, allparticipants contribute resources and they jointly hire monitors. With
local monitors, conditional cooperators are assured that someone is generally checking on the conformance of others to local rules. Thus, they
can continue their own cooperation without constant fear that others aretaking advantage of them.
Some government-owned forests have successfully adopted monitoringarrangements similar to those of self-organized systems. Banana andGombya-Ssembajjwe (2000) compare the Echuya Forest in Uganda withthree other government-owned forests and one private forest. In the threeother government-owned forests, they found extensive illegal harvesting—charcoal burning, pit-sawing, grazing, and cutting commercial
ﬁrewood—in the forest plots they had randomly selected for observationand measurement. Over 70 percent of the sample forest plots in the three

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other government forests contained evidence of illegal harvesting (90). In
the Echuya Forest and the private forest, the level of illegal harvest was
relatively minor—only 20 percent of the sample plots showed any evidence of illegal harvesting (90). In Echuya, members of an Abayanda
pygmy community, who live in the forest itself, have been asked by thegovernment to monitor local harvesting from the predominately bamboo
forest that is ofﬁcially limited to one day a week. Even though the Echuya
Forest is quite large, local monitors have made a signiﬁcant difference inthe level of illegal harvesting. A similar system drawing on local appropriators to monitor government reserves has evolved the State of Rondônia
in Brazil. In a series of forest reserves located near to a large area devoted
to colonist settlements, Batistella (2001) has documented the positive impact of the government using local rubber tappers to monitor forest use(see online supplement to Dietz, Ostrom, and Stern 2003).
In a study of the forest conditions used by 178 forest user groups located in twelve countries studied by the International Forestry Resources
and Institutions (IFRI) research network, Gibson, Williams, and Ostrom(2005) found that the level of local monitoring varies substantially acrossgroups. One of the measures obtained in this study is the frequency withwhich a local group monitors and sanctions rule breaking behavior in
the forest.4 We examined the impact of this variable on appropriators’
assessment of forest conditions (as well as on a forester’s assessment). We
also examined the impact of a group’s social capital, the group’s dependence of forest resources, and whether the group was formally organized
or not. The result of the analysis is that regular monitoring by a localgroup is more important than the other three variables in enhancing forest
conditions. Regardless of the levels of social capital, forest dependence
and formal organization, regular monitoring and sanctioning is strongly
and statistically associated with better forest conditions (Gibson, Williams, and Ostrom 2005).
Graduated Sanctions
The ﬁfth design principle identiﬁed earlier was the use of graduated sanctions by robust governance arrangements. In many self-organized systems, the ﬁrst sanction imposed by a local monitor is so low as to haveno impact on the expected beneﬁt-cost ratio of breaking local rules (given
the substantial temptations frequently involved). Rather, the initial sanction needs to be considered more as information to the person who is“caught” as well as to others in the community. Everyone can make anerror or can face difﬁcult problems leading them to break a rule. A few
rule infractions, however, can generate a downward cascade of cooperation in a group that relies only on conditional cooperation and has no

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capacity to sanction (see, for example, Kikuchi et al. 1998). In a regime
that uses graduated punishments, however, a person who purposely or byerror breaks a rule is notiﬁed that others notice the infraction (thereby
increasing the individual’s conﬁdence that others would also be caught).
Further, the individual learns that others basically continue to extend theirtrust and want only a small token to convey a recognition that the mishap
occurred. Self-organized regimes rely more on what Margaret Levi (1988)
calls “quasi-voluntary” cooperation than either strictly voluntary or coerced cooperation. A real threat to the continuance of self-organized regimes occurs, however, if some participants break rules repeatedly. The
capability to escalate sanctions enables such a regime to warn membersthat if they do not conform, they will have to pay ever higher sanctions
and may eventually be forced to leave the community.
Let me summarize the argument to this point. When the users of a
resource design their own rules (design principle 3) that are enforced by
local users or accountable to them (design principle 4) using graduatedsanctions (design principle 5) that clearly deﬁne who has rights to withdraw from a well-deﬁned resource (design principle 1) and that effectively
assign costs proportionate to beneﬁts (design principle 2), collective action and monitoring problems tend to be solved in a reinforcing manner.
Individuals who think a set of rules will be effective in producing higher
joint beneﬁts and that monitoring (including their own) will protect themagainst being a sucker, are willing to undertake conditional cooperation.
Once some users have made contingent self-commitments, they are thenmotivated to monitor other people’s behavior, at least from time to time,in order to assure themselves that others are following the rules most of
the time. Conditional cooperation and mutual monitoring reinforce one
another especially in regimes where the rules are designed to reduce monitoring costs (Kameda, Takezawa, and Hastie 2003). Over time, further
adherence to shared norms evolves and high levels of cooperation areachieved without the need to engage in extensive monitoring and the imposition of costly sanctions in all cases of observed infractions in orderto achieve rule conformance.
Conﬂict-Resolution Mechanisms
The operation of the above principles is bolstered by the sixth principle,
which points to the importance of access to rapid, low-cost, local arenas
to resolve conﬂict among users or between users and ofﬁcials. Rules, unlike physical constraints, have to be understood in order to be effective.
There are always situations in which participants can interpret a rule thatthey have jointly made in different ways. By devising simple, local mechanisms to get conﬂicts aired immediately and resolutions that are generally

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known in the community, the number of conﬂicts that reduce trust canbe reduced. If individuals are going to follow rules over a long period oftime, some mechanism for discussing and resolving what is or is not arule infraction is quite necessary to the continuance of rule conformanceitself. Further, one way of reducing the problem of elite capture of a local
resource is the availability of arenas for conﬂict resolution at levels abovethat of a local resource.
Minimal Recognition of Rights
The capability of local users to develop an ever more effective regime overtime is affected by whether they have at least minimal recognition of theright to organize by a national or local government (design principle 7).
While some resource regimes have operated for relatively long times without such rights (see Ghate 2000), participants have had to rely almost
entirely on unanimity as the rule used to change rules. Otherwise, any
temporarily disgruntled participant who voted against a rule change
could go to the external authorities to threaten the regime itself! Unanimity as a decision rule for changing rules imposes high transaction costs
and prevents a group from searching for better matched rules at relatively
lower costs. Lobe and Berkes (2004) do describe a remarkable system,
however, designed by a set of ﬁshers in Bengal who have no legal rights
and who call themselves illicit ﬁshers, that is enforced locally without any
recognition by government ofﬁcials.
Some users do devise their own rules without creating formal, governmental jurisdictions for this purpose. In many inshore ﬁsheries, for example, local ﬁshers devise extensive rules deﬁning who can use a ﬁshingground and what kind of equipment can be used (Acheson 2003; Schlager
2004). So long as external governmental ofﬁcials give at least minimal
recognition to the legitimacy of such rules, the ﬁshers themselves may beeffective enforcers of these rules when government agencies do not havethe staff to enforce them. When external governmental ofﬁcials presume
that only they can make authoritative rules, then it is difﬁcult, but not
impossible, for local users to sustain a self-organized regime (Johnson andLibecap 1982).
Communities that have the authority to craft their own rules, however,are frequently able to overcome the lack of local scientiﬁc knowledge if
and when reliable information is made available about complex relationships and is understood locally. When a study was ﬁrst conducted byWorkshop colleagues in the Loma Alta comuna in 1995, they found thatthe comuna owned almost seven thousand hectares of land in western
Ecuador (Gibson and Becker 2000). Further, the comuna had full authority to allocate land—including land in a high altitude fog forest—to mem-ROBUST RESOURCE GOVERNANCE

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bers of the community for their use. At the time of the initial study, ourteam found that the comuna had allocated a substantial portion of theirland to community members who cut down the indigenous trees in orderto grow paja toquilla to generate ﬁber to make hats and other handicrafts
for income.
Members of the comuna had not recognized that the indigenous trees
growing in this fog forest were extremely efﬁcient in capturing water fromthe fog and allowing water to precipitate into the ground. The water so
captured eventually emerged as a stream many miles away. Members of
the community relied heavily on this stream to water their agriculturalplots. Given the distance involved, it would be a challenge to discoverthat connection even though farmers in the community had increasing
concerns about the reduced ﬂow of water in this stream. In other words,
the community did not recognize the “ecosystem services” that the indigenous forest generated for their own agricultural lands.
Fortunately, one of the coleaders of our initial research team, Dusty
Becker, was able to return to Loma Alta in 1996 with a group of Earthwatch volunteers. They spent a summer carefully monitoring the amountof water captured by the indigenous trees in the high altitude forest ascompared to the trees planted for commercial purposes. They also recruited some of the high school students from Loma Alta to work withthem collecting data on a daily basis. By the end of the summer, it was
clear from the data they had collected that the indigenous trees collected
substantially more water than the trees planted for commercial purposes.
Becker (2003) estimated that the community lost 2 million liters of water
per hectare per year in those sections of the forest converted to agroforestry. The students from Loma Alta were so motivated by participating inthis summer project that they proposed making a video for their parents
that demonstrated the value of keeping the indigenous trees in their own
forest. Once the knowledge was generally available to the citizens ofLoma Alta, they voted to create a forest reserve of one thousand hectaresin the highlands of their own forest (ibid.).
Nested Enterprises
When common-pool resources are larger, an eighth design principle tends
to characterize robust systems—the presence of governance activities organized in multiple layers of nested enterprises. The rules appropriate
for allocating water among major branches of an irrigation system, forexample, may not be appropriate for allocating water among farmers
along a single distributory channel (Yoder 1994). Consequently, among
long-enduring self-governed regimes, smaller-scale organizations tend tobe nested in ever larger organizations. O. Choe (2004) provides an excel-

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lent overview of how nested enterprises have successfully been used toovercome the weakness of relying only on large-scale or small-scale units
to govern complex resource systems. We will return to this design principle in our discussion of polycentricity in the last section of this chapter.
Using the Design Principles in Practice
It is reassuring to review the research conducted on self-organized,
common-pool resource governance systems since 1990 and ﬁnd thatmany scholars have agreed with my earlier speculations about the designprinciples characterizing robust systems. Tucker (1999) uses the designprinciples to examine the evolution of a common-property forest owned
by a community in Honduras. She ﬁnds that examining the congruenceof this system with the design principles helps to identify underlying weaknesses in the regime that made this system more vulnerable to forest degradation stimulated by rapid economic development. Her ﬁndings are consistent with those of scholars who have identiﬁed multiple threats to thesustainability of self-organized governance systems over time—the topic
of the next section of this chapter—but her analysis illustrates the connection between the lack of an adequate response to external threats andsystems that are weak in regard to several of the design principles.
There is a danger, however, that project planners searching for the“right” design will try to build a one-size-ﬁts-all project supposedly basedon the design principles. Such an effort is entirely inconsistent with thetheoretical argument presented in this book concerning the importanceof matching the rules of a system to the underlying biophysical world andtype of human community involved. The question is often raised, however: How can the design principles be used in practice in addition to theiruse in research?
At a recent colloquium where the design principles were discussed,
Mike McGinnis made an interesting observation drawing broadly on thework of Herbert Simon. He noted that Simon has repeatedly stressed thecomplexity of designing humanly engineered systems whether they becomputers, road networks, or institutional arrangements. In The Sciencesof the Artiﬁcial (1981), Simon specially argues that no humanly designed,
complex system can be fully planned to achieve optimal performance.
Rather, he stressed that all complex systems must be built up from simpler
components. Simon does point out that where one begins a search to
improve the performance of a complex system, however, makes a substantial difference in the quality and speed of the search process (see also
H. Simon, 1972, 1995, 1999).
My own conclusion related to the impossibility of doing a complete
analysis of a complex, adaptive system is, of course, strongly inﬂuenced

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by the work of Simon, as well as our research on coupled social-ecologicalsystems. So, one way of thinking about the practical implications of thedesign principles is as a beginning point for conducting a broad search
for appropriate means of solving problems. One can translate the designprinciples into a series of questions that could be asked when thinking
about improving the sustainability of a common-pool resource system.
For local appropriators, a rough translation of the ﬁrst six design principles into a set of initial questions would be:
1. How can we better deﬁne the boundaries of this resource, and of the individuals who are using it, so as to make clear who is authorized to harvest andwhere harvesting is authorized?
2. How can we clarify the relationship between the beneﬁts received and thecontributions to the costs of sustaining this system?
3. How can we enhance the participation of those involved in making key
decisions about this system?
4. Who is monitoring this system and do they face appropriate incentives
given the challenge of monitoring?
5. What are the sanctions we are authorizing and can they be adjusted so thatsomeone who makes an error or a small rule infraction is sufﬁciently warned so
as to ensure longer-term compliance without our trying to impose unrealistic
sanctions?
6. What local and regional mechanisms exist to resolve conﬂicts arising overthe use of this resource?

The seventh and eighth principles are targeted at a higher level of governance. They could be translated as:
7. Are there functional and creative efforts by local appropriators tocraft effective stewardship mechanisms for local resources that should berecognized?
8. How do we create a multiple-layer, polycentric system that can be
dynamic, adaptive, and effective over time?

These are not, of course, the only questions appropriators and ofﬁcials
should ask in an effective design process, but they can be thought of as agood beginning.

Threats to Robust Governance of Common-Pool Resources
No matter how well a governance system is initially designed, however,
all humanly designed systems are vulnerable to threats. Self-organized,
resource-governance regimes are no exception. Both exogenous and endogenous factors challenge their long-term viability. Robust institutions

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may survive many threats for long periods of time. New threats may,
however, unravel systems that have survived for multiple generations.5
Major migration (out of or into an area) is always a threat that may or
may not be countered effectively (Baker 2005). Out-migration may simply change the economic viability of a regime due to loss of those who
contribute needed resources. In-migration may bring new participants
who do not trust others and do not rapidly learn social norms that havebeen established over a long period of time. Since collective action islargely based on mutual trust and reciprocity, some self-organized resource regimes that are in areas of rapid settlement have disintegrated
within relatively short times (Baland and Platteau 1996).
Even institutions that are characterized by the design principles fail.
Thus, we need to speculate about other threats to community governancethat arise from observations in the ﬁeld, theoretical conjectures, and empirical ﬁndings of scholars studying small-scale resource governance systems. Here is a list of ﬁve threats to sustainable community governanceof small-scale resource governance systems that I have come across indifferent contexts:
1. rapid exogenous changes;
2. transmission failures from one generation to the next of the operational
principles on which community governance is based;
3. programs relying on blueprint thinking and easy access to external funds;
4. corruption and other forms of opportunistic behavior; and5. lack of large-scale institutional arrangements related to reliable information collection, aggregation, and dissemination; fair and low cost conﬂictresolution mechanisms; educational and extension facilities; and facilities forhelping when natural disasters or other major problems occur at a local level.

Let us brieﬂy discuss each of these.
Rapid Exogenous Changes
All rapid changes in technology, in human, animal, or plant populations;in factor availability; in substitution of relative importance of monetarytransactions in the national governance system; or in the heterogeneity ofparticipants are a threat to the continuance of any self-organized system,
whether it is a ﬁrm in a competitive market or a community-governed
resource. Individuals who have adapted an effective way of coping witha particular technological, economic, or social environment may be able
to adjust to slow changes in one or several variables if substantial feedback is provided about the consequences of these changes for the longterm sustainability of the resource and/or the set of institutions used forgoverning that resource (Gupta and Tiwari 2002). They may even be able
to adjust to changes in these variables that occur at a moderate rate. The

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faster that key variables change and the more variables that change at
the same time, the more demanding is the problem of adaptation to new
circumstances. These kinds of threats are difﬁcult for all organizations.
Those that rely to a greater extent on quasi-voluntary compliance are,however, more threatened than those who are able to coerce contributions(Bromley and Chapagain 1984; Goodland, Ledec, and Webb 1989).
Ottar Brox (1990) provides a vivid illustration of what happened in
the northern regions of Norway when technology, population density,and other factors changed rapidly. As he points out, traditional northern
Norwegian ﬁsheries were seasonal ﬁsheries. “Large oceanic ﬁsh populations migrate during phases in their life or yearly cycles, and occur withinreach of coastal ﬁshermen only during short seasons” (231). Using traditional harvesting techniques, “coastal ﬁshermen did not have the boats,
gear and preservational techniques necessary to follow the ﬁsh populations continually” (231). This had the consequence that it was almost
impossible to destroy the ﬁshery.
Nor were the part-time farmers and part-time ﬁshers able to reap mostof the resource rent from ﬁshing until the Norwegian Raw Fish Act of1938, which empowered ﬁshers with the right to negotiate legally enforceable landing prices. Fishers, who for many centuries could not themselves
reap the rents from a migratory ﬁshery, now could do so, and could do
so in an era of fast-changing technology making it possible to capture andstore ever-greater quantities of ﬁsh. Further, other ﬁshermen from othercountries after the Second World War had the technology and capital tosubstantially increase effort dramatically above that which could be devoted prior to this era. A ﬁshery that had survived, and even ﬂourished,
during many centuries of part-time ﬁshing rapidly changed to a threatened resource without adequate institutional means to respond to the new
incentives facing the ﬁshers.
Transmission Failures
Rapid change of population or culture may lead to a circumstance inwhich the general principles involved in the design of effective communitygoverned institutions are not transmitted from one generation to another.
When individuals substitute rote reliance on formal rules for an understanding of why particular formal rules are used, they can argue forinterpretations of the formal rules that undercut the viability of community organization. For example, the charter or constitution of a community
organization may specify that simple majority rule will be used in making
decisions about future projects and how the costs and beneﬁts of theseprojects will be allocated. If the founders of such an organization recognize the importance of gaining general agreement, they will rarely pushforward on a large project supported only by a minimal winning

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coalition. When there is a bare majority, almost as many community
members oppose a project as those who support it. If over time, however,the principle of gaining general agreement to future projects is not conveyed and accepted by those who accept leadership responsibilities, thendecisions barely receiving a majority may be pushed forward. Leadersof communities who rely for too many decisions on minimal winningcoalitions may ﬁnd themselves having to use patronage, coercion, and/or corruption, rather than a foundation of general agreement, to keep
themselves in power.
Similarly, if participants view their own rules as obstacles to be overcome, rather than as the written representation of general underlying
principles of organization, they may push for interpretations of rulesthat lead to their general weakening. If each household tries to ﬁnd every
legal way to minimize the amount of labor contributed to the maintenanceof a farmer-governed irrigation system, for example, eventually the cumulative effect is an insufﬁcient maintenance effort and the unraveling of
the contingent contributions of all. If one family tries to make a favorable interpretation of how much labor they should contribute, given theland they own, others come to know that this family is interpreting rules
in a manner that is highly favorable to them. Others who would be favored by such an interpretation begin to use it as well. The total quantityof labor contributed declines. Unless there is a community discussion
about the underlying principles that can be used in interpreting rules,
practices may evolve that cannot be sustained over time. The danger existsthat the unraveling continues unabated until the community organization
falls apart.
If one family tries to make a favorable interpretation of how muchlabor they should contribute, given the land they own, others come toknow that this family is interpreting rules in a manner that is highly favorable to them. Others, who would be favored by such an interpretation,
begin to use it as well. The total quantity of labor contributed declines.
Unless there is a community discussion about the underlying principles
that can be used in interpreting rules, practices may evolve that cannot besustained over time. Then, the danger exists that the unraveling continues
unabated until the community organization falls apart.
Programs Relying on Blueprint Thinking and Easy Accessto External Funds
Blueprint thinking occurs whenever policymakers, donors, citizens, or
scholars propose uniform solutions to a wide variety of problems that are
clustered under a single name based on one or more successful exemplars.
David Korten (1980) called this the “blueprint approach” and made a

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devastating critique of its prevalence in development work at the end of
the 1970s. Projects or programs rely on some formula—the design ofanother project, imposition of a particular voting rule in all settings, orthe way the project is initiated—rather than learning the speciﬁcs of aparticular setting and enabling participants to experiment and learn fromtheir own experience and that of others.
Even with all of the lessons learned in the last three decades about thedangers of blueprint thinking, the temptation to fall into this trap continues unabated. Fabián Repetto (2002), for example, describes an ambitious, but failed, antipoverty program adopted in Argentina, the Plan Solidaridad, which was an imitation of the supposedly successful antipoverty
program, Progresa, implemented in Mexico. Roconi (2002) describes another Argentine program that was modeled on a blueprint, Plan Trabajar,
which also ended up stymied by clientelistic networks that resorted torent seeking and other mutually beneﬁcial actions that undermined theprogram. In searching for the “holy grail,” efforts to design homegrownsolutions to unique ecological conditions are stymied while policymakers
switch policies rapidly trying to copy whatever is considered the latest
and best (Mukand and Rodrik 2002; Acuna and Tommasi 2000). Pritchett and Woolcock (2003) bemoan the problem of trying to ﬁnd solutions when “the” problem is actually the blueprint solution recommended
by donors and national governments for solving a problem.
Tragically, advocates of community governance have sometimes fallen
into this trap. A major program at the World Bank, Community Driven
Development (CDD), sounds as if it should support effective local development. A requirement of this program is that the initial proposal for a
World Bank project must come from local ofﬁcials or communities. Manyof the projects that are called “community driven,” however, turn out tobe quick investments in infrastructure such as local schools or roads. They
are indeed recommended by local ofﬁcials. What school principal would
not actively lobby for a new school building once the possibility of gaining
World Bank funds is announced? The principal only needs to make a goodplea for the importance of a new school without any requirement forﬁnancing the repayment of the loan. One evaluation of such CDD projects
found few other improvements beyond the infrastructure (e.g., few booksin the new schools and little impact on children’s educational achievement) (World Bank 2002). Negative evaluations have not, however, hadmuch impact on the fervent advocates of CDD (Platteau 2004; Mansuri
and Rao 2003). CDD projects at the Bank have increased from an annual
expenditure of $325 million in 1996 to over $2 billion in 2003 (Mansuri
and Rao 2003) based more on enthusiasm than on objective evaluations(Conning and Kevane 2002; Tendler 2000). Major risks of elite capture
and fraud exist in such programs (Platteau and Gaspart 2003).

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The availability of funds from donors or from national governmental
budgets that make no requirements for contributions from recipients canalso undermine local institutions.6 This is particularly salient in regard to
local infrastructure.7 Monetary resources for constructing, operating, andmaintaining infrastructure is frequently contributed by the taxpayers of
the nation in which the infrastructure is located or the taxpayers of thosenations providing economic assistance funds. When these external funds
are used, the ﬁnancial connection between supply and use is nonexistent.
Whether the resources so mobilized are directly invested in the construction and operation of the infrastructure or are diverted for individual
use by politicians or contractors depends on the professionalism of those
involved and on active efforts to monitor and sanction diversions of resources and on the incentives built into the disbursement rules used
(Platteau and Gaspart 2003). Consequently, a considerable portion of themobilized resources is diverted to purposes other than those for which it
was intended.
Further, the design of projects is oriented more toward capturing theapproval of those who fund new construction than toward providing systems that solve the problems facing present and future users. To convince
politicians that large chunks of a national budget should be devoted tothe construction of local infrastructure, planners attempt to design projects that are “politically attractive.” This means that politicians who support such expenditures can claim that the voters’ funds are being used toinvest in projects that will greatly expand the amount of food available
and lower the cost of living. Development projects need to have considerable local involvement to be successful (Shivakumar 2005).
To convince external funding agencies that major infrastructure projects should be funded through loans or grants, the evaluative criteria usedby these agencies in selecting projects has to play a prominent role in thedesign of projects (Gibson, Andersson, et al. 2005). Projects designed byengineers, who lack on-the-ground experience or training as institutional
analysts, are frequently oriented toward winning political support or international funding. This orientation does not lead to the construction ofprojects that serve most users effectively or encourage the investment ofusers in their long-term sustenance. Inefﬁciencies occur at almost every
stage. At the same time, this inefﬁcient process leads to the construction ofprojects that generate substantial proﬁts for large landholders and strong
political support for a government.
Processes that encourage looking to external sources of funding makeit difﬁcult to build upon indigenous knowledge and institutions (Haller
2001, 2002). A central part of the message asking for external funds isthat what has been accomplished locally has failed and massive external
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some cases, no recognition is made at all of prior institutional arrangements. This has three adverse consequences: (1) property rights that resource users had slowly achieved under earlier regimes are swept away
and the poor lose substantial assets; (2) those who have lost prior investments are less willing to venture further investments; and (3) there is a
general downgrading of the status of indigenous knowledge and institutions. In light of their own analysis of a failed effort to use external funds
to create an effective community forestry project, Morrow and Hull(1996) provide a good summary of the problems resulting from externallydriven funding and priorities: “This case, along with the experience ofother community forestry enterprises in Latin America, suggests that
donor-driven projects often fail to analyze in sufﬁcient depth the factors
outlined by the design principles, particularly the issues of institutional
and technological appropriateness and the impact of the larger political
economy” (1655).
Corruption and Rent-Seeking
All types of opportunistic behavior are encouraged, rather than discouraged, by the availability of massive funds to subsidize the construction
and operation of large-scale infrastructure projects (Gibson, Andersson,
et al., 2005). Corrupt exchanges between ofﬁcials and private contractors
are a notorious and widespread form of opportunism; corrupt payments
by citizens to government ofﬁcials are less publicized, but probably noless widespread. Free-riding on the part of those receiving beneﬁts andthe lack of trust between citizens and ofﬁcials, as well as among citizens,are also endemic. Further, the potential rents that can be derived fromfree electricity and free water by large-scale landowners stimulate efforts
to inﬂuence public decision making as to where projects should be locatedand how they should be ﬁnanced. Politicians, for their part, win politicalsupport by strategic decisions concerning who will receive or continue toreceive artiﬁcially created economic rents.
Robert Bates (1987, 128) explains many of the characteristics of African agricultural policies by arguing that major “inefﬁciencies persist because they are politically useful; economic inefﬁciencies afford governments means of retaining political power.” Part of Bates’s argument
relates to the artiﬁcial control exercised over the prices paid for agricultural products, a topic that is not addressed in this study. The other partof Bates’s argument relates to the artiﬁcial lowering of input prices.
When they lower the price of inputs, private sources furnish lesser quantities,
users demand greater quantities, and the result is excess demand. One consequence is that the inputs acquire new value; the administratively created short-

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age creates an economic premium for those who acquire them. Another is that,at the mandated price, the market cannot allocate the inputs; they are in short
supply. Rather than being allocated through a pricing system, they must be
rationed. Those in charge of the regulated market thereby acquire the capacity
to exercise discretion and to confer the resources upon those whose favor they
desire. (128)

Public programs that distribute farm credit, tractor-hire services, seeds,
and fertilizers, and which bestow access to government-managed irrigation schemes and public land, thus become instruments of political organization in the countryside of Africa (130).
There is an added dimension to rent seeking. The losses that the general
consumer and taxpayer accrue from rent-seeking activities are one dimension. The second aspect of rent seeking in highly centralized economies isthe acquisition of resources needed to accumulate and retain politicalpower. All forms of opportunistic behavior, therefore, are exacerbated in
an environment in which an abundance of funds are available for theconstruction of new and frequently large-scale infrastructure projects thatprovide subsidized electricity, local roads, schools, and water.
Lack of Large-Scale Supportive Institutions
While smaller-scale, community-governed resource institutions may be
more effective than centralized government in achieving many aspects ofsustainable development, the absence of supportive, large-scale institutional arrangements may be just as much a threat to long-term sustenanceas the presence of preemptive large-scale governmental agencies. Obtaining reliable information about the effects of different uses of resource
systems and resource conditions is an activity that is essential to longterm sustainability. If all local communities were to have to develop all of
their own scientiﬁc information about the physical settings in which they
were located, few would have the resources to accomplish this.
Let me use the example of the important role that the U.S. Geological
Survey has played in the development of more effective local groundwater
institutions in some parts of the United States. What is important to stressis that the Geological Survey does not construct engineering works or doanything other than obtain and disseminate accurate information abouthydrologic and geologic structures within the United States. When a local
set of water users wants to obtain better information about a local
groundwater basin, they can contract with the Geological Survey to conduct an intensive study in their area. Water producers would pay a portionof the cost of such a survey. The Geological Survey would pay the otherportion. The information contained in such a survey is then public infor-

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mation available to all interested parties. The Geological Survey employsa highly professional staff who rely on the most recent scientiﬁc techniques for determining the structure and condition of groundwater basins. Local water producers obtain the very best available informationfrom an agency that is not trying to push any particular future projectthat the agency is interested in conducting. Many countries, such as India,
that do have large and sometimes dominating state agencies do not haveagencies that provide public access to high-quality information about resource conditions and consequences. Recent efforts to open up groundwater exploration in India may lead to the massive destruction of groundwater basins rather than a ﬁrm basis for long-term growth.
Similarly, the lack of a low-cost, fair method for resolving those conﬂicts that spill out beyond the bounds of a local community is also a threat
to long-run sustainability. All groups face internal conﬂicts or intergroupconﬂicts that can destroy the fundamental trust and reciprocity on which
so much effective governance is based. If the only kind of conﬂict-resolution mechanisms available are either so costly or so biased that most selfgoverned common-pool resources cannot make use of them, these conﬂicts can themselves destroy even very robust institutional arrangements.

Modest Coping Methods for Dealing with Threats to Sustainability
No sureﬁre mechanisms exist for addressing all of the above threats.
There are three modest methods that I would like to discuss here before
turning to the concept of polycentricity—which is the major coping
method discussed in the last section of this chapter. I do so because thesemethods are not frequently mentioned as being important ways of increasing the effectiveness of self-governed institutions. However, they frequently have high payoffs. They are: (1) the creation of associations ofcommunity-governed entities, (2) comparative institutional research thatprovides a more effective knowledge base about design and operating
principles, and (3) the development of more effective high school andcollege courses on local governance.
Creating Associations of Community-Governance EntitiesThose who think local participation is important in the process of developing sustainable resources and more effective governance of resources
are frequently committed to doing a good deal of “community organization.” All too frequently, this type of organization is conceptualized as
fostering a large number of community groups at the same level. If community organization is fostered by nongovernmental organizations

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(NGOs) who then provide staff assistance and some external resources,
the organizations may ﬂourish as long as the NGOs remain interested,
but wither on the vine when the NGOs turn to other types of projects. A
technique that draws on our knowledge of how self-governed institutions
operate is helping to create associations of community organizations.
When community organizations are brought together in federations,they can provide one another some of the backup that NGOs may provide
to single-layer community organizations. While no single communitygoverned organization may be able to fund information collection thatis unbiased and of real value to the organization, a federation of suchorganizations may be able to amass the funds to do so. Simply having a
newsletter that shares information about what has worked and why it
has worked in some settings helps others learn from each other’s trialand-error methods. Having an annual meeting that brings people together
to discuss their common problems and ways of tackling them greatly expands that repertoire of techniques for coping with threats that any one
group can muster on its own.8
Rigorous Institutional Research
In addition to the type of exchange of information that those involved inself-governing entities can undertake on their own, it is important to ﬁnd
ways of undertaking rigorous, comparative research that controls for the
many confounding variables that simultaneously affect performance(Hayes 2004; Gibson, Williams, and Ostrom 2005). In the ﬁeld of medicine, folk medicine has frequently been based on unknown foundations
that turned out to be relatively sound, but some folk medicine continued
for centuries, doing more harm to patients than good. The commons thatare governed by users and the institutions they use are complex and sometimes difﬁcult to understand. It is important to blend knowledge and information obtained in many different ways as we try to build a more effective
knowledge base about what works and why. The recent study of Theesfeld(2004) is an outstanding example of such a blend. It is a rigorous study
drawing on theory, in-depth ﬁeldwork, and quantitative survey research
to understand the constraints facing Bulgarian farmers in their efforts toengage in collective action in the Bulgarian transitional economy.
Developing Better Curricula on Local Governance
Textbooks on governance used to focus as much on local as they did on
national governance arrangements. During the past half-century, introductory textbooks on American government have moved from a ﬁfty-ﬁfty
split between national and local government, to a ninety-ﬁve to ﬁve split.

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The textbooks used in the West have strongly inﬂuenced the textbooks
used in developing countries. Consequently, many public ofﬁcials learn
nothing in high school and college about how local communities can govern themselves effectively or about the threats to local self-governance.
Instead, a presumption is made that governance is what is done in national capitals and what goes on in villages is outmoded if not completely
useless.9
The Advantage and Limits of Polycentric Systems in Coping with
Design and Long-Term Sustainability of Systems
The last major task to be undertaken in this chapter is to discuss why
autonomous, self-organized resource governance systems may be more
effective in learning from experimentation than a single central authority.
I will ﬁrst discuss the advantages and limits of a fully decentralized system
where all responsibility for making decisions related to smaller-scalecommon-pool resources is localized. Then, I will discuss why a polycentric governance system involving higher levels of government as well as
local systems is better able to cope more effectively with tragedies of thecommons (V. Ostrom 1999).
Among the advantages of authorizing the users of smaller-scalecommon-pool resources to adopt policies regulating the use of these resources are:• Local knowledge. Appropriators who have lived and appropriated from aresource system over a long period of time have developed relatively accurate
mental models of how the biophysical system itself operates, since the very
success of their appropriation efforts depends on such knowledge. They also
know others living in the area well and what norms of behavior are considered appropriate.
• Inclusion of trustworthy participants. Appropriators can devise rules that
increase the probability that others are trustworthy and will use reciprocity.
This lowers the cost of relying entirely on formal sanctions and paying forextensive guarding.
• Reliance on disaggregated knowledge. Feedback about how the resource system responds to changes in actions of appropriators is provided in a disaggregated way. Fishers are quite aware, for example, if the size and species distribution of their catch is changing over time. Irrigators learn whether aparticular rotation system allows most farmers to grow the crops they most
prefer by examining the resulting productivity of speciﬁc ﬁelds.
• Better adapted rules. Given the above, appropriators are more likely to craft
rules over time that are better adapted to each of the local common-poolresources than any general system of rules.

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• Lower enforcement costs. Since local appropriators have to bear the cost ofmonitoring, they are apt to craft rules that make infractions highly obvious
so that monitoring costs are lower. Further, by creating rules that are seen as
legitimate, rule conformance will tend to be higher.
• Parallel autonomous systems. The probability of failure throughout a large
region is greatly reduced by the establishment of parallel systems of rulemaking, interpretation, and enforcement.

There are, of course, limits to all ways of organizing the governance ofcommon-pool resources. Among the limits of a highly decentralized system are:
• Some appropriators will not organize. While the evidence from the ﬁeld isthat many local appropriators do invest considerable time and energy into
their own regulatory efforts, other groups of appropriators will not do so.
• Some self-organized efforts will fail. Given the complexity of the task involved in designing rules, some groups will select combinations of rules thatgenerate failure instead of success. They may be unable to adapt rapidly
enough to avoid the collapse of a resource system.
• Local tyrannies. Not all self-organized resource governance systems will beorganized democratically or rely on the input of most appropriators. Somewill be dominated by a local leader or a power elite who only change rulesthat they think will advantage them still further. This problem is accentuatedin locations where the cost of exit is particularly high and reduced whereappropriators can exercise choice over submitting to a local regime or not.
• Stagnation. Where local ecological systems are characterized by considerable
variance, experimentation can produce severe and unexpected results leading appropriators to cling to systems that have worked relatively well in thepast and to stop innovating.
• Inappropriate discrimination. The use of identity tags is frequently an essential method for increasing the level of trust and rule conformance. Tags basedon ascribed characteristics that have nothing to do with their trustworthinesscan, however, be the basis of excluding some individuals from access tosources of productive endeavor.
• Limited access to scientiﬁc information. While time and place information
may be extensively developed and used, local groups may not have access toscientiﬁc knowledge concerning the type of resource system involved.
• Conﬂict among appropriators. Without access to an external set of conﬂictresolution mechanisms, conﬂict within and across common-pool resource
systems can escalate and provoke physical violence. Two or more groups
may claim the same territory and may continue to make raids on one anotherover a very long period of time.
• Inability to cope with larger-scale common-pool resources. Without accessto some larger-scale jurisdiction, local appropriators may have substantial

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difﬁculties regulating only a part of a larger-scale common-pool resource.
They may not be able to exclude others who refused to abide by the rulesthat a local group would prefer to use. Given this, local appropriators haveno incentives to restrict their own use and watch others take away all of thevalued resource units that they have not appropriated.

The Capabilities of Polycentric Systems in Coping with Tragediesof the Commons
Many of the capabilities of a parallel adaptive system can be retained ina polycentric governance system. By polycentric I mean a system where
citizens are able to organize not just one but multiple governing authorities at differing scales (see V. Ostrom, Tiebout, and Warren 1961; V. Ostrom 1997, 1999). Each unit exercises considerable independence tomake and enforce rules within a circumscribed domain of authority for a
speciﬁed geographical area. In a polycentric system, some units are
general-purpose governments while others may be highly specialized. Selforganized resource governance systems in such a system may be special
districts, private associations, or parts of a local government. These arenested in several levels of general-purpose governments that also provide
civil, equity, as well as criminal courts.10
In a polycentric system, the users of each common-pool resource would
have some authority to make at least some of the rules related to howthat particular resource will be utilized. Thus, they would achieve mostof the advantages of utilizing local knowledge as well as the potential tolearn from others who are also engaged in a similar trial-and-error learning process in parallel systems (Folke, Berkes, and Colding 1998). On theother hand, problems associated with local tyrannies and inappropriate
discrimination can be addressed by larger, general-purpose governmental
units who are responsible for protecting the rights of all citizens and forthe oversight of appropriate exercises of authority within smaller units ofgovernment. It is also possible to make a more effective blend of scientiﬁc
information with local knowledge where major universities and researchstations are located in larger units but have a responsibility to relate recent
scientiﬁc ﬁndings to multiple smaller units within their region. Because
polycentric systems have overlapping units, information about what has
worked well in one setting can be transmitted to others who may try it
out in their settings. Associations of local resource governance units canbe encouraged to speed up the exchange of information about relevant
local conditions and about policy experiments that have proved particularly successful. And, when small systems fail, there are larger systems tocall upon—and vice versa.

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When there is only a single governing authority, policymakers have toexperiment simultaneously with all of the common-pool resources within
their jurisdiction with each policy change. And, once a major change has
been made and implemented, further changes will not be made rapidly.
The process of experimentation will usually be slow, and information
about results may be contradictory and difﬁcult to interpret. Thus, anexperiment that is based on erroneous data about one key structural variable or one false assumption about how actors will react can lead to avery large disaster (see Wilson, Low, et al. 1999). In any design process
where there is substantial probability of error, having redundant teams ofdesigners has repeatedly been shown to have considerable advantage (seeLandau 1969, 1973; Bendor 1985). The important point is: If the systems
are relatively separable, allocating responsibility for experimenting withrules will not avoid failure, but will drastically reduce the probability ofimmense failures for an entire region.
While the theoretical argument in support of polycentric systems ofgovernance to cope with multitier ecological systems is strong, the resistance to these forms of organization is also strong. As discussed in chapter
8, the very concept of organization is closely tied for many scholars to thepresence of a central director who has designed a system to operate in aparticular way. Consequently, the mechanisms used by polycentric systems are not well understood in many cases. Polycentric systems are themselves complex, adaptive systems without one central authority dominating all of the others. Thus, no guarantee exists that such systems willﬁnd the combination of rules at diverse levels that are optimal for any
particular environment. In fact, one should expect that all governancesystems will be operating at less than optimal levels given the immense
difﬁculty of ﬁne-tuning any complex, multitiered system.
Alcorn and Toledo (1998) stress the complementary institutional systems at the national level in Mexico, supportive of local communities, as
generating a more sustainable governance system than exists in similar
ecological conditions. In the United States, there are many examples ofdynamic polycentric resource governance systems where there is strong
evidence of high performance.
One example from the United States is the Maine lobster ﬁshery. Thissystem is noteworthy because of the long-term, complementary roles
adopted by both local and state governance systems. Maine is organized
into riparian territories along most of the coast. Boundary rules and manyof the day-to-day ﬁshing regulations are organized by harbor gangs(Acheson 1988, 2003).
In order to go ﬁshing at all, one must become a member of a “harbor gang,”
the group of ﬁshermen who go lobstering from a single harbor. Once one has

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gained admittance into such a group, one can only set traps in the traditional
territory of that particular harbor gang. Members of harbor gangs are expected
to obey the rules of their gang concerning ﬁshing practices, which vary somewhat from one part of the coast to another. In all areas a person who gains areputation for molesting others’ gear or for violating conservation laws will beseverely sanctioned. Incursions into the territory of one gang by ﬁshers fromanother are ordinarily punished by surreptitious destruction of lobster gear.
There is strong statistical evidence that the territorial system, which operates
to limit the number of ﬁshers exploiting lobsters in each territory, helps to conserve the lobster resource. (Acheson, Wilson, and Steneck 1998, 400)

At the same time, the state of Maine has long established formal lawsthat protect the breeding stock and increase the likelihood that regeneration rates will be high. “At present, the most important conservation laws
are minimum and maximum size measures, a prohibition against catching
lobsters with eggs, and a law to prohibit the taking of lobsters which once
had eggs and were marked—i.e. the ‘V-notch’ law” (Acheson, Wilson,
and Steneck 1998, 400). Neither the state nor any of the harbor gangs
has tried to limit the quantity of lobster captured. The state does not try
to limit the number of lobster ﬁshers, since this is already done at a local
level. However, the state has been willing to intercede when issues exceed
the scope of control of local groups. In the late 1920s, for example, when
lobster stocks were at very low levels and many local areas appear to have
had substantial compliance problems, the state took a number of steps—
including threats to close the ﬁshery—that supported informal local enforcement efforts. By the late 1930s, compliance problems were largely
resolved and stocks had rebounded (although it can’t be shown that these
two results are related, just correlated).
Recently, in response to changes that were breaking down the informal
harbor gang system, the state has formalized the system by dividing thestate into zones with democratically elected councils. Each council has
been given authority over rules that have principally local impacts—trap
limits, days and times ﬁshed, and so forth. Interestingly, the formalizationof local zones was followed almost immediately by the creation of an
informal council of councils to address problems at a greater than local
scale (J. Wilson 1997).
The system of comanagement of the Paciﬁc salmon ﬁsheries in the stateof Washington is another noteworthy example of an evolving polycentric
system that appears to be working much better than an earlier systemthat was dominated primarily by state and federal agencies (see Singleton
1998). The change in the system came as a result of a major court decisionin the mid-1970s.11 The court held that the twenty-one Indian tribes, who
had signed treaties more than a century before, had protected rights to286

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50 percent of the ﬁsh that passed through the normal ﬁshing areas of thetribes. The decision has required the state to develop a “comanagement”
system that involves both the state of Washington and the twentyone Indian tribes in diverse policy roles related to salmon. The salmon
ﬁsheries are a large, transboundary resource utilized by major commercial
ﬁrms as well as by the Indian tribes. Having the state strongly involvedmeans that it is “safe” for any local group to agree to follow strong conservation practices because they know that other local groups are also
involved in the same conservation practices. At the same time, the earlier
centrally regulated system had focused on the ocean ﬁshery and spent
little time on the fresh-water habitats that are essential to maintain theviability of salmon ﬁsheries over the long term. Individual tribal authorities have concentrated their attention on the speciﬁc stocks and how tomanage these better.
Polycentric systems can generate considerable conﬂict among the various units at multiple levels due to their interdependence. Conﬂicts that
escalate from misunderstandings to ever more serious charges and countercharges that turn to violence are certainly negative processes. Conﬂict
may, on the other hand, generate more information that is useful to participants in their efforts to solve challenging problems. Ebbin (2002, 2004)
has traced the evolution of conﬂict in the comanaged salmon ﬁsheries
along the coast of Washington both as a ﬁshery biologist working withseveral of the tribal organizations and as a researcher conducting extensive interviews with participants at all levels. While the early conﬂict wasframed as a technical problem regarding the knowledge to be used inmanaging the system, later conﬂict “focused on questions of equity andconservation as well as authority and jurisdiction” (Ebbin 2004, 82). The
redeﬁnition of conﬂict in the court system and in other arenas helped tocreate new institutional mechanisms that “changed the rules of the gameand the processes in which new conﬂicts are addressed” (82). After someexperience with the new institutions, even the government ofﬁcials recognized that new information was being generated that initially led to more
conﬂict but eventually led to better management of the stock.12
Coping with potential tragedies of the commons is never easy and neverﬁnished. Now that we know that those dependent on these resources are
not forever trapped in situations that will only get worse over time, weneed to recognize that governance is frequently an adaptive process involving multiple actors at diverse levels. Such systems look terribly messy
and hard to understand. The scholars’ love of tidiness needs to be resisted.
Instead, we need to develop better theories of complex adaptive systems
focused on overcoming social dilemmas, particularly those that have
proved themselves able to utilize renewable natural resources sustainably
over time.

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287

Conclusion
Contextual variables are essential for understanding the initial growthand sustainability of collective action as well as the challenges that longsurviving, self-organized regimes must try to overcome. Simply sayingthat “context matters” is not, however, a satisfactory theoretical approach. In particular, we need to address how context affects the recruitment of individuals who may become conditional cooperators and willing
punishers into an ongoing system and the likelihood that the norms heldby these participants are adopted and strengthened by others in a relevant
population.
Trying to understand why so many diverse institutions are created in
the ﬁrst place, and then the consequences that are engendered in diverse
ecological, social, and economic settings, is a big challenge. Considerable
empirical and theoretical progress has been made and, I hope, adequately
summarized in this volume.
Empirical and theoretical work in the future needs to ask how a large
array of contextual variables affects the processes of teaching and evoking
social norms, of informing participants about the behavior of others andtheir adherence to social norms, and of rewarding those who use social
norms, such as reciprocity, trust, and fairness. We need to understandhow institutional, cultural, and biophysical contexts affect the types ofindividuals who are recruited into and leave particular types of collectiveaction situations, the kind of information that is made available aboutpast actions, and how individuals can themselves change structural variables so as to enhance the probabilities of norm-using types being involved and growing in strength over time.
Further developments along these lines are essential for the development of public policies that enhance socially beneﬁcial, cooperative behavior based in part on social norms. It is possible that past policy initiatives that attempted to solve collective-action problems primarily bychanging extrinsic payoff structures may have been misdirected. Imposing
sanctions and inducements can crowd out the formation of social norms
that can enhance cooperative behavior in their own way. Increasing theauthority of individuals to devise their own rules may well result in processes that allow social norms to evolve and thereby increase the probability of individuals actually solving collective-action problems. There is areal role for legislators and government agency ofﬁcials in solving problems at all levels but they need to follow Mark Moore’s (1995, 20) advice
to become “explorers who, with others, seek to discover, deﬁne, and produce public value.”
Norms of reciprocity and trust are necessary for the long-term sustenance of self-governing regimes. Norms alone, however, are not sufﬁcient

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to support individuals facing the temptations of social dilemmas. Rules
that are fair, effective, and legitimate are necessary complements to sharednorms for sustaining self-governing institutions over time. And, in turn,self-organizing arrangements enable people to learn more about one another’s needs and the ecology around them. Learning problem-solving
skills in a local context generates citizens with more general problemsolving skills that enables them to reach out and more effectively examine
far-reaching problems that affect all peoples living on this earth.

Notes
Chapter One
Understanding the Diversity of Structured Human Interactions
1. The Workshop in Political Theory and Policy Analysis was established at
Indiana University in the 1973–74 academic year. A central interest of all Workshop activities has always been understanding institutions at all scales. Early research focused on polycentric systems in metropolitan areas drawing on Vincent
Ostrom’s earlier work with Charles Tiebout and Robert Warren (1961), “The
Organization of Government in Metropolitan Areas” (see E. Ostrom, Parks, andWhitaker 1974; McGinnis 1999b). Studies conducted at a macrolevel (see Kaminski 1992; V. Ostrom 1997; Loveman 1993; Sawyer 1992) have focused more on
national-level constitutional and collective-choice decisions as these eventually
impinge on the day-to-day decisions of citizens and/or subjects. Studies conducted
at a microlevel (Firmin-Sellers 1996; Gibson 1999; Agrawal 1999; E. Ostrom,
Gardner, and Walker 1994; E. Ostrom 1990) have tended to focus more on
operational-level decisions as they are in turn affected by collective-choice andconstitutional-choice rules—some of which have been crafted at the local level.
2. For earlier discussions see Kiser and Ostrom 1982; E. Ostrom 1986; Oakerson 1992; Gardner and Ostrom 1991; E. Ostrom, Gardner, and Walker 1994;
Crawford and Ostrom 2000.
3. See Diermeier and Krehbiel (2003), who develop an institutional methodology using these four steps in an iterative process.
4. Elements of the framework have been used in teaching both graduate andundergraduate courses at Indiana University since the mid-1970s.
5. See Oakerson 1992; E. Ostrom 1986, 1999; and E. Ostrom, Gardner, andWalker 1994, chap. 2.
6. See discussion in chapter 5 on doing ﬁeldwork related to studying rules andnorms.
7. In an interesting paper, Ahn et al. (2004) examine the impact of an increasein the number of referees in the National Hockey League. Other studies had foundthat during the two seasons of a gradual transition from one to two referees,
where part of the games were played with one referee, players derived more penalty minutes when two referees were on the ice. This is in contradiction to theeconomic theory of crime that predicts a deterrence effect. Ahn et al. found thatover a period of seasons, a deterrence effect can be found at the team level since
the composition of the team was adjusted. At the team level, the amount of penalty
minutes dropped, and salaries of aggressive players, the “goons,” decreased relative to the average.
8. In a sympathetic critique of E. Ostrom, Gardner, and Walker’s (1994) useof the IAD framework, Jonathan Bendor (1995, 189) reﬂects on the confusionthat exists regarding the concept of a framework. “Hard core social scientists,
those believing that even frameworks must generate hypotheses if they are to have

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NOTES TO CHAPTER TWO

any scientiﬁc value, might therefore dismiss the IAD approach as constituting amere list of important variables.” Bendor then argues that it would be premature
to dismiss the IAD approach, but does worry about the problem of the numberof possible rule conﬁgurations that it generates.
9. Chapter 2 of Thomas Dye’s (1981) extensively used textbook, Understanding Public Policy, is devoted to “models of politics.” Included among these “models” are organization charts (22), a framework for analyzing policy processes (24),group theory (26–28), elite theory (29–31), criteria for evaluating policy (31–35),
incrementalism (36–37), game theory (36–40), and systems theory (41–43). No
wonder students ﬁnd it challenging to sort out the difference between frameworks,
theories, and models.
10. As I have indicated elsewhere, I will forever be grateful to Larry Kiser and
Sue Crawford for our long, long conversations, outlines, and rough drafts of chapters that were part of these earlier efforts and to Roger Parks for the many memos
he has sent us to clarify key points and conversations we have had about thesecentral issues. This book would not have been possible without those extensive
efforts and many others by my wonderful Workshop colleagues.

Chapter Two
Zooming In and Linking Action Situations
1. When game theorists ﬁrst formally describe the structure of a game, they
make a distinction between the normal form and the extensive form. An n-player
normal form game is usually speciﬁed as consisting of: (1) a set of players
i = 1, . . . , n; (2) a set Si of strategies for player i = 1, . . . , n. A strategy proﬁlefor the game would be: s = (s1, . . . , sn), where si ∈ Si for i = 1, . . . , n; (3) function
πi : S → R for player i = 1, . . . , n, where S is the set of strategy proﬁles, so πi (s)is player i’s payoff when strategy proﬁle s is chosen (Gintis 2000b, 12). A normal
form game is usually represented in matrix format with the strategies represented
by the rows and columns and the payoffs as the cells.
2. There are also major debates over whether Deep Blue is simply a fast processor or whether it represents a form of “real intelligence.”
3. This game was inspired by a game of STEAL proposed by Plott and Meyer(1975).
4. Hamburger (1979) pointed out that participants in a two-person Prisoner’s
Dilemma are more likely to select cooperative strategies than those in a threeperson PD Game.
5. Scharpf (1997) also examines two-level games.
6. Peter Hupe and Michael Hill (2004) develop a somewhat modiﬁed versionof these three levels that they call constitutive, directive, and operation. They use
these three levels to integrate many of the efforts in the policy science to lay out
the stages of the policy process.
7. The four levels presented here are broadly similar to the four levels of social
analysis presented by Oliver Williamson (2000). His top level is called the embedded level, where informal custom and tradition are located. The next level in his

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291

system is “the institutional environment,” where the formal rules establishing a
judiciary, executive, etc., are found in his L2 (our constitutional-choice level). The
governance level is his L3 (our collective-choice level), and resource allocationand employment is his L4 (our operational level).
8. Ciriacy-Wantrup and Bishop (1975) also recommended three levels when
they described the nested multilevel aspect of rules within organizations. Their
three levels of decision making were: (1) the “operating level,” where decisions
about daily activities were made; (2) the “institutional level,” which regulates thedecisions made at an operational level; and (3) the “policy level,” which focuseson the structure of an organization by creating a charter that has standing withan external government.
9. It is important for the reader to recognize that not all constitutional rules areembodied in a written national constitution. Of course, the constitutions of somecountries are not contained at all in a written constitution. Further, each house of
a bicameral legislature will have its own rules related to the decision rules to befollowed in making collective choices. Each private corporation will have a set of
constitutional rules specifying the rights and duties of corporate ofﬁcials in making
policies for the ﬁrm. Every family will have made some basic rules for how futurefamily policies will be made. These will rarely be made in a formal arena.
10. The decisions reached by members of a legislature will not be stable if opponents of these decisions are able (1) to win a majority of seats in future elections
on promises made to the electorate that they will reverse the earlier decision or(2) to change the constitutions so as to make the decisions unconstitutional. The
stability of decisions in complex modern institutions is dependent not only upon
the preferences and procedures used to organize decision making in one arena,
but upon the entire nested set of arenas (Shepsle 1989).
11. This problem is particularly acute if one were to adopt the “institutionsas-equilibria” approach described brieﬂy in the ﬁrst part of chapter 4. The complexity of the statements that would be required to specify an equilibrium if one
took the institutions-as-equilibria approach are literally beyond comprehension.
12. As Hamilton wrote in The Federalist, no. 37: “All new laws thoughpenned with the greatest technical skills and passed on the fullest and most maturedeliberation, are considered as more or less obscure and equivocal, until their
meaning be liquidated and ascertained by a series of particular discussions andadjudications.”

Chapter Three
Studying Action Situations in the Lab
1. In some versions of this game, the Trustee also receives an endowment.
2. Another major effort to replicate experiments across cultures is the research
conducted in ﬁfteen small-scale and relatively isolated societies (see Henrich et al.
2004). In this case, the experiments included the Dictator and Ultimatum Games(and, in some locations, Public Good Games) where the predictions from traditional game theory using an assumption of selﬁsh participants were very clear. No

292

NOTES TO CHAPTER FOUR

cooperation should be seen in any of these experiments. The predictions, however,were not supported in this major effort. As one of the scholars responsible fororganizing this study, Herbert Gintis (2004, 65) reports: “We found, ﬁrst, that the
self-interested actor model is not supported in any society studied. Second, there
is considerably more behavioral variability across groups than had been found inprevious cross-cultural research. . . . Third, group-level differences in the degreeof cooperation in production and the extent of contact with market economies
explain a substantial portion of the behavioral variations across societies.”
3. Still further experiments with the basic Trust Game have been reported byBolle (1998); Fehr, Gächter, and Kirchsteiger (1996); and Fehr and Gächter (forthcoming). Rieskamp and Gigerenzer (2003) report on a very interesting effort toprogram the heuristics that individuals tend to use when faced with situations
involving the structure of the basic Trust Game. For an overview of this researchtradition, see E. Ostrom and Walker 2003 and the special issue “Trust and Trustworthiness” edited by Bohnet and Croson 2004.
4. James Cox and colleagues at the University of Arizona have embarked on
an ambitious and rigorous program to sort out behavior motivated by norms suchas trust and reciprocity, as contrasted to other-regarding preferences. See Cox
2004; Cox and Deck in press; and Cox and Sadiraj 2004.
5. In the experiments reported herein, we used a return rate of 1 cent per unit.
6. Gürerk, Irlenbusch, and Rockenbach (2004) have examined cooperationlevels in a public good experimental environment. They ﬁnd cooperation reaches
maximum levels when subjects can themselves adopt and use a costly sanctioning
system. Not only is cooperation at a maximum; sanctioning costs are very low at
the end of thirty periods. See also Maier-Rigaud and Apesteguia 2003.
7. Casari and Plott (2003) also changed the instructions given to players so as
to make the differences between the three conditions very distinct. In general, thebroad pattern of behavior in the base condition was very similar to our own earlier
experiments.
8. A form of retribution, called a grim trigger strategy, was occasionally discussed in communication rounds but never deployed. Grim trigger strategies arethose that cooperate until someone defects and then never cooperate again. They
have been posited to be one of the strategies that, if known to be followed by someparticipants, will lead others to cooperate and thereby solve a social dilemma (seeE. Ostrom, Gardner, and Walker 1994, chap. 7).

Chapter Four
Animating Institutional Analysis
1. It is also necessary to adopt a “solution concept,” such as the core that isused in cooperative game theory (Shepsle 1975) or the Nash equilibrium that isused in noncooperative game theory (Calvert 1995). Both are used extensively ininstitutional theory to generate predictions (see Diermeier and Krehbiel 2003 for
a comparison of these two solution concepts as used in institutional theories).
Skyrms (1997) makes a powerful critique of how equilibrium concepts are casually used in the social sciences without positing a dynamic process that would lead

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293

to a particular equilibrium. Animating institution theories, however, comes priorto choosing solution concepts.
2. Jerome Bruner (2004) provides an excellent overview of the diverse learningtheories—molecular associationism, molar conﬁgurationism, associative bonds,
conditioned reﬂex, gestalt theory, behavioralism, psycholoinquistics—of thelast century and a half that have loosely led to a better understanding of how
humans learn.
3. Mike McGinnis has pointed out to me that many of the situations that aremodeled as if they were simple situations are actually very complex when one
studies them in the ﬁeld. Thus, the notion of a simple situation is one that we canuse in analyzing models of a situation, but rarely in analyzing ﬁeld settings. I
deeply appreciate the exchange of memos and discussions I have had with Mike
regarding this issue.
4. Classical utility theory did not make this assumption. In fact, little effort
was devoted to where utility came from until researchers tried to test propositions
derived from theory and have had to make a speciﬁc assumption as to the direct,
monotonic linkage between external, objective payoffs and internal utility valuations (see V. Smith 1982). It is well known that without the second assumption,
it is difﬁcult to derive hypotheses about human behavior that can be empiricallytested. In fact, it is impossible to test the proposition that individuals maximize
utility without obtaining some objective measure of utility. A variety of recent
theoretical efforts have explored different valuation assumptions by linking utility
overtly to the distribution of objective payoffs to self and others (see Fehr andGächter 1998, 2000a, for examples).
5. See Rabin 1998 and Kahneman and Tversky 2000 for recent reviews;
Kahneman, Knetsch, and Thaler 1990 for a description of the endowment anomaly; and Grether and Plott 1979 for a discussion of the preference reversal anomaly. Hodgson 2004b also makes a cogent argument that the model of opportunistic behavior, which is an extension of the rational egoist to behavior within ﬁrms
(Williamson 1985), places far too much emphasis on a single motivation as theprimary source of contractual difﬁculties leading to the creation of ﬁrms.
6. In an ingenious experiment with monkeys as subjects, Sugrue, Corrado, andNewsome (2004) examined how the history of past choices and rewards affects
the internal representation of the expected value of alternative potential moves.
By training monkeys in a dynamic foraging environment, their eye movements
and behavior provided a window into their subjective valuation. The researchers
provided strong support that their subjects were using cognitive mechanisms leading them to match time spent in foraging at a particular site in proportion to theabundance of resources available at that site.
7. Thanks to John Schiemann for further clariﬁcation of this point via ane-mail communication.
8. In a thoughtful article on the development of shared norms in a community,
Gibbard (1990, 798) places considerable emphasis on the importance of ritual asa vivid symbol. “A vivid symbol, after all, is a stimulus that commands attention
and generalizes of itself. . . . Why are ritual and symbols so pervasive in human
life? Perhaps because psychic mechanisms that respond to them stabilize cooperative arrangements and guard an individual’s place in those arrangements.”

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NOTES TO CHAPTER FOUR

9. Recent research using brain-imaging technology (Hasson et al. 2004; Pessoa
2004) investigated whether the brains of different subjects, who watched a vivid
thirty-minute segment of the movie “The Good, the Bad, and the Ugly” hadsimilar activation patterns. They found a relatively substantial correlation between the signals generated by one brain with those of another subject watching
the same segment. Hasson et al. (2004) found that a major component of brain
activation occurred when emotionally salient segments of the movie—e.g., scenes
containing gunshots and explosions—occurred. They also recorded strong responses when subjects viewed faces. While Hasson et al. did ﬁnd considerable
evidence that regions of individual brains “ticked together,” they also found that
substantial portions of cortex activation could not be predicted from another subject’s responses.
10. Vernon Smith (2001, 21) has reﬂected that we all function in at least two
worlds: “one of personal exchange governed by self-policed norms of reciprocity(positive and negative) in which there is much intentional cooperation generating
gains from such trade; another of impersonal exchange through markets governedby constantly evolving cultural rules invented by no one person, in which unintentionally, and without awareness, we cooperate by pursuing our own self-deﬁned
interests. Each world is a complex self-ordering system to which we, our parents,
our parents’ parents, etc., have adapted and contributed to its evolution.”
11. Although the discussion in chapter 3 focused on the Trust Game and Commons Dilemma, the large number of experiments on the Ultimatum Game and theDictator Game also challenge the predictions derived from using rational egoist
assumptions about strategies and outcomes (Camerer 2003; Cox 2004).
12. Recent research by Brandts, Saijo, and Schram (2004) conduct the same
experimental social dilemma (in this case, a linear, voluntary contribution, public
goods game) in Japan, the Netherlands, Spain, and the United States. They foundonly minor differences in the level of cooperation in all four countries.
13. Cameron (1999), for example, conducted ultimatum experiments in Indonesia and was able to use sums that amount to three months’ wages. In this extremely tempting situation, she still found that 56 percent of the Proposers allocated between 40 and 50 percent of this very substantial sum to the Responder.
14. See Rothstein 1998, 2005; Levi 1997a; Frohlich and Oppenheimer 1996.
Many scholars are also engaged in serious theoretical efforts to try to understandhow humans use norms (see, in particular, Falk, Fehr, and Fischbacher 2002;
McCabe and Smith 2003; Frey 1997a; Ben-Ner and Putterman 2000a, 2000b;
Casari and Plott 2003; Rothstein 2005).
15. Orbell and Dawes (1991) made a cogent theoretical argument that individuals project their own normative preferences onto others. Glaeser et al. (2000)
observed a high correlation between those who were trustworthy in sequential
trust settings and those who were also more likely to be trusting. Esarey and Ahn(2004) ﬁnd that when no information is provided about the prior behavior of
subjects in the second mover’s position, that subjects who are themselves trustworthy tend to trust others at a signiﬁcantly higher rate. Brosig (2002) ﬁnds that
communication enables subjects to signal intentions accurately.
16. It is always a challenge to understand which heuristic is being used in aparticular setting. Jager and Janssen (2003) assume that cognitive costs affect this

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295

choice. In their analysis, an individual is likely to evaluate heuristics based on theirsatisfaction with outcomes achieved and the certainty with which a heuristic leads
to satisfactory results.
17. The intrinsic cost or anguish that an individual suffers from failing to use
a norm, such as telling the truth or keeping a promise, is usually referred to as
guilt, if primarily self-inﬂicted, or as shame, when the knowledge of the failure iswell known by others (Posner and Rasmusen 1999). While guilt may be selfinﬂicted, extensive research by psychologists have led to a recognition that it arises
out of interpersonal transitions and varies by interpersonal context (Baumeister,
Stillwell, and Heatherton 1994). The same action generates guilt in one situation(e.g., the immediate family), but not in another (e.g., the workplace) (Millar andTesser 1988).
18. In chapter 5, where we deﬁne delta parameters in more detail, we makea further distinction between those parameters that are invoked by internalmechanisms (e.g., through guilt) and those that are invoked by external mechanisms (e.g., gossip leading to shame, etc.). For simplicity, I do not make a distinction here.
19. Gangs in urban areas can be thought of as a clique of participants who
obtain positive rewards from doing acts that others think are prohibited. The
positive internal reward is magniﬁed by the increase in the esteem of other gangmembers for the perpetrator of these acts.
20. Riolo, Cohen, and Axelrod (2001) have shown how individuals who engage in image scoring (Nowak and Sigmund 1998) can contribute to others’ welfare without any need for internal norms of reciprocity or even repeat encounterswith the same individual. What is required is that all individuals carry arbitrary
tags (Holland 1995), and that individuals at the beginning of an evolutionary
process tend to donate resources to others who are “similar” to them—meaning
they carry some or all of the same tags. They show that within a few hundred
generations, a cluster of cooperative individuals reaching up to 80 percent of the
population can emerge, be challenged by a new cluster who have inherited a different set of tags, and then be replaced by the new cluster as it grows to about 80
percent of the population (442). Such models are a useful starting point for thinking about competition and relative survival rates as among different strategies
over a long time period.
21. An excellent example of the internal accounts that individuals develop
without much self-conscious thought was conducted among a small group of sugarcane cultivators in Ecuador (Price, in press). The tradition in this region is toorganize regular mingas where attendance is required and all are expected to work
toward achieving a collective outcome such as clearing the weeds out of a sugarcane ﬁeld with machetes. Since mingas were held once or twice a week, workers
could become familiar with the effort that others regularly expended. Price asked
a set of workers to rank the effort that others regularly expended and comparedthese individual perceptions with data obtained from systematic measurement of
the same workers’ effort in six observed mingas. Price found a high correlation
between these perceptions of work effort and objective measures of effort.
22. The Journal of Economic Theory devoted a special issue in 2001 to theevolution of preferences (vol. 97, no. 2). See also Orbell et al. 2004 for a rigorous

296

NOTES TO CHAPTER FIVEand imaginative use of simulation of evolutionary processes to address how cooperative dispositions may have evolved. As Werner Güth had kindly pointed out tome in correspondence, preference evolution is only one of the possibilities open
to models that use an indirect evolutionary approach. One can make other aspectsof participant decision making endogenous as well, such as beliefs and timing ofdecisions.
23. Eshel, Samuelson, and Shaked (1998) develop a learning model where apopulation of Altruists who adopt a strategy of providing a local public goodinteracts in a local circular neighborhood with a population of Egoists who
free ride. In this local interaction setting, Altruists’ strategies are imitated sufﬁciently often in a Markovian learning process to become one of the absorbing
states. Altruists interacting with Egoists in a larger environment are not so likely
to survive.
24. This implies that in a game where players know only their own payoffs
and not the payoffs of others, that they are more likely to behave like rational
egoists. McCabe and Smith (2003) show that players tend to evolve toward thepredicted, subgame perfect outcomes in experiments where they have only private
information of their own payoffs and to cooperative outcomes when they haveinformation about payoffs and the moves made by other players (see also
McCabe, Rassenti, and Smith 1996).
25. Further, Kikuchi, Watanabe, and Yamagishi (1996) have found that those
who express a high degree of trust are able to predict others’ behavior more accurately than those with low levels of trust.
26. To examine the frequency of nonrational egoist preferences, a group of 181
undergraduates were given a questionnaire containing a similar payoff structureon the ﬁrst day of classes at Indiana University in January 1999 (Ahn et al. 2003).
They were asked to rank their preferences. In this nondecision setting, 52 percent
reﬂected preferences that were not consistent with being rational egoists (27 percent ranked the outcome [C,C] over [D,C] and 25 percent were indifferent).
27. See also Esarey and Ahn (2004), who report on a critical experiment designed to test the capability of traditional game-theoretic predictions as contrasted
to indirect evolutionary theory predictions for a repeated, sequential PD game.
They found more cooperation than predicted by traditional game theory and that
subjects enter the experiment with relatively stable preferences for reciprocity or
being a rational egoist. The behavior of second movers was broadly consistent
with the predictions of indirect evolutionary theory.

Chapter FiveA Grammar of Institutions
1. The grammar of institutions and much of the analysis in this chapter ﬁrst
appeared in print in a 1995 American Political Science Review article. This chapter builds on and adds to our APSR article. In particular, we add several ﬁguresthat had to be cut from the original article due to space limitations and that existed
in an appendix form until they were reprinted in Crawford and Ostrom 2000. In
this chapter, we provide more information on how to use the grammar of institu-

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297

tions, including new discussions of how the grammar relates to the other institutional analysis tools and frameworks described in this book. The grammar in thischapter retains the same logic and basic components as the grammar outlined in
the 1995 article; however, we have reﬁned the grammar a bit based on what wehave learned from scholars using the grammar and what we learned as we worked
through the grammar in the larger context of this book. We have tried in particular
to improve the clarity of the distinction between rules and norms. To use a software analogy, this chapter is Institutional Grammar 2.0 with an expanded instruction book written speciﬁcally for institutional analysts.
2. All of these statements use the notion of a “holder”—a part of the rule thatwill be ﬁlled by concepts that are either deﬁned in general everyday language, suchas age, drive a car, or being in a particular location, or in legal documents, suchas legislation, court decisions, administrative decisions. The fact that all of ourrules involved “holders” is a recognition that ATTRIBUTES, AIMs, and CONDITIONS all refer to concepts that are either deﬁned in everyday language or are
created by a generative rule. The three deontic operators are deﬁned in a formal
language—deontic logic.
3. We can also compare our syntax with that of other scholars. The Dalhousie
logic of rules, for example, uses a similar syntax without an OR ELSE and requiresthat prescriptive statements be recast to use the forbidden DEONTIC operator(see Braybrooke 1996).
4. Our concept of ATTRIBUTES is the same as the wenn component of theDalhousie syntax (Braybrooke 1996).
5. The largest group to which a prescriptive statement could apply is the folk
component in the Dalhousie system (Braybrooke 1996).
6. John R. Commons ([1924] 1968) stressed the correlative nature of rights.
To state that someone has a right, someone must have a duty to observe that right.
The person with the right, then, is permitted to do something, while those withthe duty are forbidden or required to do something.
7. See V. Ostrom and E. Ostrom (1970) for further development of these ideas.
8. The deontic “may” is still meaningful in statements that do not contain an
OR ELSE. Consider a legislative body that shares an institutional statement likethe following: [All junior members] [P] [contest senior members] [in committee
hearings]. This is the equivalent to: [All junior members] [∼F] [contest senior members] [in committee hearings]. This prescription implies a prescription on senior
members not to reprimand or castigate junior members who challenge them incommittee hearings. Of course, the existence of such a norm does not ensure that
senior members will follow it in all instances. However, there will be a sharednotion that a rebuke based on seniority alone is inappropriate or unacceptable. If
a senior member reprimands a junior member, then we might expect the juniormember to use the grant of permission to defend against the senior member’s
actions. It means something for the junior member to say “everyone here knowsthat I am permitted to challenge senior members in committee hearings.”
9. Societies undergoing substantial “liberalization” could be thought of as developing shared understandings that individuals who in earlier times had been
forbidden to take certain actions are now permitted to do so. When the new norm
is shared, individuals who still attempt to obstruct the previously restricted actors

298

NOTES TO CHAPTER FIVE

now face a cost for breaking the norm. Thus, changes in norms over time will be
reﬂected both in the particular deontic assigned to an action and potentially towhom a cost of breaking the norm is assigned.
10. Kreps et al. (1982, 247) do not assume that the basis for one actor playing
tit-for-tat is necessarily the acceptance of a norm. They simply assume that eithersome players have available to them only a tit-for-tat strategy or that there is someprobability that one player’s payoffs are such that tit-for-tat is strongly dominant.
The latter condition would be the case if some players in a population have somecombination of delta parameters associated with playing tit-for-tat whose values
are high enough to make tit-for-tat the dominant strategy for these players. Game
theorists have frequently assumed that such players were somehow “irrational,”
but an institutional explanation would be that such players had accepted a normthat the obligatory way to play a repeated Prisoner’s Dilemma game was to follow
a tit-for-tat strategy.
11. We are, of course, aware that all actions have consequences as pointed out
to us by many of our students—Ryan Adams, in particular. The difference that
the OR ELSE makes is that the consequences speciﬁed by a rule would NOT haveoccurred without the rule being in place and being enforced. Thus, a speciﬁc ruleadds further consequences to those that would occur as a result of individuals
interacting in a situation without that speciﬁc rule.
12. Tsebelis (1989, 1991) argues that in a game with only mixed-strategy equilibria, increasing the size of the OR ELSE does not reduce the level of rule infraction but rather reduces the level of monitoring. Weissing and Ostrom (1991b)
have shown that Tsebelis’s results hold in many but not all cases.
13. Hans Albert (1986, 25) bemoaned the difﬁculty of making any universallybinding demarcation between norms and rules of law. He opted to call prescriptions “law only where they meet certain minimum requirements such as the existence of secondary rules that regulate the identiﬁcation, modiﬁcation and adjudication of the various primary rules in a society” (see also Hart 1961).
14. Or, as we indicated above, the rule or norm backing a rule may reward thesanctioner for taking positive actions rather than punishing the sanctioner forshirking.
15. It would, of course, be possible to include discount rates in the analysis,
but we assume they are zero here to keep the focus on other questions and not
those related to the size of the discount rate. For a discussion of the importanceof discount rates in the analysis of cooperation see Axelrod 1981, 1986.
16. A mixed strategy is a probability distribution over the pure strategies fora player. In a static game, one may view the mixed strategy as the probability ofchoosing one of the other pure strategies. One can also interpret mixed strategies
as behavioral tendencies in a repeated context where the probability of choosing
a pure strategy, say C, is viewed as a cooperation rate.
17. In many farmer-governed irrigation systems, for example, farmers devise
simple rules that are easy to monitor themselves and do not employ any formal
guards. In others, where a guard is employed by the farmers, a frequent payment
to the guard is in a proportion of the yield obtained by the irrigators. Thus, theguard participates in the increased productivity of a system that reduces the rate

NOTES TO CHAPTER SIX

299of stealing water or free-riding on the provision of needed resource inputs to maintain the system (see E. Ostrom 1992a).
18. The delta parameters for player 4 could be disaggregated into their internaland external components for an analysis that wished to focus on questions that
distinguished between internal and external sources of normative constraints related to the Sanctioner.
19. One could argue that when prescriptions are rules, individuals will placehigher values on the deltas than when the prescriptions are norms (see Braybrooke1987). If one assumes that the presence of rules inﬂuences the internal deltas, that δoi
are higher in the Rules Game (ﬁgure 5.6) than in the Norms Game (ﬁgure 5.4), thenthe rule would inﬂuence the structure of the game even when the Monitor fails.
20. In this game, player 3 always correctly detects whether defection has occurred and player 4 only has the option of sanctioning players who have defected.
If players 1 and 2 cooperate, player 4 does not have a choice of whether or not
to sanction. This eliminates issues of false detection and corrupt sanctioners fromthe current analysis but not from future efforts that assumed only a probabilityof correct detection and honest sanctioning.
21. Offe and Wiesenthal 1980; Hirschman 1985; Etzioni 1988; Coleman
1988; Ellickson 1991; Elster 1989b; Knack 1992; Udéhn 1993; Margolis 1991;
E. Ostrom 1990; V. Ostrom 1997; Mansbridge 1990, 1994; Schmid 2004.
22. See E. Ostrom 1986 for a discussion of the conﬁgurational aspect of rules.
23. This logic of the delta parameter is similar to Etzioni’s (1988) discussionof deontology.

Chapter Six
Why Classify Generic Rules?
1. And, it turns out, for other evolving sciences as well. Grimm and Wissel(1997) note a babbling equilibrium problem in ecology, for example, when they
identify 163 deﬁnitions of “stability” in the ecological literature.
2. The latter problem assumed a substantial urgency during the summer of2001 as I was writing an early draft of this chapter. I had taken a new laptop
computer to our Canadian writing cabin on the Manitoulin Island and had notused the new computer with the old printer that I have used there for years. WhenI ﬁrst started to print, out came gibberish that occasionally had a recognizable
word but was generally a mess. After many hours of trying to ﬁx this myself, I took
it to the island’s “Computer Doc,” Roland Panamick. He had to work through adozen hypotheses as to what was the problem. The ﬁrst was a mechanical problem—the printer might have needed to be cleaned. This was eminently reasonable,
given that I had used it for some time without a cleaning. However, in this case,cleaning was not the source of the problem. After that, he began working on thevarious levels of software that drive a computer. It turned out that there was amajor conﬂict occurring in the Windows software commands for a printer andthe software needed to run my old-fashioned dot matrix printer. After digging
down layer after layer of software commands, he was ﬁnally able to make myprinter work. If he had not understood which software commands did what and300

NOTES TO CHAPTER SEVEN

how they ﬁt (or did not ﬁt) together, he could not have ﬁxed this system for me.
I will always remember the several hours I spent in his shop, both for the terror
in my heart that he would not be able to ﬁx it (meaning I would be stuck withouta printer for several more weeks) and for the lesson he gave me in the importanceof understanding the multiple languages that create the structure that I use all thetime and take pretty much for granted.
3. See, for example, the seven market rules that Gode and Sunder (1997) use
in their analysis “What Makes Markets Allocationally Efﬁcient?” and the ﬁfteen
types of rules that Libecap (1996, 44) examines to understand the evolution ofmining law in the American West.
4. Readers who would like to pursue the impact of the “lack of agreement” inthese earlier studies are invited to look at E. Ostrom 1986, where I do discuss theimpact of these rules in some depth. It has also been reprinted in McGinnis 2000,
chap. 3.

Chapter SevenClassifying Rules
1. See Benda-Beckmann 2000 for critique of the simple dichotomy of public
versus private as an adequate conceptual foundation for comparative legal research. Schmid (1999) makes a strong critique of the tendency to talk about government versus markets.
2. Since strategies and norms also have AIMs, we could also use the same wayof classifying them. Given the focus of this volume, however, we are most interested in understanding the prescriptions that create the structure of an action situation and will not focus here on the way that individuals adopt normative values(internal delta components) in relation to actions they feel that must, must not,or may take or outcomes to which similar prescriptions have been attached.
3. The classes of rules proposed in this chapter are somewhat similar to the ﬁve“dimensions” of rules that Koremenos, Lipson, and Snidal (2001, 763) propose as
being basis for the rational design of institutional institutions:
Membership rules (MEMBERSHIP)
Scope of issues covered (SCOPE)
Centralization of tasks (CENTRALIZATION)
Rules for controlling the institution (CONTROL)
Flexibility of arrangements (FLEXIBILITY)

The classiﬁcation used in this chapter does have a distinct theoretical foundation—that rules do directly, and indirectly, affect the elements of an action situation, and thus we focus ﬁrst on the seven rules that have this direct impact.
4. Anthony Giddens (1979, 65) expressed the conﬁgural nature of rules when
he stated: “There is not a singular relation between ‘an activity’ and ‘a rule,’ as issometimes suggested or implied by statements like ‘the rule governing the Queen’s
move’ in chess. Activities or practices are brought into being in the context ofoverlapping and connected sets of rules, given coherence by their involvement in
the constitution of social systems in the movement of time.”

NOTES TO CHAPTER SEVEN

301

5. Anthony Giddens (1979, 117) deﬁned a position in a similar manner, as “asocial identity that carries with it a certain range . . . of prerogatives and obligations that an actor who is accorded that identity (or is an ‘incumbent’ of that
position) may activate or carry out: these prerogatives and obligations constitutethe role-prescriptions associated with that position.”
6. When some persons can be forced into a decision situation, unscrupulous participants may initiate formal actions in order to obtain illegal payoffs to let the person
withdraw. Whenever formal rules are written in an overly strict, unenforceable manner, they can be used by street-level bureaucrats to force a shakedown. The New
York “bite” occurs when residents pay building inspectors not to enforce overly strictbuilding codes. Hobbes characterized bad laws as “traps for money.”
7. The four sets of position rules are similar to a set of rules used by Shepsle(1979b) to deﬁne a committee system in a legislature. However, the form of thestatements differs. Shepsle’s formulations state the result of the operation of therule in a situation. Shepsle shows that equilibria can be structurally induced by aparticular combination of rules when the distribution of preferences and a simple
majority-rule institution could not lead to an equilibrium.
8. In earlier papers, this type of rule has been called “authority rule.” Since allrules assign some form of authority to some participant in regard to one of thecomponents of an action situation, we thought it would be clearer to use the term“choice” for this kind of rule.
9. Lack of agreement rules strongly affects the bargaining strength and strategies adopted in labor management disputes. For an interesting empirical study ofthe effect of three different rules specifying what actions can be taken by either
labor or management, see Dannin and Singh forthcoming.
10. President Clinton issued Executive Order No. 12866, which was retainedby President Bush. The order directs regulatory agencies to specify performance
objectives whenever possible in new regulations rather than requiring the acts thatmust, or must not, be done.
11. The use of scope rules do date back, however, to one of the earliest-known
government codes—the Hammurabi Code (at <http://www.yale.edu/lawweb/
avalon/medieval/hammenu.htm/>). See Foliente 2000 for a discussion of this code,
as well as recent efforts to devise building codes relying more on scope rules.
12. This is a modiﬁed version originally presented in Gardner and Ostrom 1991.
13. Gode and Sunder (1997, 610–11) analyze the set of rules that they posit isneeded to transform a simple market into an efﬁcient market even with “zerointelligence traders.” They specify the following seven rules as needed to accomplish this task:
1. Voluntary trading rule: traders are free to accept or reject offers.
2. Binding contract rule: bids and asks are binding, i.e., buyers must pay
what they bid; and sellers must sell at what they ask.
3. Price priority rule: higher bids dominate lower bids, and lower asks dominate higher asks.
4. Accumulation rule: the highest bid (and the lowest ask if it is a double
auction) are picked only after all bids (and tasks) have been collected.

302

NOTES TO CHAPTER EIGHT

5. Double auction rule: buyers can bid as well as sellers can ask.
6. Multiple rounds rule: multiple rounds of bids and asks are allowed; i.e.,
if the highest bid is less than the lowest ask, then there are further rounds ofbids and asks.
7. Public bids and asks with bid-ask improvement rule: a bid must be greaterthan previous bids, and an ask must be less than previous asks.
One would also need a judge or some other enforcer to ensure these rules arefollowed.
14. As we discuss in chapters 2 and 5, rules at one level are operational only
when these rules are backed by monitoring and sanctioning rules (or norms) atanother level. To undertake analysis at one level, one may assume that the rules
needed to change a situation at that level will be monitored and enforced. This
helps to simplify the analysis, but the assumption must be thoroughly examinedwhen doing applied work. All too many formal laws have been passed to change
behavior at an operational level without asking how the police and judges at thatlevel will themselves be monitored and sanctioned if they do not apply the rules
appropriately.
15. The extensive historical and analytical review of Greif, Milgrom, and Weingast (1994) of the history of merchant guilds in the medieval era demonstrate that
local rulers frequently did renege on their promises and either let local merchantssteal the goods of an alien trader or conﬁscated their goods for themselves. Greif,
Milgrom, and Weingast argue that to obtain stable, long-term commitment fromrulers required that the merchants themselves organize so as to be able to threaten(and enforce the threat so it was credible) a boycott of all merchants from theorganizing city or region if rulers did not keep their pledges. Such an organization—a merchant guild—could coordinate the actions of its own members so thatrulers who failed to keep their commitments would face serious expected costs.
“To permit an efﬁcient expansion of trade in the medieval environment, there wasa need for an organization that would supplement the operation of a multilateralreputation mechanism by coordinating the responses of a large fraction of themerchants. Only when a coordinating organization exists can the multilateral reputation mechanism potential overcome the commitment problem” (753). To implement such a system would require still a further set of rules beyond those speciﬁed above.

Chapter Eight
Using Rules as Tools to Cope with the Commons
1. McCay and Acheson 1987; Fortmann and Bruce 1988; Berkes 1989; Berkes
et al. 2001; V. Ostrom, Feeny, and Picht 1993; Netting 1993; Bromley et al. 1992;
National Research Council 2002.
2. On the other hand, one can use simpliﬁed models of a policy process asuseful benchmarks to examine deviations observed in the ﬁeld (see Scharpf 1997).
One can then ask: What is it about a setting that leads ofﬁcials and/or appropriators to deviate a little or a lot from a presumed solution?

NOTES TO CHAPTER EIGHT

303

3. Boundary rules are also important in more urban and industrial settings. In
a recent study of the biophysical, social, and institutional factors associated withthe establishment of conjunctive surface and groundwater arrangements, Heikkila(2004) randomly selected seventy groundwater basins in California. Her statistical analysis demonstrated that “institutional settings devised around the boundaries of the resource, which allow water providers to control the resource throughbasin adjudication or special groundwater management districts, can facilitate
conjunctive water management” (111).
4. Faysse (2003) provides a good analysis of these types of rules on the efﬁciency of the allocation process.
5. Lobe and Berkes (2004) provide a detailed study of the padu ﬁshing system
on an island off the coast of the state of Kerala in India. Each local shrimp ﬁsherygovernance system creates a set of ﬁshing locations. All of these systems “attempt
to redistribute the catch fairly among the ﬁshers by rotating access to the ﬁshinglocation” (276). They rotate according to a lottery system run each year that assures an equal opportunity to ﬁsh in the best locations.
6. The water-holding capacity of paddy ﬁelds in Chitwan is also considerably
better than the sloped terraces that characterize Tanahun and other districts inthe middle hills (Shivakoti personal communication).
7. As a ﬁeld researcher, I have witnessed a wide variety of systems that change
their rule structure regularly during a year depending on the seasons. Many such
systems have been developed locally and without knowledge of what others havedone. When I visited several sites in Tarija, Bolivia (near Cochabamba), for example, in the mid-1990s, the local ofﬁcials proudly told me that they had developed
a complex system of land allocation that existed nowhere else on earth! They thendescribed their system of small plots of land allocated as private property during
the good agricultural season of the year. All of the land in the valley was thenconverted to a common-property system during the dry period of each year. This
enabled all of the local farmers to graze animals on the stubble of their agriculturalﬁelds without concern for who owned which plot. It was indeed an ingenious
system well tailored to the local environment. It was, however, not unique given
the long history of the “open ﬁeld system” of Europe (see Dahlman 1980; de
Moor, Shaw-Taylor, and Warde 2002).
8. See, for example, Weimer and Vining 1992. Other, more critical approaches
taken to the study of policy include scholars’ focus on the deliberative processes
involved in efforts to solve problems. See, for example, Dryzek 2000; Elster 1998;
Habermas 1996; Rawls 1971; and Fisher 2003.
9. Plott and Porter (1996) undertook a similar analysis including experiments
examining diverse ways of pricing the use of space stations. They point out theimportance of using lab experiments as a method for avoiding some of the costsof experimenting later in the world using only models as a basis for choosing
policies.
10. See McKean 1992, 2000; Wade 1994; Schlager 1990; Baland and Platteau
1996; Tang 1992; E. Ostrom 1990, 1992b; E. Ostrom, Gardner, and Walker 1994;
Meinzen-Dick, Raju, and Gulati 2002. In addition, see Agrawal 2002 for an excellent discussion of the over thirty variables that one or more scholar has speculated
affect the likelihood of collective action being undertaken.

304

NOTES TO CHAPTER NINE

11. Theesfeld (2004) provides an analysis of these calculations in regard toirrigation in a transitional economy—Bulgaria.
12. See Feeny, Hanna, and McEvoy 1996 for a discussion of these issues related
to the collapse of the California sardine industry.
13. See Libecap 1995 for a discussion of the efforts to use the courts to challenge the validity of de facto governance of inshore ﬁsheries in the U.S.; see also
P. Alexander (1982).

Chapter Nine
Robust Resource Governance in Polycentric Institutions
1. As Jared Diamond (2005) illustrates so dramatically, some ecological disasters might have been avoided if those making major decisions were not isolated
from the impact of their own decisions.
2. On the other hand, this also means that corrupt leaders searching for rulesthat will maximize the returns they obtain over time are not fully able to do acomplete analysis either. Firmin-Sellers (1996) provides a well-documented studyof the failed effort of a strong African leader to gain the beneﬁts he predictedwould come his way when the constitution he strongly supported for the Gold
Coast was approved and put into operation.
3. Recently, considerable attention has also been devoted to the relevance of
these principles for the governance of digital resources (Mueller 2002; Schweik
and Semenov 2003; Schweik, Evans, and Grove forthcoming; Hess and Ostrom
2003; Van Wendel de Joode 2004; Less 2002).
4. Four levels of monitoring were recorded: never, occasionally, seasonally, or
year-round. We dichotomized this variable and recoded seasonal or year-roundmonitoring as “regular” and never or sporadic monitoring as “sporadic.”
5. Lansing (1991) graphically describes how changes in agricultural practices
induced by external agencies supposedly to help farmers in Bali increase theirproductivity almost destroyed irrigation systems that had ﬂourished for centuries.
6. The problem of local units becoming dependent on external funding is not
limited to the funding provided by international aid agencies. Two decades ago,
Sieber (1981) reviewed some of the reverse effects created by domestic U.S. policy.
The supposed aim of Nixon’s “New Federalism” reform was to increase the autonomy of local units and strengthen the overall federal system. A study by Hudson (1980) revealed that the policy has an opposite effect in some cities such as
El Paso. “El Paso is now more dependent, politically and economically, on federal
grants than it was prior to the New Federalism and local autonomy is signiﬁcantly
reduced” (Hudson 1980, 900, quoted in Sieber 1981, 186).
7. Douglas Vermillion (2001) provides a cogent story about this process. In
discussing the Small-Scale Irrigation Turnover Program in Indonesia, he heard
about an earlier effort to increase “farmer participation” in the project. A memberof the project staff
informed farmers in a small scheme located in a hilly area of West Sumatra that acertain, but undisclosed amount of funds were available to make small repairs to theirNOTES TO CHAPTER NINE

305

scheme before full management responsibility would be turned over to them. Farmers
were invited to make a list of priorities for repairs. This was seen as a form of farmer
participation. The farmers responded by generating a long wish list. High on the list
was a curious request for the government to raise the masonry embankment along a
300-meter reach of the upper main canal by about 25 cms. When asked why they requested this, some farmer representatives answered that about 11 years before, thegovernment had ﬁrst installed the masonry embankment. By now the calan had accumulated about 20 cms of silt. They said that if the government raised the embankment
another 25 cms they would probably not have to do any desiltation for another 11
years! (187)

8. See Yoder 1991, Pradhan and Yoder 1989, and Water and Energy Commission Secretariat 1990 for descriptions of a highly innovative and successful program of assisting farmers to design their own institutional rules rather than imposing a set of model bylaws on them.
9. For a refreshing and different view of the importance of village governance,
see Ayo 2002.
10. See Blomquist, Schlager, and Heikkila 2004 for an insightful study of polycentric institutions in the American West. Wagner (in press) provides an excellent
overview of the study of polycentricity. Hong and Page (2004) provide strong
evidence of the superiority of having diverse problem solvers involved in making
decisions about complex systems. While their study focuses on individual decision
makers rather than on the diversity of decision-making units in a polycentric system, the core point of their study is that diversity of skills and backgrounds enhance decision-making performance. This provides another foundation for why
polycentric systems that draw on diversely organized units will outperform singlelayer systems solving the same set of problems over time.
11. This decision was rendered by Judge Boldt of the Western District Court
in 1974 and upheld in 1979 by the Supreme Court (443 U.S. 658 1979).
12. For good analyses of other “co-management” systems on the Paciﬁc Coastof North America, see Pinkerton 1989 and Poffenberger and McGean 1996. Wilson, Nielsen, and Degnbol (2003) provide an excellent collection of empiricalstudies focusing on relative successes and failures of systems that are called “comanagement,” in many parts of the world. Unfortunately, some such systems havefallen into the trap of “blueprint” thinking described above and have lost thevitality of evolving polycentric systems, while others have continued to adapt anddevelop productive institutional structures.

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Index

Abernathy, Charles L., 260
Acheson, James, 230, 236, 284–85
action arena, 13–16, 55
action situation, 32; potential outcomes of,42; participants in, 37–40; opportunityin, 43; repeated interaction in, 53; organizational linkage of, 55
actions, 45
Actor-Centered Institutionalism framework, 29
ADICO, 140–41; in repeated PD game,
158; and ﬁeld research, 171–74
agenda control, 201
aggregation rules, 202–6; nonsymmetric,
202–3; symmetric, 203–4; lack ofagreement, 205
Agrawal, Arun, 227, 234, 251
AIM, 140, 148–49, 186–87
Alchian, Armen, 101
alcoholic beverages, 182–83
Alcorn, Janis B., 284
altruism, 110
ATTRIBUTES, 139, 141; of goods, 23–26;
of community, 15, 26; of biophysicalworld, 26; of participants, 40; of resources, 247–48; of appropriators,
248–54; rapid change of, 272–73
babbling equilibrium. See language
Banana, Abwoli Y., 265
Bardhan, Pranab, 264
Bates, Robert, 277–78
Becker, C. Dustin, 268–69
Berg, Joyce, 72, 77
Berkes, Fikret, 248, 263, 268
biophysical world, 15, 22–24, 26, 42
Black, Max, 16–17
Blomquist, William, 235, 237
blueprint thinking, 274–77
Blurton Jones, Nicholas G., 49
boundary rules, 194–200; ﬁrst order, 195;
second order, 195–96; and invitational
entry, 196; and competitive entry, 196;
and compulsory entry, 196; and succession rules, 198; and exit rules, 198–99;
and common-pool resources, 223–26,

239; residency-based, 225; technologybased, 225
Brander, James A., 249
Brox, Ottar, 273
Buchan, Nancy R., 74–75
Butler, Christopher K., 10
Cadenasso, Mary L., 12
Cain, Michael, 129
Camerer, Colin, 124–25
Cardenas, Juan-Camilo, 93–94
Carson, Richard T., 237
Casari, Marco, 95
case studies, 35
centralized governance, 237–40, 278–79,
283–84cheap talk, 85. See also communication,
game theory
Choe, Jaesong, 269–70
choice rules, 200–2, 228–33
Coglianese, Cary, 209–11
Coleman, James, 9, 146collective choice, 33, 57, 63; rules of, 58–
60; examples of, 60. See also levels ofanalysiscommon-pool resource (CPR), 79–80,
219–54, 280; attributes of, 23–25; public provision of, 24; dilemma, 78–97;
open access, 80; and generic rules, 183;
and ﬁeld research, 221–22; threats to,
271–79Commons, John R., 20, 144–45, 187
communication, 64–65, 98; in Trust Game,
74–75; in Commons Dilemma, 85; faceto-face, 85–87, 93; among heterogeneous participants, 87–89; lack of, 89;
among Columbian villagers, 107; andmental models, 107; in PD game, 130;
intergenerational, 273
community, 15, 26, 274; governance, 220–
1, 243–45, 279–83
complexity, 9–10, 13, 242–43, 256–57,
286
CONDITIONS, 140, 144, 149, 171constitutional choice, 33, 58, 61. See also
levels of analysis

352
contingent action situation, 44
contracting, 215
control, 49–50
control variable, 46
corruption, 277
Cooter, Robert, 166
Crawford, Sue, 121, 137–74, 186–215
Croson, Rachel T., 74–75
culture, 27, 126–27
Deadman, Peter, 84
default conditions, 182, 205, 210–14
de los Reyes, Romana, 228delta parameter, 121–22, 146, 153. See
also norms
Denzau, Arthur T., 105, 107, 109
DEONTIC, 126, 140, 141–42, 172; operators, 142–44; in formal games, 146
design principles, 259; and well-deﬁnedboundaries, 260–62; and equivalence ofcosts/beneﬁts, 262–63; and collectivechoice arrangements, 264–65; and monitoring, 265–66; and graduated sanctions, 266–67; and conﬂict-resolution
mechanism, 267–68; and recognition ofrights, 268–69; and nested enterprises,
269–70; in research, 270–71
Diamond, Jared, 248–49
Dickhaut, John, 72, 75, 77
duties, 145
Ebbin, Syma A., 286
evolutionary theory, 129. See also gametheoryexposure, 145
Fehr, Ernst, 72–73, 76
ﬁsheries, 225–26, 227, 230, 238, 248, 273.
See also Maine lobster ﬁshery, Paciﬁcsalmon ﬁshery
fMRI, 111
foreign donors, 276–78
Forsyth, Timothy, 222
Frey, Bruno, 77, 95, 112–13
Frohlich, Norman, 100, 102, 103, 107–8,
130
game theory, 6–7; decision point, 45; folk
theorem, 54–55; Tit for Tat, 54; inconsistencies of, 98; evolutionary, 116, 125–31; and norms, 120–25; noncooperative,

INDEX

124; and ADICO, 153–65; strategy, 156;
grim trigger strategy, 157
games: bridge, 40; chess, 34–35; Snatch,
35–36; 5-person county council, 46;
Trust Game, 71–78; commons dilemma,
78–97; Snatch with norms, 122–23;
Prisoner’s Dilemma (PD), 129–30,
147–48, 154–55; repeated PD, 56–58;
PD with monitor, monitoring norms,
and sanctions, 162–65; “urban taxi,”
191; Snatch with private property,
211–14
Gardner, Roy, 82, 86, 92–93, 207
Gibbard, Allan, 108
Gibson, Clark, 266, 268–69
Gigerenzer, Gerd, 114
Gode, Dhananjay K., 101
Gombya-Ssembajjwe, William, 265
goods, excludability of, 24–25; subtractability of, 25–26
Grainger, Alan, 237
Grether, David M., 205, 240
grim trigger strategy, 113–14, 157
Güth, Werner, 75, 128
Hackett, Steven, 87
Haley, Sharman, 260
Hammond, Thomas H., 10
Hardin, Garrett, 64
Hawkes, Kristen, 49
Hechter, Michael, 179
Heiner, Ronald, 209
heuristics, 98, 113–16; in Commons
Dilemma, 83; and grim trigger, 113–
14; models of, 115
Hoffrage, Ulrich, 114–16
holons, 11–12, 103. See also levels ofanalysis
Hull, Rebecca Watts, 262
information, 50–51, 102, 104–9
information rules, 206–7
Ingram, Helen M., 237
Institutional Analysis and Development(IAD) framework, 6–7, 9, 27–29
institutional statement, 137, 178. See also
norms, rules, strategies
institutions, 3, 179; diversity of, 4, 181–
82; commonalities of, 5–6, 30, 185; andhuman behavior, 118. See also rules
irrigation, 226, 227–28, 230–33, 251, 261

INDEX

Irrigation Management Systems Study
Group (IMSSG), 231–33
Isaac, R. Mark, 205, 240–41
Jager, Wander, 84, 116
Janssen, Marco A., 84, 116
Jegen, Reto, 112–13
Johnson, Craig, 222
Johnson, Eric J., 74
Jones, Bryan, 109
Karotkin, Drora, 202–3
Kliemt, Hartmut, 128
Knight, Frank, 170
Knox, Anna, 264
Koestler, Arthur, 11–12
Koford, Kenneth, 73–74, 102
Korten, David, 274–75
Kreps, David, 147
Kurki, Anja, 102
Lam, Wai Fung, 222, 251
language, 27, 126; and babbling equilibrium, 176–80; in rules, 180–81; andslogans, 181, 256
learning, 106, 128
levels of analysis, 56, 214–15; and IAD,
8; and holons, 11–12; choosing levels in
research, 12, 61, 172; and nested rules,
58–60, 187; shifting among levels, 62–
64. See also operational choice, collective choice, constitutional choiceLevi, Margaret, 167, 267
Levin, Jonathan, 200
Lewis, David, 176–77
Libecap, Gary, 248, 250, 251
liberty, 145, 170
Lobe, Kenton, 268
local governance, 220–21, 243–45, 279–83
Maine lobster ﬁsheries, 6, 248, 284–85
Margolis, Howard, 167–68
Marinova, Nadja, 237
Marwell, Gerald, 10, 252
Maynard Smith, John, 126
Mayntz, Renate, 29
McAdams, Richard, 179
McCabe, Kevin, 72, 77
McGinnis, Michael, 45, 270
Meinzen-Dick, Ruth, 264
migration, 161353
models, 28; of human behavior, 124; ofrules, 240–41
monitor, 158, 162, 227
monitoring, 265–66; of rules, 21; andvoter turnout, 168
Morrow, Christopher E., 262
Moxnes, Erling, 238
Nalebuff, Barry, 202
Nash, Jennifer, 209–10
niches, 124
norms, 112, 119, 123–24, 147; in social
dilemmas, 120; as payoffs, 121–22, 146;
evolution of, 123–27; no agreed deﬁnition of, 178–79
North, Douglass C., 105, 107, 109, 179
Ockenfels, Peter, 75
O’Connell, James F., 49
Offe, Claus, 168
Oliver, Pamela, 10, 252
Olmstead, Todd, 209–10
Olson, Mancur, 253
operational choice, 34, 55; rules of, 58;
examples of, 60. See also levelsof analysis
operator. See DEONTIC
Opp, Karl-Dieter, 179
Oppenheimer, Joe A., 100, 102, 103,
107–8, 130
OR ELSE, 140, 149–52. See also sanctions
Orbell, John, 41–42
organization, 57, 179
Ostrom, Elinor, 28, 29, 30, 82–84, 86, 92–
93, 121, 175, 207, 224–25, 241–42,
253, 257, 266
Ostrom, Vincent, 20, 181
outcomes, 43–44, 64–65, 66, 233–36
Paciﬁc salmon ﬁshery, 285–86
Paroush, Jacob, 202–3
participants, 38–40; standing of, 41; assumptions of 103–4, 117–18; information process of, 104–9; valuation processof, 109–13; selection process of, 113–16
payoff rules, 42–43, 52, 233
permission rules, 144–46
Pickett, Steward T. A., 12
Plott, Charles, 95, 182, 205, 240–41
policy design, 240–43, 255–58, 274–77
polycentricity, 269–70, 281–86

354
positions, 40, 193–94, 197, 227–28
Poteete, Amy, 253
power, 50
principal-agent problem, 51
Pritchett, Lant, 275
private property rights, 21–22, 29, 189–
90, 211–14, 237
public goods, 56, 23–25
quotas, 191–92, 230–31
Rapoport, Anatol, 17–18
Rasmusen, Eric, 179
rational egoist, 101, 127–28. See also
rationality
rationality, 101; and Trust Game, 78; incompetitive markets, 99–101; alternative
assumptions and, 103–4; in nonmarket
settings, 120
Ray, Isha, 264
reciprocity, 76–77, 112
redundancy, 284
Repetto, Fabián, 275
reputation, 27
reversion level, 182. See also default
conditions
Rieskamp, Jörg, 114–16
rights, 144–46
Rilling, James K., 111
risk, 48
ritual, 108
robustness, 243, 258–60, 271–79
Rocco, Elena, 93
Rockenbach, Bettina, 76
Roconi, Lucas, 275
Rolett, Barry, 248
rules, 16–18; origin of, 19; in use, 19, 138,
166; predictability of, 20; enforcementof, 21, 130–31; external, 130; changing,
138, 212–13, 215, 243–45, 245–47; generative or regulatory, 138; legitimacy of,
167; lack of agreed deﬁnition of, 178; semantics of, 180–81; variety of, 181–82;
and information transformation, 184–
85; types of, 190; interdependency of,
191; permission, 144–46; position, 193–
94, 227–28; boundary, 194–200, 223–
26; choice, 200–2, 228–33; aggregation,
202–6, 235–36; information, 206–7; payoff, 207–8, 233–34; scope, 208–10; suboptimal selection of, 220, 236, 243, 254,
256, 270; and CPR, 222–23

INDEX

salience, 107
Sally, Hilmy, 260
sanctions, 92–93, 95–96; external, 77, 94–
95, 130–31; in Commons Dilemma, 79,
91–92; in Italian Alps, 95–96; graduated, 152, 266–67; severity of, 152
Scharpf, Fritz, 10, 29, 39
Schiemann, John, 107
Schlager, Edella, 87, 225–26, 230, 233
Schmitt, Pamela, 88
Schwartz-Shea, Perry, 41–42
scientiﬁc management, 236–37; andrational egoists, 238; and information
problems, 238; and central management, 240
Sekher, Madhushree, 264
self-organization, 220–21, 243–45; andgroup size, 251–52; and group heterogeneity, 252–53
Sen, Amartya, 111–12
Sengupta, Nirmal, 234
Shepsle, Kenneth, 258
Shukla, Ashutosh, 264
signaling, 128, 176–77
Simmons, Randy T., 41–42
Simon, Herbert, 270–71
Smith, Vernon, 69
social dilemma, 37, 79; and collective
action, 119–21; and evolutionary cooperation, 127
spillovers, 249, 279
Steneck, Robert S., 230, 236, 284–85
Stranlund, John K., 94
strategies, 156, 178
suboptimal solutions, 220, 236, 243, 254,
256, 270
Sunder, Shyam, 101
Swope, Kurtis, 88
Szathmáry, Eörs, 126
Tang, Shui Yan, 226, 228, 230–32, 233,
251
Taylor, M. Scott, 249
Theesfeld, Insa, 280
theory, 28
Toledo, Victor, 284
transmission failures, 273–75
trust, 98, 74. See also games
Tucker, Catherine M., 270
unanimity rule, 182, 203–4
uncertainty, 48–49

355

INDEX

U.S. Geological Survey, 278–79
utility, 53
valuation, 43, 52; intrinsic, 53, 113, 119,
123–24, 146–47; process of, 109; heterogeneity of, 123–24, 147
Viek, Charles A. J., 116
vividness, 107–8
Vogt, Nathan, 262
von Wright, Georg Henrik, 142
Wade, Robert, 234
Walker, James, 82, 86, 87, 88, 92–93, 207
Warglien, Massimo, 93
Weinstein, Martin S., 260
Weissing, Franz J., 28

Wendel, Markus, 75
Wiesenthal, Helmut, 168
Wiggins, Steven N., 248
Williams, Jeffrey, 264
Williams, John, 45, 266
Willis, Cleve E., 94
Wilson, James A., 230, 236, 284–85
Woolcock, Michael, 275
Workshop in Political Theory and Policy
Analysis, 1, 34, 222
World Bank, 275. See also foreign
donorsYadama, Gautam N., 227
Zilberman, David, 237