The Sciences of the Artificial
Third edition
Herbert A. Simon

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The Sciences of the Artificial
Simon, Herbert Alexander.
MIT Press
0262193744
9780262193740
9780585360102
EnglishScience--Philosophy.
1996
Q175.S564 1996eb
300.1/1
Science--Philosophy.

Page iv

© 1996 Massachusetts Institute of Technology
All rights reserved. No part of this book may be reproduced in any form by any
electronic or mechanical means (including photocopying, recording, orinformation storage and retrieval) without permission in writing from thepublisher.
This book was set in Sabon by Graphic Composition, Inc.
Printed and bound in the United States of America.
Library of Congress Cataloging-in-Publication Data

Page v

Simon, Herbert Alexander, 1916
The sciences of the artificial / Herbert A. Simon.3rd ed.
p. cm.
Includes bibliographical references and index.
ISBN 0-262-19374-4 (alk. paper).ISBN 0-262-69191-4 (pbk.: alk.
paper)
1. Science Philosophy. I. Title.
Q175.S564 1996
300.1'1dc20 96-12633
CIP

Page viTo
Allen
Newellin memory of a friendshipPage vii

Contents
Preface to Third Editionix

Preface to Second Editionxi

1
Understanding the Natural and Artificial Worlds

1

2
Economic Rationality: Adaptive Artifice25

3
The Psychology of Thinking: Embedding Artifice in Nature

51

4
85
Remembering and Learning: Memory As Environment forThought
5
The Science of Design: Creating the Artificial

111

6Social Planning: Designing the Evolving Artifact

139

7
Alternative Views of Complexity

169

8
The Architecture of Complexity: Hierarchic Systems

183

Name Index

217

Subject Index

221

Page ix

Preface to Third Edition
As the Earth has made more than 5,000 rotations since The Sciences of the
Artificial was last revised, in 1981, it is time to ask what changes in our
understanding of the world call for changes in the text.
Of particular relevance is the recent vigorous eruption of interest in complexity
and complex systems. In the previous editions of this book I commented only
briefly on the relation between general ideas about complexity and the particularhierarchic form of complexity with which the book is chiefly concerned. I now
introduce a new chapter to remedy this deficit. It will appear that the devotees ofcomplexity (among whom I count myself) are a rather motley crew, not at all
unified in our views on reductionism. Various among us favor quite different
tools for analyzing complexity and speak nowadays of "chaos," "adaptive
systems," and "genetic algorithms." In the new chapter 7, "Alternative Views ofComplexity'' ("The Architecture of Complexity" having become chapter 8), I sortout these themes and draw out the implications of artificiality and hierarchy forcomplexity.
Most of the remaining changes in this third edition aim at updating the text. In
particular, I have taken account of important advances that have been made since
1981 in cognitive psychology (chapters 3 and 4) and the science of design(chapters 5 and 6). It is gratifying that continuing rapid progress in both of these
domains has called for numerous new references that record the advances, whileat the same time confirm and extend the book's basic theses about the artificial
sciences. Changes in emphases in chapter 2 reflect progress in my thinking about
the respective roles of organizations and markets in economic systems.

Page xThis edition, like its predecessors, is dedicated to my friend of half a lifetime,
Allen Newell but now, alas, to his memory. His final book, Unified Theories of
Cognition, provides a powerful agenda for advancing our understanding ofintelligent systems.
I am grateful to my assistant, Janet Hilf, both for protecting the time I haveneeded to carry out this revision and for assisting in innumerable ways in getting
the manuscript ready for publication. At the MIT Press, Deborah Cantor-Adams
applied a discerning editorial pencil to the manuscript and made communication
with the Press a pleasant part of the process. To her, also, I am very grateful.
In addition to those others whose help, counsel, and friendship I acknowledgedin the preface to the earlier editions, I want to single out some colleagues whose
ideas have been especially relevant to the new themes treated here. These include
Anders Ericsson, with whom I explored the theory and practice of protocol
analysis; Pat Langley, Gary Bradshaw, and Jan Zytkow, my co-investigators of
the processes of scientific discovery; Yuichiro Anzai, Fernand Gobet, Yumi
Iwasaki, Deepak Kulkarni, Jill Larkin, Jean-Louis Le Moigne, Anthony
Leonardo, Yulin Qin, Howard Richman, Weimin Shen, Jim Staszewski, Hermina
Tabachneck, Guojung Zhang, and Xinming Zhu. In truth, I don't know where toend the list or how to avoid serious gaps in it, so I will simply express my deep
thanks to all of my friends and collaborators, both the mentioned and theunmentioned.
In the first chapter I propose that the goal of science is to make the wonderful
and the complex understandable and simple but not less wonderful. I will bepleased if readers find that I have achieved a bit of that in this third edition of
The Sciences of the Artificial.
HERBERTA.
SIMON
PITTSBURGH, PENNSYLVANIAJANUARY 1, 1996

Page xi

Preface to Second Edition
This work takes the shape of fugues, whose subject and counter subject were firstuttered in lectures on the opposite sides of a continent and the two ends of adecade but are now woven together as the alternating chapters of the whole.
The invitation to deliver the Karl Taylor Compton lectures at the Massachusetts
Institute of Technology in the spring of 1968 provided me with a welcome
opportunity to make explicit and to develop at some length a thesis that has been
central to much of my research, at first in organization theory, later in economics
and management science, and most recently in psychology.
In 1980 another invitation, this one to deliver the H. Rowan Gaither lectures at
the University of California, Berkeley, permitted me to amend and expand thisthesis and to apply it to several additional fields.
The thesis is that certain phenomena are "artificial" in a very specific sense: they
are as they are only because of a system's being moulded, by goals or purposes,to the environment in which it lives. If natural phenomena have an air of"necessity" about them in their subservience to natural law, artificial phenomena
have an air of "contingency" in their malleability by environment.
The contingency of artificial phenomena has always created doubts as to whether
they fall properly within the compass of science. Sometimes these doubts refer tothe goal-directed character of artificial systems and the consequent difficulty ofdisentangling prescription from description. This seems to me not to be the realdifficulty. The genuine problem is to show how empirical propositions can bemade at all about systems that, given different circumstances, might be quite
other than they are.

Page xii

Almost as soon as I began research on administrative organizations, some forty
years ago, I encountered the problem of artificiality in almost its pure form:. . . administration is not unlike play-acting. The task of the good actor is to know and play his role,
although different roles may differ greatly in content. The effectiveness of the performance will
depend on the effectiveness of the play and the effectiveness with which it is played. The
effectiveness of the administrative process will vary with the effectiveness of the organization andthe effectiveness with which its members play their parts. [Administrative Behavior, p. 252]

How then could one construct a theory of administration that would contain morethan the normative rules of good acting? In particular, how could one construct
an empirical theory? My writing on administration, particularly in Administrative
Behavior and part IV of Models of Man, has sought to answer those questions byshowing that the empirical content of the phenomena, the necessity that rises
above the contingencies, stems from the inabilities of the behavioral system toadapt perfectly to its environment from the limits of rationality, as I have called
them.
As research took me into other areas, it became evident that the problem of
artificiality was not peculiar to administration and organizations but that it
infected a far wider range of subjects. Economics, since it postulated rationality
in economic man, made him the supremely skillful actor, whose behavior could
reveal something of the requirements the environment placed on him but nothing
about his own cognitive makeup. But the difficulty must then extend beyond
economics into all those parts of psychology concerned with rational behavior
thinking, problem solving, learning.
Finally, I thought I began to see in the problem of artificiality an explanation of
the difficulty that has been experienced in filling engineering and otherprofessions with empirical and theoretical substance distinct from the substanceof their supporting sciences. Engineering, medicine, business, architecture, andpainting are concerned not with the necessary but with the contingent not withhow things are but with how they might be in short, with design. The possibilityof creating a science or sciences of design is exactly as great as the possibility ofcreating any science of the artificial. The two possibilities stand or fall together.
These essays then attempt to explain how a science of the artificial is possible
and to illustrate its nature. I have taken as my main examples thePage xiii

fields of economics (chapter 2), the psychology of cognition (chapters 3 and 4), andplanning and engineering design (chapters 5 and 6). Since Karl Compton was a
distinguished engineering educator as well as a distinguished scientist, I thought it notinappropriate to apply my conclusions about design to the question of reconstructing
the engineering curriculum (chapter 5). Similarly Rowan Gaither's strong interest in
the uses of systems analysis in public policy formation is reflected especially inchapter 6.
The reader will discover in the course of the discussion that artificiality is interesting
principally when it concerns complex systems that live in complex environments. The
topics of artificiality and complexity are inextricably interwoven. For this reason I
have included in this volume (chapter 8) an earlier essay, "The Architecture ofComplexity," which develops at length some ideas about complexity that I could touchon only briefly in my lectures. The essay appeared originally in the December 1962
Proceedings of the American Philosophical Society.
I have tried to acknowledge some specific debts to others in footnotes at appropriate
points in the text. I owe a much more general debt to Allen Newell, whose partner I
have been in a very large part of my work for more than two decades and to whom I
have dedicated this volume. If there are parts of my thesis with which he disagrees,
they are probably wrong; but he cannot evade a major share of responsibility for the
rest.
Many ideas, particularly in the third and fourth chapters had their origins in work thatmy late colleague, Lee W. Gregg, and I did together; and other colleagues, as well as
numerous present and former graduate students, have left their fingerprints on various
pages of the text. Among the latter I want to mention specifically L. Stephen Coles,
Edward A. Feigenbaum, John Grason, Pat Langley, Robert K. Lindsay, David Neves,
Ross Quillian, Laurent Siklóssy, Donald S. Williams, and Thomas G. Williams, whose
work is particularly relevant to the topics discussed here.
Previous versions of chapter 8 incorporated valuable suggestions and data contributedby George W. Corner, Richard H. Meier, John R. Platt, Andrew Schoene, Warren
Weaver, and William Wise.
A large part of the psychological research reported in this book was supported by the
Public Health Service Research Grant MH-07722 from the National Institute of
Mental Health, and some of the research on

Page

xiv

design reported in the fifth and sixth chapters, by the Advanced Research
Projects Agency of the Office of the Secretary of Defense (SD-146). These
grants, as well as support from the Carnegie Corporation, the Ford Foundation,and the Alfred P. Sloan Foundation, have enabled us at Carnegie-Mellon to
pursue for over two decades a many-pronged exploration aimed at deepening ourunderstanding of artificial phenomena.
Finally, I am grateful to the Massachusetts Institute of Technology and to the
University of California, Berkeley, for the opportunity to prepare and present
these lectures and for the occasion to become better acquainted with the researchin the sciences of the artificial going forward on these two stimulating campuses.
I want to thank both institutions also for agreeing to the publication of theselectures in this unified form, The Compton lectures comprise chapters 1, 3, and5, and the Gaither lectures, chapters 2, 4, and 6. Since the first edition of thisbook (The MIT Press, 1969) has been well received, I have limited the changes
in chapters 1, 3, 5, and 8 to the correction of blatant errors, the updating of a few
facts, and the addition of some transitional paragraphs.

Page 1

1
Understanding the Natural and the Artificial Worlds
About three centuries after Newton we are thoroughly familiar with the conceptof natural science most unequivocally with physical and biological science. A
natural science is a body of knowledge about some class of things objects or
phenomena in the world: about the characteristics and properties that they have;about how they behave and interact with each other.
The central task of a natural science is to make the wonderful commonplace: toshow that complexity, correctly viewed, is only a mask for simplicity; to findpattern hidden in apparent chaos. The early Dutch physicist Simon Stevin,showed by an elegant drawing (figure 1) that the law of the inclined planefollows in "self-evident fashion" from the impossibility of perpetual motion, forexperience and reason tell us that the chain of balls in the figure would rotate
neither to right nor to left but would remain at rest. (Since rotation changes
nothing in the figure, if the chain moved at all, it would move perpetually.) Since
the pendant part of the chain hangs symmetrically, we can snip it off without
disturbing the equilibrium. But now the balls on the long side of the planebalance those on the shorter, steeper side, and their relative numbers are in
inverse ratio to the sines of the angles at which the planes are inclined.
Stevin was so pleased with his construction that he incorporated it into a
vignette, inscribing above it
Wonder, en is gheen wonderthat is to say: "Wonderful, but not incomprehensible."This is the task of natural science: to show that the wonderful is notincomprehensible, to show how it can be comprehended but not toPage 2

Figure 1
The vignette devised by Simon Stevin toillustrate his derivation of the law of
the inclined plane

destroy wonder. For when we have explained the wonderful, unmasked thehidden pattern, a new wonder arises at how complexity was woven out of
simplicity. The aesthetics of natural science and mathematics is at one with theaesthetics of music and painting both inhere in the discovery of a partiallyconcealed pattern.
The world we live in today is much more a man-made,1 or artificial, world than it
is a natural world. Almost every element in our environment shows evidence ofhuman artifice. The temperature in which we spend most of our hours is kept
artificially at 20 degrees Celsius; the humidity is added to or taken from the air
we breathe; and the impurities we inhale are largely produced (and filtered) byman.
Moreover for most of us the white-collared ones the significant part of theenvironment consists mostly of strings of artifacts called "symbols" that we
receive through eyes and ears in the form of written and spoken language andthat we pour out into the environment as I am now doing by mouth or hand. The
laws that govern these strings of1.

I will occasionally use "man" as an androgynous noun, encompassing both sexes, and "he," "his,"and "him" as androgynous pronouns including women and men equally in their scope.

Page 3symbols, the laws that govern the occasions on which we emit and receive them,the determinants of their content are all consequences of our collective artifice.
One may object that I exaggerate the artificiality of our world. Man must obey
the law of gravity as surely as does a stone, and as a living organism man must
depend for food, and in many other ways, on the world of biological phenomena.
I shall plead guilty to overstatement, while protesting that the exaggeration isslight. To say that an astronaut, or even an airplane pilot, is obeying the law ofgravity, hence is a perfectly natural phenomenon, is true, but its truth calls forsome sophistication in what we mean by "obeying" a natural law. Aristotle did
not think it natural for heavy things to rise or light ones to fall (Physics, Book
IV); but presumably we have a deeper understanding of "natural" than he did.
So too we must be careful about equating "biological" with "natural." A forest
may be a phenomenon of nature; a farm certainly is not. The very species uponwhich we depend for our food our corn and our cattle are artifacts of ouringenuity. A plowed field is no more part of nature than an asphalted street andno less.
These examples set the terms of our problem, for those things we call artifacts
are not apart from nature. They have no dispensation to ignore or violate naturallaw. At the same time they are adapted to human goals and purposes. They arewhat they are in order to satisfy our desire to fly or to eat well. As our aims
change, so too do our artifacts and vice versa.
If science is to encompass these objects and phenomena in which human purpose
as well as natural law are embodied, it must have means for relating these two
disparate components. The character of these means and their implications forcertain areas of knowledge economics, psychology, and design in particular are
the central concern of this book.
The Artificial
Natural science is knowledge about natural objects and phenomena. We ask
whether there cannot also be "artificial" science knowledge about artificial
objects and phenomena. Unfortunately the term "artificial" has a pejorative air
about it that we must dispel before we can proceed.

Page 4

My dictionary defines "artificial" as, "Produced by art rather than by nature; notgenuine or natural; affected; not pertaining to the essence of the matter." It
proposes, as synonyms: affected, factitious, manufactured, pretended, sham,simulated, spurious, trumped up, unnatural. As antonyms, it lists: actual, genuine,
honest, natural, real, truthful, unaffected. Our language seems to reflect man's
deep distrust of his own products. I shall not try to assess the validity of that
evaluation or explore its possible psychological roots. But you will have tounderstand me as using "artificial" in as neutral a sense as possible, as meaning
man-made as opposed to natural.2
In some contexts we make a distinction between "artificial" and "synthetic." Forexample, a gem made of glass colored to resemble sapphire would be called
artificial, while a man-made gem chemically indistinguishable from sapphirewould be called synthetic. A similar distinction is often made between "artificial"and "synthetic" rubber. Thus some artificial things are imitations of things innature, and the imitation may use either the same basic materials as those in thenatural object or quite different materials.
As soon as we introduce "synthesis" as well as "artifice," we enter the realm ofengineering. For "synthetic" is often used in the broader sense of "designed" or"composed.'' We speak of engineering as concerned with "synthesis," whilescience is concerned with "analysis." Synthetic or artificial objects and more
specifically prospective artificial objects having desired properties are the central
objective of engineering activity and skill. The engineer, and more generally thedesigner, is concerned with how things ought to be how they ought to be in order
to attain goals,
2.

I shall disclaim responsibility for this particular choice of terms. The phrase "artificial intelligence"
which led me to it, was coined, I think, right on the Charles River, at MIT. Our own research groupat Rand and Carnegie Mellon University have preferred phrases like "complex information
processing" and "simulation of cognitive processes." But then we run into new terminologicaldifficulties, for the dictionary also says that "to simulate" means "to assume or have the mere
appearance or form of, without the reality; imitate; counterfeit; pretend." At any rate, "artificial
intelligence" seems to be here to stay, and it may prove easier to cleanse the phrase than to dispensewith it. In time it will become sufficiently idiomatic that it will no longer be the target of cheap
rhetoric.

Page 5and to function. Hence a science of the artificial will be closely akin to a scienceof engineering but very different, as we shall see in my fifth chapter, from whatgoes currently by the name of "engineering science."
With goals and "oughts" we also introduce into the picture the dichotomy
between normative and descriptive. Natural science has found a way to exclude
the normative and to concern itself solely with how things are. Can or should wemaintain this exclusion when we move from natural to artificial phenomena,from analysis to synthesis?3
We have now identified four indicia that distinguish the artificial from thenatural; hence we can set the boundaries for sciences of the artificial:
1. Artificial things are synthesized (though not always or usually with full
forethought) by human beings.
2. Artificial things may imitate appearances in natural things while lacking, in
one or many respects, the reality of the latter.
3. Artificial things can be characterized in terms of functions, goals, adaptation.
4. Artificial things are often discussed, particularly when they are being
designed, in terms of imperatives as well as descriptives.
The Environment As Mold
Let us look a little more closely at the functional or purposeful aspect of artificial
things. Fulfillment of purpose or adaptation to a goal involves a relation among
three terms: the purpose or goal, the character of the artifact, and theenvironment in which the artifact performs. When we think of a clock, forexample, in terms of purpose we may use the child's definition: "a clock is to telltime." When we focus our attention on the clock itself, we may describe it interms of arrangements of gears and the3.

This issue will also be discussed at length in my fifth chapter. In order not to keep readers insuspense, I may say that I hold to the pristine empiricist's position of the irreducibility of "ought" to"is," as in chapter 3 of my Administrative Behavior (New York: Macmillan, 1976). This position isentirely consistent with treating natural or artificial goal-seeking systems as phenomena, withoutcommitment to their goals. Ibid., appendix. See also the well-known paper by A. Rosenbluth, N.
Wiener, and J. Bigelow, ''Behavior, Purpose, and Teleology," Philosophy of Science, 10 (1943):18
24.

Page 6application of the forces of springs or gravity operating on a weight or pendulum.
But we may also consider clocks in relation to the environment in which they are
to be used. Sundials perform as clocks in sunny climates they are more useful in
Phoenix than in Boston and of no use at all during the Arctic winter. Devising aclock that would tell time on a rolling and pitching ship, with sufficient accuracy
to determine longitude, was one of the great adventures of eighteenth-century
science and technology. To perform in this difficult environment, the clock had
to be endowed with many delicate properties, some of them largely or totallyirrelevant to the performance of a landlubber's clock.
Natural science impinges on an artifact through two of the three terms of therelation that characterizes it: the structure of the artifact itself and theenvironment in which it performs. Whether a clock will in fact tell time depends
on its internal construction and where it is placed. Whether a knife will cut
depends on the material of its blade and the hardness of the substance to which itis applied.
The Artifact As "Interface"
We can view the matter quite symmetrically. An artifact can be thought of as ameeting point an "interface" in today's terms between an "inner" environment,
the substance and organization of the artifact itself, and an ''outer" environment,
the surroundings in which it operates. If the inner environment is appropriate tothe outer environment, or vice versa, the artifact will serve its intended purpose.
Thus, if the clock is immune to buffeting, it will serve as a ship's chronometer.
(And conversely, if it isn't, we may salvage it by mounting it on the mantel athome.)
Notice that this way of viewing artifacts applies equally well to many things thatare not man-made to all things in fact that can be regarded as adapted to somesituation; and in particular it applies to the living systems that have evolved
through the forces of organic evolution. A theory of the airplane draws on naturalscience for an explanation of its inner environment (the power plant, forexample), its outer environment (the character of the atmosphere at different
altitudes), and the relation between its inner and outer environments (themovement of an air foil

Page 7through a gas). But a theory of the bird can be divided up in exactly the same
way.4
Given an airplane, or given a bird, we can analyze them by the methods ofnatural science without any particular attention to purpose or adaptation, without
reference to the interface between what I have called the inner and outerenvironments. After all, their behavior is governed by natural law just as fully as
the behavior of anything else (or at least we all believe this about the airplane,
and most of us believe it about the bird).
Functional Explanation
On the other hand, if the division between inner and outer environment is notnecessary to the analysis of an airplane or a bird, it turns out at least to be highly
convenient. There are several reasons for this, which will become evident fromexamples.
Many animals in the Arctic have white fur. We usually explain this by sayingthat white is the best color for the Arctic environment, for white creatures escapedetection more easily than do others. This is not of course a natural science
explanation; it is an explanation by reference to purpose or function. It simply
says that these are the kinds of creatures that will "work;" that is, survive, in thiskind of environment. To turn the statement into an explanation, we must add to it
a notion of natural selection, or some equivalent mechanism.
An important fact about this kind of explanation is that it demands anunderstanding mainly of the outer environment. Looking at our snowy
surroundings, we can predict the predominant color of the creatures we are likely
to encounter; we need know little about the biology of the creatures themselves,
beyond the facts that they are often mutually hostile, use visual clues to guidetheir behavior, and are adaptive (through selection or some other mechanism).
4.

A generalization of the argument made here for the separability of "outer" from "inner"
environment shows that we should expect to find this separability, to a greater or lesser degree, in alllarge and complex systems, whether they are artificial or natural. In its generalized form it is anargument that all nature will be organized in "levels:" My essay "The Architecture of Complexity,''included in this volume as chapter 8, develops the more general argument in some detail.

Page 8

Analogous to the role played by natural selection in evolutionary biology is therole played by rationality in the sciences of human behavior. If we know of abusiness organization only that it is a profit-maximizing system, we can often
predict how its behavior will change if we change its environment how it willalter its prices if a sales tax is levied on its products. We can sometimes makethis prediction and economists do make it repeatedly without detailed
assumptions about the adaptive mechanism, the decision-making apparatus that
constitutes the inner environment of the business firm.
Thus the first advantage of dividing outer from inner environment in studying an
adaptive or artificial system is that we can often predict behavior fromknowledge of the system's goals and its outer environment, with only minimal
assumptions about the inner environment. An instant corollary is that we often
find quite different inner environments accomplishing identical or similar goals
in identical or similar outer environments airplanes and birds, dolphins and tuna
fish, weight-driven clocks and battery-driven clocks, electrical relays andtransistors.
There is often a corresponding advantage in the division from the standpoint of
the inner environment. In very many cases whether a particular system willachieve a particular goal or adaptation depends on only a few characteristics ofthe outer environment and not at all on the detail of that environment. Biologists
are familiar with this property of adaptive systems under the label ofhomeostasis. It is an important property of most good designs, whether biologicalor artifactual. In one way or an other the designer insulates the inner system fromthe environment, so that an invariant relation is maintained between inner system
and goal, independent of variations over a wide range in most parameters thatcharacterize the outer environment. The ship's chronometer reacts to the pitchingof the ship only in the negative sense of maintaining an invariant relation of thehands on its dial to the real time, independently of the ship's motions.
Quasi independence from the outer environment may be maintained by various
forms of passive insulation, by reactive negative feedback (the most frequently
discussed form of insulation), by predictive adaptation, or by various
combinations of these.

Page 9Functional Description and Synthesis
In the best of all possible worlds at least for a designer we might even hope tocombine the two sets of advantages we have described that derive from factoringan adaptive system into goals, outer environment, and inner environment. We
might hope to be able to characterize the main properties of the system and its
behavior without elaborating the detail of either the outer or inner environments.
We might look toward a science of the artificial that would depend on therelative simplicity of the interface as its primary source of abstraction andgenerality.
Consider the design of a physical device to serve as a counter. If we want thedevice to be able to count up to one thousand, say, it must be capable ofassuming any one of at least a thousand states, of maintaining itself in any given
state, and of shifting from any state to the "next" state. There are dozens ofdifferent inner environments that might be used (and have been used) for such a
device. A wheel notched at each twenty minutes of arc, and with a ratchet device
to turn and hold it, would do the trick. So would a string of ten electricalswitches properly connected to represent binary numbers. Today instead ofswitches we are likely to use transistors or other solid-state devices.5
Our counter would be activated by some kind of pulse, mechanical or electrical,
as appropriate, from the outer environment. But by building an appropriate
transducer between the two environments, the physical character of the interior
pulse could again be made independent of the physical character of the exterior
pulse the counter could be made to count anything.
Description of an artifice in terms of its organization and functioning its interface
between inner and outer environments is a major objective of invention anddesign activity. Engineers will find familiar the language of the following claim
quoted from a 1919 patent on an improved motor controller:
What I claim as new and desire to secure by Letters Patent is:
1 In a motor controller, in combination, reversing means, normally effective field-weakening means
and means associated with said reversing means for
5.

The theory of functional equivalence of computing machines has had considerable development inrecent years. See Marvin L. Minsky, Computation: Finite and Infinite Machines (Englewood Cliffs,
N.J.: Prentice-Hall, 1967), chapters 1 4.

Page 10rendering said field-weakening means ineffective during motor starting and thereafter effective todifferent degrees determinable by the setting of said reversing means . . .6

Apart from the fact that we know the invention relates to control of an electricmotor, there is almost no reference here to specific, concrete objects or
phenomena. There is reference rather to "reversing means" and "field-weakeningmeans," whose further purpose is made clear in a paragraph preceding the patentclaims:
The advantages of the special type of motor illustrated and the control thereof will be readily
understood by those skilled in the art. Among such advantages may be mentioned the provision of ahigh starting torque and the provision for quick reversals of the motor.7

Now let us suppose that the motor in question is incorporated in a planingmachine (see figure 2). The inventor describes its behavior thus:
Referring now to [figure 2], the controller is illustrated in outline connection with a planer (100)operated by a motor M, the controller being adapted to govern the motor M and to be automaticallyoperated by the reciprocating bed (101) of the planer. The master shaft of the controller is provided
with a lever (102) connected by a link (103) to a lever (104) mounted upon the planer frame andprojecting into the path of lugs (105) and (106) on the planer bed. As will be understood, thearrangement is such that reverse movements of the planer bed will, through the connections
described, throw the master shaft of the controller back and forth between its extreme positions andin consequence effect selective operation of the reversing switches (1) and (2) and automatic
operation of the other switches in the manner above set forth.8

In this manner the properties with which the inner environment has beenendowed are placed at the service of the goals in the context of the outerenvironment. The motor will reverse periodically under the control of theposition of the planer bed. The "shape" of its behavior the time path, say, of avariable associated with the motor will be a function of the "shape" of theexternal environment the distance, in this case, between the lugs on the planerbed.
The device we have just described illustrates in microcosm the nature of artifacts.
Central to their description are the goals that link the inner
6.

U.S. Patent 1,307,836, granted to Arthur Simon, June 24, 1919.

7.

Ibid.

8.

Ibid.

Page 11

Figure 2
Illustrations from a patent for a motor controller

to the outer system. The inner system is an organization of natural phenomena
capable of attaining the goals in some range of environments, but ordinarily there
will be many functionally equivalent natural systems capable of doing this.
The outer environment determines the conditions for goal attainment. If the inner
system is properly designed, it will be adapted to the outer environment, so thatits behavior will be determined in large part by thePage 12

behavior of the latter, exactly as in the case of "economic man." To predict how
it will behave, we need only ask, "How would a rationally designed systembehave under these circumstances?" The behavior takes on the shape of the task
environment.9
Limits of Adaptation
But matters must be just a little more complicated than this account suggests. "Ifwishes were horses, all beggars would ride." And if we could always specify aprotean inner system that would take on exactly the shape of the task
environment, designing would be synonymous with wishing. "Means for
scratching diamonds" defines a design objective, an objective that might beattained with the use of many different substances. But the design has not beenachieved until we have discovered at least one realizable inner system obeying
the ordinary natural laws one material, in this case, hard enough to scratch
diamonds.
Often we shall have to be satisfied with meeting the design objectives only
approximately. Then the properties of the inner system will "show through." Thatis, the behavior of the system will only partly respond to the task environment;
partly, it will respond to the limiting properties of the inner system.
Thus the motor controls described earlier are aimed at providing for "quick"reversal of the motor. But the motor must obey electromagnetic and mechanicallaws, and we could easily confront the system with a task where the environment
called for quicker reversal than the motor was capable of. In a benign
environment we would learn from the motor only what it had been called upon todo; in a taxing environment we would learn something about its internal structure
specifically about those aspects of the internal structure that were chiefly
instrumental in limiting performance.10
9.

On the crucial role of adaptation or rationality and their limits for economics and organization
theory, see the introduction to part IV, "Rationality and Administrative Decision Making," of myModels of Man (New York: Wiley, 1957); pp. 38 41, 80 81, and 240 244 of Administrative
Behavior; and chapter 2 of this book.
10.

Compare the corresponding proposition on the design of administrative organizations: "Rationality,
then, does not determine behavior. Within the area of rationality behavior is perfectly flexible andadaptable to abilities, goals, and(footnote continued on next page)Page 13

A bridge, under its usual conditions of service, behaves simply as a relatively
smooth level surface on which vehicles can move. Only when it has been
overloaded do we learn the physical properties of the materials from which it isbuilt.
Understanding by Simulating
Artificiality connotes perceptual similarity but essential difference, resemblancefrom without rather than within. In the terms of the previous section we may saythat the artificial object imitates the real by turning the same face to the outersystem, by adapting, relative to the same goals, to comparable ranges of externaltasks. Imitation is possible because distinct physical systems can be organized to
exhibit nearly identical behavior. The damped spring and the damped circuit
obey the same second-order linear differential equation; hence we may use either
one to imitate the other.
Techniques of Simulation
Because of its abstract character and its symbol manipulating generality, thedigital computer has greatly extended the range of systems whose behavior canbe imitated. Generally we now call the imitation "simulation," and we try tounderstand the imitated system by testing the simulation in a variety ofsimulated, or imitated, environments.
Simulation, as a technique for achieving understanding and predicting the
behavior of systems, predates of course the digital computer. The model basin
and the wind tunnel are valued means for studying the behavior of large systems
by modeling them in the small, and it is quite certain that Ohm's law wassuggested to its discoverer by its analogy with simple hydraulic phenomena.
(footnote continued from previous page)knowledge. Instead, behavior is determined by the irrational and non-rational elements that boundthe area of rationality . . . administrative theory must be concerned with the limits of rationality, andthe manner in which organization affects these limits for the person making a decision."
Administrative Behavior, p. 241. For a discussion of the same issue as it arises in psychology, see
my "Cognitive Architectures and Rational Analysis: Comment," in Kurt Van Lehn (ed.),Architectures for Intelligence (Hillsdale, NJ: Erlbaum, 1991).

Page 14

Simulation may even take the form of a thought experiment, never actually
implemented dynamically. One of my vivid memories of the Great Depression isof a large multi colored chart in my father's study that represented a hydraulicmodel of an economic system (with different fluids for money and goods). The
chart was devised by a technocratically inclined engineer named Dahlberg. The
model never got beyond the pen-and-paint stage at that time, but it could be used
to trace through the imputed consequences of particular economic measures or
events provided the theory was right!11
As my formal education in economics progressed, I acquired a disdain for thatnaive simulation, only to discover after World War II that a distinguishedeconomist, Professor A. W. Phillips had actually built the Moniac, a hydraulicmodel that simulated a Keynesian economy.12 Of course Professor Phillips's
simulation incorporated a more nearly correct theory than the earlier one and wasactually constructed and operated two points in its favor. However, the Moniac,
while useful as a teaching tool, told us nothing that could not be extracted readily
from simple mathematical versions of Keynesian theory and was soon priced outof the market by the growing number of computer simulations of the economy.
Simulation As a Source of New Knowledge
This brings me to the crucial question about simulation: How can a simulation
ever tell us anything that we do not already know? The usual implication of thequestion is that it can't. As a matter of fact, there is an interesting parallelism,
which I shall exploit presently, between two assertions about computers andsimulation that one hears frequently:
1. A simulation is no better than the assumptions built into it.
2. A computer can do only what it is programmed to do.
I shall not deny either assertion, for both seem to me to be true. But despite bothassertions simulation can tell us things we do not already know.
11.

For some published versions of this model, see A. O. Dahlberg, National Income Visualized(N.Y.: Columbia University Press, 1956).
12.

A. W. Phillips, "Mechanical Models in Economic Dynamics," Economica, New Series, 17 (1950):283
305.

Page 15

There are two related ways in which simulation can provide new knowledge oneof them obvious, the other perhaps a bit subtle. The obvious point is that, evenwhen we have correct premises, it may be very difficult to discover what they
imply. All correct reasoning is a grand system of tautologies, but only God canmake direct use of that fact. The rest of us must painstakingly and fallibly teaseout the consequences of our assumptions.
Thus we might expect simulation to be a powerful technique for deriving, fromour knowledge of the mechanisms governing the behavior of gases, a theory of
the weather and a means of weather prediction. Indeed, as many people areaware, attempts have been under way for some years to apply this technique.
Greatly oversimplified, the idea is that we already know the correct basic
assumptions, the local atmospheric equations, but we need the computer to workout the implications of the interactions of vast numbers of variables starting fromcomplicated initial conditions. This is simply an extrapolation to the scale ofmodern computers of the idea we use when we solve two simultaneous equations
by algebra.
This approach to simulation has numerous applications to engineering design.
For it is typical of many kinds of design problems that the inner system consistsof components whose fundamental laws of behavior mechanical, electrical, or
chemical are well known. The difficulty of the design problem often resides in
predicting how an assemblage of such components will behave.
Simulation of Poorly Understood Systems
The more interesting and subtle question is whether simulation can be of any
help to us when we do not know very much initially about the natural laws thatgovern the behavior of the inner system. Let me show why this question mustalso be answered in the affirmative.
First, I shall make a preliminary comment that simplifies matters: we are seldominterested in explaining or predicting phenomena in all their particularity; we areusually interested only in a few properties abstracted from the complex reality.
Thus, a NASA-launched satellite is surely an artificial object, but we usually do
not think of it as "simulating" the moon or a planet. It simply obeys the samelaws of physics, which relatePage 16

only to its inertial and gravitational mass, abstracted from most of its otherproperties. It is a moon. Similarly electric energy that entered my house from theearly atomic generating station at Shipping port did not "simulate" energy
generated by means of a coal plant or a windmill. Maxwell's equations hold for
both.
The more we are willing to abstract from the detail of a set of phenomena, theeasier it becomes to simulate the phenomena. Moreover we do not have to know,
or guess at, all the internal structure of the system but only that part of it that is
crucial to the abstraction.
It is fortunate that this is so, for if it were not, the top down strategy that built thenatural sciences over the past three centuries would have been infeasible. We
knew a great deal about the gross physical and chemical behavior of matter
before we had a knowledge of molecules, a great deal about molecular chemistry
before we had an atomic theory, and a great deal about atoms before we had any
theory of elementary particles if indeed we have such a theory today.
This skyhook-skyscraper construction of science from the roof down to the yet
unconstructed foundations was possible because the behavior of the system at
each level depended on only a very approximate, simplified, abstracted
characterization of the system at the level next beneath.13 This is lucky, else thesafety of bridges and airplanes might depend on the correctness of the "Eightfold
Way" of looking at elementary particles.
Artificial systems and adaptive systems have properties that make them
particularly susceptible to simulation via simplified models. The characterizationof such systems in the previous section of this chapter
13.

This point is developed more fully in "The Architecture of Complexity," chapter 8 in this volume.
More than fifty years ago, Bertrand Russell made the same point about the architecture ofmathematics. See the "Preface" to Principia Mathematica: ". . . the chief reason in favour of any
theory on the principles of mathematics must always be inductive, i.e., it must lie in the fact that thetheory in question enables us to deduce ordinary mathematics. In mathematics, the greatest degree ofself-evidence is usually not to be found quite at the beginning, but at some later point; hence theearly deductions, until they reach this point, give reasons rather for believing the premises because
true consequences follow from them, than for believing the consequences because they follow fromthe premises." Contemporary preferences for deductive formalisms frequently blind us to thisimportant fact, which is no less true today than it was in 1910.

Page 17

explains why. Resemblance in behavior of systems without identity of the inner
systems is particularly feasible if the aspects in which we are interested arise outof the organization of the parts, independently of all but a few properties of theindividual components. Thus for many purposes we may be interested in only
such characteristics of a material as its tensile and compressive strength. We may
be profoundly unconcerned about its chemical properties, or even whether it iswood or iron.
The motor control patent cited earlier illustrates this abstraction to organizational
properties. The invention consisted of a ''combination" of "reversing means," of"field weakening means," that is to say, of components specified in terms of theirfunctioning in the organized whole. How many ways are there of reversing a
motor, or of weakening its field strength? We can simulate the system describedin the patent claims in many ways without reproducing even approximately theactual physical device that is depicted. With a small additional step of
abstraction, the patent claims could be restated to encompass mechanical as wellas electrical devices. I suppose that any undergraduate engineer at Berkeley,
Carnegie Mellon University, or MIT could design a mechanical systemembodying reversibility and variable starting torque so as to simulate the systemof the patent.
The Computer As Artifact
No artifact devised by man is so convenient for this kind of functionaldescription as a digital computer. It is truly protean, for almost the only ones of
its properties that are detectable in its behavior (when it is operating properly!)
are the organizational properties. The speed with which it performs it basic
operations may allow us to infer a little about its physical components and theirnatural laws; speed data, for example, would allow us to rule out certain kinds of"slow" components. For the rest, almost no interesting statement that one canmake about an operating computer bears any particular relation to the specificnature of the hardware. A computer is an organization of elementary functional
components in which, to a high approximation, only the function

Page 18

performed by those components is relevant to the behavior of the whole system.14
Computers As Abstract Objects
This highly abstractive quality of computers makes it easy to introduce
mathematics into the study of their theory and has led some to the erroneous
conclusion that, as a computer science emerges, it will necessarily be amathematical rather than an empirical science. Let me take up these two points inturn: the relevance of mathematics to computers and the possibility of studying
computers empirically.
Some important theorizing, initiated by John von Neumann, has been done on
the topic of computer reliability. The question is how to build a reliable systemfrom unreliable parts. Notice that this is not posed as a question of physics orphysical engineering. The components engineer is assumed to have done his best,
but the parts are still unreliable! We can cope with the unreliability only by ourmanner of organizing them.
To turn this into a meaningful problem, we have to say a little more about thenature of the unreliable parts. Here we are aided by the knowledge that any
computer can be assembled out of a small array of simple, basic elements. For
instance, we may take as our primitives the so-called Pitts-McCulloch neurons.
As their name implies, these components were devised in analogy to the
supposed anatomical and functional characteristics of neurons in the brain, butthey are highly abstracted. They are formally isomorphic with the simplest kindsof switching circuits "and" "or," and "not'' circuits. We postulate, now, that weare to build a system from such elements and that each elementary part has a
specified probability of functioning correctly. The problem is to arrange theelements and their interconnections in such a way that the complete system will
perform reliably.
The important point for our present discussion is that the parts could as well be
neurons as relays, as well relays as transistors. The natural laws governing relays
are very well known, while the natural laws governing
14.

On the subject of this and the following paragraphs, see M. L. Minsky, op. cit.; then John von
Neumann, "Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable
Components," in C. E. Shannon and J. McCarthy (eds.), Automata Studies (Princeton: Princeton
University Press, 1956).

Page 19

neurons are known most imperfectly. But that does not matter, for all that isrelevant for the theory is that the components have the specified level of
unreliability and be interconnected in the specified way.
This example shows that the possibility of building a mathematical theory of asystem or of simulating that system does not depend on having an adequate
micro theory of the natural laws that govern the system components. Such a
micro theory might indeed be simply irrelevant.
Computers As Empirical Objects
We turn next to the feasibility of an empirical science of computers as distinct
from the solid-state physics or physiology of their componentry.15 As a matter of
empirical fact almost all of the computers that have been designed have certain
common organizational features. They almost all can be decomposed into an
active processor (Babbage's "Mill") and a memory (Babbage's "Store") in
combination with input and output devices. (Some of the larger systems,
somewhat in the manner of colonial algae, are assemblages of smaller systems
having some or all of these components. But perhaps I may oversimplify for the
moment.) They are all capable of storing symbols (program) that can beinterpreted by a program-control component and executed. Almost all haveexceedingly limited capacity for simultaneous, parallel activity they are basically
one-thing-at-a-time systems. Symbols generally have to be moved from thelarger memory components into the central processor before they can be acted
upon. The systems are capable of only simple basic actions: recoding symbols,
storing symbols, copying symbols, moving symbols, erasing symbols, andcomparing symbols.
Since there are now many such devices in the world, and since the properties that
describe them also appear to be shared by the human central nervous system,
nothing prevents us from developing a natural history of them. We can study
them as we would rabbits or chipmunks and discover how they behave under
different patterns of environmental stimulation. Insofar as their behavior reflects
largely the broad functional
15.

A. Newell and H. A. Simon, "Computer Science as Empirical Inquiry," Communications of theACM, 19(March 1976):113 126. See also H. A. Simon, "Artificial Intelligence: An Empirical
Science," Artificial Intelligence, 77(1995):95 127.

Page 20

characteristics we have described, and is independent of details of theirhardware, we can build a general but empirical theory of them.
The research that was done to design computer time-sharing systems is a good
example of the study of computer behavior as an empirical phenomenon. Only
fragments of theory were available to guide the design of a time-sharing systemor to predict how a system of a specified design would actually behave in anenvironment of users who placed their several demands upon it. Most actual
designs turned out initially to exhibit serious deficiencies, and most predictionsof performance were startlingly inaccurate.
Under these circumstances the main route open to the development andimprovement of time-sharing systems was to build them and see how they
behaved. And this is what was done. They were built, modified, and improved insuccessive stages. Perhaps theory could have anticipated these experiments andmade them unnecessary. In fact it didn't, and I don't know anyone intimately
acquainted with these exceedingly complex systems who has very specific ideas
as to how it might have done so. To understand them, the systems had to beconstructed, and their behavior observed.16
In a similar vein computer programs designed to play games or to discover
proofs for mathematical theorems spend their lives in exceedingly large andcomplex task environments. Even when the programs themselves are only
moderately large and intricate (compared, say, with the monitor and operating
systems of large computers), too little is known about their task environments topermit accurate prediction of how well they will perform, how selectively they
will be able to search for problem solutions.
Here again theoretical analysis must be accompanied by large amounts ofexperimental work. A growing literature reporting these experiments is
beginning to give us precise knowledge about the degree of heuristic power ofparticular heuristic devices in reducing the size of the problem spaces that must
be searched. In theorem proving, for example, there has
16.

The empirical, exploratory flavor of computer research is nicely captured by the account ofMaurice V. Wilkes in his 1967 Turing Lecture, "Computers Then and Now," Journal of the
Association for Computing Machinery, 15(January 1968):1 7.

Page 21been a whole series of advances in heuristic power based on and guided byempirical exploration: the use of the Herbrand theorem, the resolution principle,
the set-of-support principle, and so on.17
Computers and Thought
As we succeed in broadening and deepening our knowledge theoretical andempirical about computers, we discover that in large part their behavior isgoverned by simple general laws, that what appeared as complexity in thecomputer program was to a considerable extent complexity of the environment towhich the program was seeking to adapt its behavior.
This relation of program to environment opened up an exceedingly importantrole for computer simulation as a tool for achieving a deeper understanding of
human behavior. For if it is the organization of components, and not their
physical properties, that largely determines behavior, and if computers areorganized somewhat in the image of man, then the computer becomes an obvious
device for exploring the consequences of alternative organizational assumptions
for human behavior. Psychology could move forward without awaiting thesolutions by neurology of the problems of component design however interestingand significant these components turn out to be.
Symbol Systems: Rational Artifacts
The computer is a member of an important family of artifacts called symbol
systems, or more explicitly, physical symbol systems.18 Another importantmember of the family (some of us think, anthropomorphically, it is the mostimportant) is the human mind and brain. It is with this family
17.

Note, for example, the empirical data in Lawrence Wos, George A. Robinson, Daniel F. Carson,and Leon Shalla, "The Concept of Demodulation in Theorem Proving," Journal of the Association
for Computing Machinery, 14(October 1967):698 709, and in several of the earlier papers referenced
there. See also the collection of programs in Edward Feigenbaum and Julian Feldman (eds.),
Computers and Thought (New York: McGraw-Hill, 1963). It is common practice in the field to title
papers about heuristic programs, "Experiments with an XYZ Program."
18.

In the literature the phrase information-processing system is used more frequently than symbol
system. I will use the two terms as synonyms.

Page 22of artifacts, and particularly the human version of it, that we will be primarily
concerned in this book. Symbol systems are almost the quintessential artifacts,
for adaptivity to an environment is their whole raison d'être. They are goalseeking, information-processing systems, usually enlisted in the service of thelarger systems in which they are incorporated.
Basic Capabilities of Symbol Systems
A physical symbol system holds a set of entities, called symbols. These arephysical patterns (e.g., chalk marks on a blackboard) that can occur ascomponents of symbol structures (sometimes called "expressions"). As I havealready pointed out in the case of computers, a symbol system also possesses anumber of simple processes that operate upon symbol structures processes thatcreate, modify, copy, and destroy symbols. A physical symbol system is amachine that, as it moves through time, produces an evolving collection ofsymbol structures.19 Symbol structures can, and commonly do, serve as internal
representations (e.g., "mental images") of the environments to which the symbol
system is seeking to adapt. They allow it to model that environment with greateror less veridicality and in greater or less detail, and consequently to reason about
it. Of course, for this capability to be of any use to the symbol system, it musthave windows on the world and hands, too. It must have means for acquiring
information from the external environment that can be encoded into internal
symbols, as well as means for producing symbols that initiate action upon theenvironment. Thus it must use symbols to designate objects and relations andactions in the world external to the system.
Symbols may also designate processes that the symbol system can interpret andexecute. Hence the programs that govern the behavior of a symbol system can be
stored, along with other symbol structures, in the system's own memory, andexecuted when activated.
Symbol systems are called "physical" to remind the reader that they exist as realworld devices, fabricated of glass and metal (computers) or flesh and blood(brains). In the past we have been more accustomed to thinking of the symbol
systems of mathematics and logic as abstract and disembodied, leaving out ofaccount the paper and pencil and human minds that were required actually tobring them to life. Computers have
19.

Newell and Simon, "Computer Science as Empirical Inquiry," p. 116.

Page 23transported symbol systems from the platonic heaven of ideas to the empiricalworld of actual processes carried out by machines or brains, or by the two ofthem working together.
Intelligence As Computation
The three chapters that follow rest squarely on the hypothesis that intelligence isthe work of symbol systems. Stated a little more formally, the hypothesis is that aphysical symbol system of the sort I have just described has the necessary andsufficient means for general intelligent action.
The hypothesis is clearly an empirical one, to be judged true or false on the basisof evidence. One task of chapters 3 and 4 will be to review some of the evidence,
which is of two basic kinds. On the one hand, by constructing computer
programs that are demonstrably capable of intelligent action, we provide
evidence on the sufficiency side of the hypothesis. On the other hand, bycollecting experimental data on human thinking that tend to show that the humanbrain operates as a symbol system, we add plausibility to the claims fornecessity, for such data imply that all known intelligent systems (brains andcomputers) are symbol systems.
Economics: Abstract Rationality
As prelude to our consideration of human intelligence as the work of a physicalsymbol system, chapter 2 introduces a heroic abstraction and idealization theidealization of human rationality which is enshrined in modern economic
theories, particularly those called neoclassical. These theories are an idealization
because they direct their attention primarily to the external environment of
human thought, to decisions that are optimal for realizing the adaptive system's
goals (maximization of utility or profit). They seek to define the decisions thatwould be substantively rational in the circumstances defined by the outerenvironment.
Economic theory's treatment of the limits of rationality imposed by the innerenvironment by the characteristics of the physical symbol system tends to be
pragmatic, and sometimes even opportunistic. In the more formal treatments ofgeneral equilibrium and in the so-called "rational expectations" approach toadaptation, the possibilities that an information-processing system may have avery limited capability forPage 24

adaptation are almost ignored. On the other hand, in discussions of the rationale
for market mechanisms and in many theories of decision making under
uncertainty, the procedural aspects of rationality receive more serious treatment.
In chapter 2 we will see examples both of neglect for and concern with the limitsof rationality. From the idealizations of economics (and some criticisms of these
idealizations) we will move, in chapters 3 and 4, to a more systematic study ofthe inner environment of thought of thought processes as they actually occur
within the constraints imposed by the parameters of a physical symbol system
like the brain.

Page 25

2
Economic Rationality: Adaptive Artifice
Because scarcity is a central fact of life land, money, fuel, time, attention, andmany other things are scarce it is a task of rationality to allocate scarce things.
Performing that task is the focal concern of economics.
Economics exhibits in purest form the artificial component in human behavior, inindividual actors, business firms, markets, and the entire economy. The outerenvironment is defined by the behavior of other individuals, firms, markets, or
economies. The inner environment is defined by an individual's, firm's, market's,
or economy's goals and capabilities for rational, adaptive behavior. Economics
illustrates well how outer and inner environment interact and, in particular, how
an intelligent system's adjustment to its outer environment (its substantive
rationality) is limited by its ability, through knowledge and computation, todiscover appropriate adaptive behavior (its procedural rationality).
The Economic Actor
In the textbook theory of the business firm, an "entrepreneur" aims at
maximizing profit, and in such simple circumstances that the computational
ability to find the maximum is not in question. A cost curve relates dollar
expenditures to amount of product manufactured, and a revenue curve relates
income to amount of product sold. The goal (maximizing the difference between
income and expenditure) fully defines the firm's inner environment. The cost andrevenue curves define the outer environment.1 Elementary calculus shows how tofind the profit-maximizing
1.

I am drawing the line between outer and inner environment not at the firm's boundary but at theskin of the entrepreneur, so that the factory is part of the external technology; the brain, perhaps
assisted by computers, is the internal.

Page 26

quantity by taking a derivative (rate at which profit changes with change inquantity) and setting it equal to zero.
Here are all the elements of an artificial system adapting to an outerenvironment, subject only to the goal defined by the inner environment. In
contrast to a situation where the adaptation process is itself problematic, we canpredict the system's behavior without knowing how it actually computes the
optimal output. We need consider only substantive rationality.2
We can interpret this bare-bones theory of the firm either positively (asdescribing how business firms behave) or normatively (as advising them how to
maximize profits). It is widely taught in both senses in business schools anduniversities, just as if it described what goes on, or could go on, in the real world.
Alas, the picture is far too simple to fit reality.
Procedural Rationality
The question of maximizing the difference between revenue and cost becomesinteresting when, in more realistic circumstances, we ask how the firm actually
goes about discovering that maximizing quantity. Cost accounting may estimate
the approximate cost of producing any particular output, but how much can besold at a specific price and how this amount varies with price (the elasticity ofdemand) usually can be guessed only roughly. When there is uncertainty (asthere always is), prospects of profit must be balanced against risk, thereby
changing profit maximization to the much more shadowy goal of maximizing a
profit-vs.-risk "utility function" that is assumed to lurk somewhere in therecesses of the entrepreneur's mind.
But in real life the business firm must also choose product quality and theassortment of products it will manufacture. It often has to invent and design
some of these products. It must schedule the factory to produce a profitablecombination of them and devise marketing procedures and structures to sellthem. So we proceed step by step from the simple caricature of the firm depictedin the textbooks to the complexities of real firms in the real world of business. At
each step toward realism, the problem
2.

H. A. Simon, "Rationality as Process and as Product of Thought," American Economic Review,
68(1978):1 16.

Page 27

gradually changes from choosing the right course of action (substantive
rationality) to finding a way of calculating, very approximately, where a goodcourse of action lies (procedural rationality). With this shift, the theory of thefirm becomes a theory of estimation under uncertainty and a theory ofcomputation decidedly non-trivial theories as the obscurities and complexities of
information and computation increase.
Operations Research and Management Science
Today several branches of applied science assist the firm to achieve procedural
rationality.3 One of them is operations research (OR); another is artificial
intelligence (AI). OR provides algorithms for handling difficult multivariate
decision problems, sometimes involving uncertainty. Linear programming,
integer programming, queuing theory, and linear decision rules are examples ofwidely used OR procedures.
To permit computers to find optimal solutions with reasonable expenditures ofeffort when there are hundreds or thousands of variables, the powerfulalgorithms associated with OR impose a strong mathematical structure on thedecision problem. Their power is bought at the cost of shaping and squeezing thereal-world problem to fit their computational requirements: for example,
replacing the real-world criterion function and constraints with linear
approximations so that linear programming can be used. Of course the decisionthat is optimal for the simplified approximation will rarely be optimal in the realworld, but experience shows that it will often be satisfactory.
The alternative methods provided by AI, most often in the form of heuristic
search (selective search using rules of thumb), find decisions that are "goodenough," that satisfice. The AI models, like OR models, also only approximate
the real world, but usually with much more accuracy and detail than the OR
models can admit. They can do this because heuristic search can be carried out ina more complex and less well-structured problem space than is required by OR
maximizing tools. The price paid3.

For a brief survey of these developments, see H. A. Simon, "On How to Decide What to Do," The
Bell Journal of Economics, 9(1978):494 507. For an estimate of their impact on management, see H.
A. Simon, The New Science of Management Decision, rev. ed. (Englewood Cliffs, NJ: Prentice-Hall,
1977), chapters 2 and 4.

Page 28

for working with the more realistic but less regular models is that AI methods
generally find only satisfactory solutions, not optima. We must trade offsatisficing in a nearly-realistic model (AI) against optimizing in a greatly
simplified model (OR). Sometimes one will be preferred, sometimes the other.
AI methods can handle combinatorial problems (e.g., factory schedulingproblems) that are beyond the capacities of OR methods, even with the largest
computers. Heuristic methods provide an especially powerful problem-solving
and decision-making tool for humans who are unassisted by any computer otherthan their own minds, hence must make radical simplifications to find evenapproximate solutions. AI methods also are not limited, as most OR methods are,to situations that can be expressed quantitatively. They extend to all situations
that can be represented symbolically, that is, verbally, mathematically or
diagrammatically.
OR and AI have been applied mainly to business decisions at the middle levelsof management. A vast range of top management decisions (e.g., strategic
decisions about investment, R&D, specialization and diversification, recruitment,
development, and retention of managerial talent) are still mostly handledtraditionally, that is, by experienced executives' exercise of judgment.
As we shall see in chapters 3 and 4, so-called ''judgment" turns out to be mainly a
non-numerical heuristic search that draws upon information stored in large
expert memories. Today we have learned how to employ AI techniques in theform of so-called expert systems in a growing range of domains previously
reserved for human expertise and judgment for example, medical diagnosis andcredit evaluation. Moreover, while classical OR tools could only choose among
predefined alternatives, AI expert systems are now being extended to thegeneration of alternatives, that is, to problems of design. More will be said about
these developments in chapters 5 and 6.
Satisficing and Aspiration Levels
What a person cannot do he or she will not do, no matter how strong the urge to
do it. In the face of real-world complexity, the business firm turns to proceduresthat find good enough answers to questions whose best answers are unknowable.
Because real-world optimization, with or with-

Page 29

out computers, is impossible, the real economic actor is in fact a satisficer, aperson who accepts "good enough" alternatives, not because less is preferred tomore but because there is no choice.
Many economists, Milton Friedman being perhaps the most vocal, have argued
that the gap between satisfactory and best is of no great importance, hence theunrealism of the assumption that the actors optimize does not matter; others,
including myself, believe that it does matter, and matters a great deal. 4 But
reviewing this old argument would take me away from my main theme, which is
to show how the behavior of an artificial system may be strongly influenced by
the limits of its adaptive capacities its knowledge and computational powers.
One requirement of optimization not shared by satisficing is that all alternatives
must be measurable in terms of a common utility function. A large body ofevidence shows that human choices are not consistent and transitive, as theywould be if a utility function existed.5 But even in a satisficing theory we needsome criteria of satisfaction. What realistic measures of human profit, pleasure,happiness and satisfaction can serve in place of the discredited utility function?
Research findings on the psychology of choice, indicate some properties athermometer of satisfaction should have. First, unlike the utility function, it is not
limited to positive values, but has a zero point (of minimal contentment). Abovezero, various degrees of satisfaction are experienced, and below zero, various
degrees of dissatisfaction. Second, if periodic readings are taken of people inrelatively stable life circumstances, we only occasionally find temperatures very
far from zero in either direction, and the divergent measurements tend to regressover time back toward the zero mark. Most people consistently register either
slightly below zero (mild discontent) or a little above (moderate satisfaction).
4.

I have argued the case in numerous papers. Two recent examples are "Rationality in Psychology
and Economics," The Journal of Business, 59(1986):S209 S224 (No. 4, Pt. 2); and "The State of
Economic Science," in W. Sichel (ed.), The State of Economic Science (Kalamazoo, MI: W. E.
Upjohn Institute for Employment Research, 1989).
5.

See, for example, D. Kahneman and A. Tversky, "On the Psychology of Prediction," Psychological
Review, 80(1973):237 251, and H. Kunreuther et al., Disaster Insurance Protection (New York: Wiley,1978).

Page 30

To deal with these phenomena, psychology employs the concept of aspirationlevel. Aspirations have many dimensions: one can have aspirations for pleasantwork, love, good food, travel, and many other things. For each dimension,
expectations of the attainable define an aspiration level that is compared with thecurrent level of achievement. If achievements exceed aspirations, satisfaction isrecorded as positive; if aspirations exceed achievements, there is dissatisfaction.
There is no simple mechanism for comparison between dimensions. In general alarge gain along one dimension is required to compensate for a small loss along
another hence the system's net satisfactions are history-dependent and it isdifficult for people to balance compensatory offsets.
Aspiration levels provide a computational mechanism for satisficing. An
alternative satisfices if it meets aspirations along all dimensions. If no suchalternative is found, search is undertaken for new alternatives. Meanwhile,
aspirations along one or more dimensions drift down gradually until asatisfactory new alternative is found or some existing alternative satisfices. A
theory of choice employing these mechanisms acknowledges the limits onhuman computation and fits our empirical observations of human decision
making far better than the utility maximization theory.6
Markets and Organizations
Economics has been concerned less with individual consumers or business firms
than with larger artificial systems: the economy and its major components,
markets. Markets aim to coordinate the decisions and behavior of multitudes of
economic actors to guarantee that the quantity of brussels sprouts shipped tomarket bears some reasonable relation to the quantity that consumers will buy
and eat, and that the price at which brussels sprouts can be sold bears areasonable relation to the cost of producing them. Any society that is not a
subsistence economy, but has
6.

H. A. Simon, "A Behavioral Model of Rational Choice," Quarterly Journal of Economics,
6(1955):99 118; I. N. Gallhofer and W. E. Saris, Foreign Policy Decision-Making: A Qualitative andQuantitative Analysis of Political Argumentation (New York: Praeger, in press).

Page 31

substantial specialization and division of labor, needs mechanisms to perform
this coordinative function.
Markets are only one, however, among the spectrum of mechanisms of
coordination on which any society relies. For some purposes, central planning
based on statistics provides the basis for coordinating behavior patterns.
Highway planning, for example, relies on estimates of road usage that reflectstatistically stable patterns of driving behavior. For other purposes, bargaining
and negotiation may be used to coordinate individual behaviors, for instance, tosecure wage agreements between employers and unions or to form legislative
majorities. For still other coordinative functions, societies employ hierarchic
organizations business, governmental and educational with lines of formal
authority running from top to bottom and networks of communications lacing
through the structure. Finally, for making certain important decisions and forselecting persons to occupy positions of public authority, societies employ awide variety of balloting procedures.
Although all of these coordinating techniques can be found somewhere in almost
any society, their mix and applications vary tremendously from one nation or
culture to another.7 We ordinarily describe capitalist societies as depending
mostly on markets for coordination and socialist societies as depending mostly
on hierarchic organizations and planning, but this is a gross oversimplification,for it ignores the uses of voting in democratic societies of either kind, and it
ignores the great importance of large organizations in modern "market" societies.
The economic units in capitalist societies are mostly business firms, which arethemselves hierarchic organizations, some of enormous size, that make almost
negligible use of markets in their internal functioning. Roughly eighty percent of
the human economic activity in the American economy, usually regarded as
almost the epitome of a "market" economy, takes place in the internal
environments of business and other organizations and not in the external,
between-organization environments of markets.8 To avoid misunderstanding, it
would be appropriate to call such
7.

R. A. Dahl and C. E. Lindblom, Politics, Economics, and Welfare (New York: Harper andBrothers, 1953).
8.

H. A. Simon, "Organizations and Markets," Journal of Economic Perspectives, 5(1991):25 44.

Page 32

a society an organization-&-market economy; for in order to give an account of it
we have to pay as much attention to organizations as to markets.
The Invisible Hand
In examining the processes of social coordination, economics has given top
billing sometimes almost exclusive billing to the market mechanism. It is indeed
a remarkable mechanism which under many circumstances can bring it about thatthe producing, consuming, buying and selling behaviors of enormous numbers ofpeople, each responding only to personal selfish interests, allocate resources so
as to clear markets do in fact nearly balance the production with the consumptionof brussels sprouts and all the other commodities the economy produces anduses.
Only relatively weak conditions need be satisfied to bring about such anequilibrium. Achieving it mainly requires that prices drop in the face of anexcess supply, and that quantities produced decline when prices are lowered orwhen inventories mount. Any number of dynamic systems can be formulated thathave these properties, and these systems will seek equilibrium and oscillate
stably around it over a wide range of conditions.
There have been many recent laboratory experiments on market behavior,sometimes with human subjects, sometimes with computer programs as
simulated subjects.9 Experimental markets in which the simulated traders are"stupid" sellers, knowing only a minimum price below which they should notsell, and "stupid" buyers, knowing only a maximum price above which theyshould not buy move toward equilibrium almost as rapidly as markets whose
agents are rational in the classical sense.10
Markets and Optimality
These findings undermine the much stronger claims that are made for the pricemechanism by contemporary neoclassical economics. Claims that it does morethan merely clear markets require the strong assumptions of perfect competitionand of maximization of
9.

V. L. Smith, Papers in Experimental Economics (New York: Cambridge University Press, 1991.)

10.

D. J. Gode and S. Sunder, "Allocative Efficiency of Markets with Zero Intelligence Traders," Journalof Political Economy, 101(1993):119 127.

Page 33

profit or utility by the economic actors. With these assumptions, but not withoutthem, the market equilibrium can be shown to be optimal in the sense that it
could not be altered so as to make everyone simultaneously better off. These are
the familiar propositions of Pareto optimality of competitive equilibrium thathave been formalized so elegantly by Arrow, Debreu, Hurwicz, and others. 11
The optimality theorems stretch credibility, so far as real-world markets areconcerned, because they require substantive rationality of the kinds we foundimplausible in our examination of the theory of the firm. Markets populated byconsumers and producers who satisfice instead of optimizing do not meet theconditions on which the theorems rest. But the experimental data on simulated
markets show that market clearing, the only property of markets for which there
is solid empirical evidence, can be achieved without the optimizing assumptions,hence also without claiming that markets do produce a Pareto optimum. As
Samuel Johnson said of the dancing dog, "The marvel is not that it dances well,
but that it dances at all "the marvel is not that markets optimize (they don't) but
that they often clear.
Order Without a Planner
We have become accustomed to the idea that a natural system like the humanbody or an ecosystem regulates itself. This is in fact a favorite theme of thecurrent discussion of complexity which we will take up in later chapters. We
explain the regulation by feedback loops rather than a central planning anddirecting body. But somehow, untutored intuitions about self-regulation without
central direction do not carry over to the artificial systems of human society. I
retain vivid memories of the astonishment and disbelief expressed by thearchitecture students to whom I taught urban land economics many years ago
when I pointed to medieval cities as marvelously patterned systems that had
mostly just "grown" in response to myriads of individual human decisions. To
my students a pattern implied a planner in whose mind it had been conceived andby whose hand it had been implemented. The idea that a city could acquire its
pattern as naturally as a snowflake was
11.

See Gerard Debreu, Theory of Value: An Axiomatic Analysis of Economic Equilibrium (NewYork: Wiley, 1959).

Page 34

foreign to them. They reacted to it as many Christian fundamentalists responded
to Darwin: no design without a Designer!
Marxist fundamentalists reacted in a similar way when, after World War I, they
undertook to construct the new socialist economies of eastern Europe. It tookthem some thirty years to realize that markets and prices might play aconstructive role in socialist economies and might even have important
advantages over central planning as tools for the allocation of resources. My
sometime teacher, Oscar Lange, was one of the pioneers who carried thisheretical notion to Poland after the Second World War and risked his career andhis life for the idea.
With the collapse of the Eastern European economies around 1990 the simple
faith in central planning was replaced in some influential minds by an equallysimple faith in markets. The collapse taught that modern economies cannot
function well without smoothly operating markets. The poor performance of
these economies since the collapse has taught that they also cannot function wellwithout effective organizations.
If we focus on the equilibrating functions of markets and put aside the illusionsof Pareto optimality, market processes commend themselves primarily because
they avoid placing on a central planning mechanism a burden of calculation thatsuch a mechanism, however well buttressed by the largest computers, could notsustain. Markets appear to conserve information and calculation by assigningdecisions to actors who can make them on the basis of information that isavailable to them locally that is, without knowing much about the rest of theeconomy apart from the prices and properties of the goods they are purchasing
and the costs of the goods they are producing.
No one has characterized market mechanisms better than Friederich von Hayek
who, in the decades after World War II, was their leading interpreter anddefender. His defense did not rest primarily upon the supposed optimum attainedby them but rather upon the limits of the inner environment the computational
limits of human beings:12
The most significant fact about this system is the economy of knowledge with which it operates, or
how little the individual participants need to know in order to be able to take the right action.
12.

F. von Hayek, "The Use of Knowledge in Society," American Economic Review, 35(September1945):519 30, at p. 520.

Page 35

The experiments on simulated markets, described earlier, confirm his view. At
least under some circumstances, market traders using a very small amount of
mostly local information and extremely simple (and non-optimizing) decisionrules, can balance supply and demand and clear markets.
It is time now that we turn to the role of organizations in an organization-&market economy and the reasons why all economic activities are not left tomarket forces. In preparation for this topic, we need to look at the phenomena of
uncertainty and expectations.
Uncertainty and Expectations
Because the consequences of many actions extend well into the future, correct
prediction is essential for objectively rational choice. We need to know aboutchanges in the natural environment: the weather that will affect next year's
harvest. We need to know about changes in social and political environments
beyond the economic: the civil warfare of Bosnia or Sri Lanka. We need to know
about the future behaviors of other economic actors customers, competitors,
suppliers which may be influenced in turn by our own behaviors.
In simple cases uncertainty arising from exogenous events can be handled byestimating the probabilities of these events, as insurance companies do but
usually at a cost in computational complexity and information gathering. An
alternative is to use feedback to correct for unexpected or incorrectly predicted
events. Even if events are imperfectly anticipated and the response to them lessthan accurate, adaptive systems may remain stable in the face of severe jolts,
their feedback controls bringing them back on course after each shock that
displaces them. After we fail to predict the blizzard, snow plows still clear thestreets. Although the presence of uncertainty does not make intelligent choice
impossible, it places a premium on robust adaptive procedures instead of
optimizing strategies that work well only when finely tuned to precisely known
environments.13
13.

A remarkable paper by Kenneth Arrow, reprinted in The New Palgrave: A Dictionary of
Economics (London: Macmillan Press, 1987), v. 2, pp. 69 74, under the title of "Economic Theory
and the Hypothesis of Rationality," shows that to preserve the Pareto optimality properties ofmarkets when there is uncertainty(footnote continued on next page)Page 36

Expectations
A system can generally be steered more accurately if it uses feed forward, basedon prediction of the future, in combination with feedback, to correct the errors of
the past. However, forming expectations to deal with uncertainty creates its own
problems. Feed forward can have unfortunate destabilizing effects, for a system
can overreact to its predictions and go into unstable oscillations. Feed forward inmarkets can become especially destabilizing when each actor tries to anticipate
the actions of the others (and hence their expectations).
The standard economic example of destabilizing expectations is the speculative
bubble. Bubbles that ultimately burst are observed periodically in the world's
markets (the Tulip Craze being one of many well-known historical examples).
Moreover, bubbles and their bursts have now been observed in experimental
markets, the overbidding occurring even though subjects know that the market
must again fall to a certain level on a specified and not too distant date.
Of course not all speculation blows bubbles. Under many circumstances market
speculation stabilizes the system, causing its fluctuations to become smaller, forthe speculator attempts to notice when particular prices are above or below their"normal" or equilibrium levels in order to sell or buy, respectively. Such actions
push the prices closer to equilibrium.
Sometimes, however, a rising price creates the expectation that it will go higher
yet, hence induces buying rather than selling. There ensues a game of economic"chicken," all the players assuming that they can get out just before the crashoccurs. There is general consensus in economics that destabilizing expectations
play an important role in monetary hyperinflation and in the business cycle.
There is less consensus as to whose expectations are the first movers in the chainof reactions or what to do about it.
The difficulties raised by mutual expectations appear wherever markets are not
perfectly competitive. In perfect competition, each firm assumes that market
prices cannot be affected by their actions: prices are as much a part of theexternal environment as are the laws of the physical world.
(footnote continued from previous page)about the future, we must impose information and computational requirements on economic actorsthat are exceedingly burdensome and unrealistic.

Page 37

But in the world of imperfectly competitive markets, firms need not make thisassumption. If, for example, there are only a few firms in an industry, each may
try to outguess its competitors. If more than one plays this game, even thedefinition of rationality comes into question.
The Theory of GamesA century and a half ago, Augustin Cournot undertook to construct a theory ofrational choice in markets involving two firms.14 He assumed that each firm,with limited cleverness, formed an expectation of its competitor's reaction to its
actions, but that each carried the analysis only one move deep. But what if one of
the firms, or both, tries to take into account the reactions to the reactions? They
may be led into an infinite regress of outguessing.
A major step toward a clearer formulation of the problem was taken a century
later, in 1944, when von Neumann and Morgenstern published The Theory ofGames and Economic Behavior.15 But far from solving the problem, the theory ofgames demonstrated how intractable a task it is to prescribe optimally rational
action in a multiperson situation where interests are opposed.
The difficulty of defining rationality exhibits itself well in the so-called
Prisoners' Dilemma game.16 In the Prisoners' Dilemma, each player has a choice
between two moves, one cooperative and one aggressive. If both choose thecooperative move, both receive a moderate reward. If one chooses thecooperative move, but the other the aggressive move, the co-operator is
penalized severely while the aggressor receives a larger reward. If both choose
the aggressive move, both receive lesser penalties. There is no obvious rational
strategy. Each player will gain from cooperation if and only if the partner does
not aggress, but each will gain even more from aggression if he can count on thepartner to cooperate. Treachery pays, unless it is met with treachery. The
mutually beneficial strategy is unstable.
14.

Researches into the Mathematical Principles of the Theory of Wealth (New York: Augustus M.
Kelley, 1960), first published in 1838.
15.
16.

Princeton: Princeton University Press, 1944.

R. D. Luce and H. Raiffa, Games and Decisions (New York: Wiley, 1957), pp. 94 102; R. M.
Axelrod, The Evolution of Cooperation, (New York: Basic Books, 1984).

Page 38

Are matters improved by playing the game repetitively? Even in this case,cleverly timed treachery pays off, inducing instability in attempts at cooperation.
However, in actual experiments with the game, it turns out that cooperative
behavior occurs quite frequently, and that a tit-for-tat strategy (behave
cooperatively until the other player aggresses; then aggress once but return tocooperation if the other player also does) almost always yields higher rewards
than other strategies. Roy Radner has shown (personal communication) that if
players are striving for a satisfactory payoff rather than an optimal payoff, thecooperative solution can be stable. Bounded rationality appears to produce better
outcomes than unbounded rationality in this kind of competitive situation.
The Prisoners' Dilemma game, which has obvious real-world analogies in both
politics and business, is only one of an unlimited number of games that illustrates
the paradoxes of rationality wherever the goals of the different actors conflicttotally or partially. Classical economics avoided these paradoxes by focusing
upon the two situations (monopoly and perfect competition) where mutual
expectations play no role.
Market institutions are workable (but not optimal) well beyond that range ofsituations precisely because the limits on human abilities to compute possible
scenarios of complex interaction prevent an infinite regress of mutual
outguessing. Game theory's most valuable contribution has been to show thatrationality is effectively undefinable when competitive actors have unlimited
computational capabilities for outguessing each other, but that the problem does
not arise as acutely in a world, like the real world, of bounded rationality.
Rational Expectations
A different view from the one just expressed was for a time popular ineconomics: that the problem of mutual outguessing should be solved byassuming that economic actors form their expectations ''rationally."17 This isinterpreted to mean that the actors know (and agree on) the laws that govern theeconomic system and that their predic17.

The idea and the phrase "rational expectations" originated with J. F. Muth, "Rational Expectations
and the Theory of Price Movements," Econometrica, 29(1961):315 335. The notion was picked up,
developed, and applied systematically to macroeconomics by R. E. Lucas, Jr., E. C. Prescott, T. J.
Sargent, and others.

Page 39

tions of the future are unbiased estimates of the equilibrium defined by theselaws. These assumptions rule out most possibilities that speculation will bedestabilizing.
Although the assumptions underlying rational expectations are empiricalassumptions, almost no empirical evidence supports them, nor is it obvious inwhat sense they are "rational" (i.e., utility maximizing). Business firms,
investors, or consumers do not possess even a fraction of the knowledge or thecomputational ability required for carrying out the rational expectations strategy.
To do so, they would have to share a model of the economy and be able tocompute its equilibrium.
Today, most rational expectationists are retreating to more realistic schemes of"adaptive expectations," in which actors gradually learn about their environmentsfrom the unfolding of events around them.18 But most approaches to adaptive
expectations give up the idea of outguessing the market, and instead assume thatthe environment is a slowly changing "given" whose path will not besignificantly affected by the decisions of any one actor.
In sum, our present understanding of the dynamics of real economic systems isgrossly deficient. We are especially lacking in empirical information about how
economic actors, with their bounded rationality, form expectations about the
future and how they use such expectations in planning their own behavior.
Economics could do worse than to return to the empirical methods proposed (andpracticed) by George Katona for studying expectation formation,19 and to an
important extent, the current interest in experimental economics represents sucha return. In face of the current gaps in our empirical knowledge there is little
empirical basis for choosing among the competing models currently proposed byeconomics to account for business cycles, and consequently, little rational basis
for choosing among the competing policy recommendations that flow from thosemodels.
18.

T. J. Sargent, Bounded Rationality in Macroeconomics (Oxford: Clarendon Press, 1993). Note that
Sargent even borrows the label of "bounded rationality" for his version of adaptive expectations, but,
regrettably, does not borrow the empirical methods of direct observation and experimentation thatwould have to accompany it in order to validate the particular behavioral assumptions he makes.
19.

G. Katona, Psychological Analysis of Economic Behavior (New York: McGraw-Hill, 1951).

Page 40

Business Organizations
We turn now to the great mass of economic activity that takes place within theinternal environments of organizations. The key question here, one muchdiscussed in "the new institutional economics" (NIE),20 is: what determines theboundary between organizations and markets; when will one be used, and when
the other, to organize economic activity?
The Organization-Market Boundary
At the outset it should be observed that the boundary is often quite movable. For
example, retail sales of automobiles are usually handled by dealerships,
organizations with separate ownership from the manufacturers. Many othercommodities are sold directly to consumers by manufacturers, and in someindustries (e.g., fast foods) there is a combination of direct outlets and franchiseagencies. The franchise is an excellent example of a hybrid species, as is thesole-source vendor who supplies raw materials or parts to a manufacturer.
We take the frequent movability or indefiniteness of organizational boundaries asevidence that often there is nearly a balance between the advantages of markets
and organizations. Nevertheless we recall again the vast activity that takes place
inside organizations, many of them very large, as an indication that in many
circumstances they offer important advantages over markets.
The NIE explanation for sometimes preferring organizations to markets is thatcertain kinds of market contracts incur transaction costs that can be avoided or
reduced by replacing the sales contract by an employment relation. On the other
hand, as all economic actors are supposed by the NIE theory to be motivated byselfish interest, organizations incur the costs of rewarding their employees forfollowing organizational goals instead of personal interest and of supervisingthem to see that they do so.21
This account of the relative advantages of the two institutions misses essential
parts of the story, especially the opportunities for decentralization of decision
making within organizations. These opportunities de20.
21.

O. E. Williamson, Markets and Hierarchies (New York: The Free Press, 1975).

O. E. Williamson, op. cit.; O. E. Williamson, The Economic Institutions of Capitalism (New York:The Free Press, 1985).

Page 41pend, in turn, upon the strength of the loyalties of employees to theirorganizations, and their identification with organizational objectives that derives
from loyalty and from the local informational environment in which they findthemselves.
Decentralization
Organizations are not highly centralized structures in which all the important
decisions are made at the center. Organizations operating in that centralized way
would exceed the limits of human procedural rationality and lose many of theadvantages attainable from the use of hierarchical authority. Real-worldorganizations behave quite differently.22
As a single decision may be influenced by a large number of facts and criteria of
choice, some fraction of these premises may be specified by superiors without
implying complete centralization. Organizations can localize and minimize
information demands just as markets do, by decentralizing decisions. Matters offact can be determined wherever the most skill and information is located todetermine them, and they can then be communicated to "collecting points" where
all the facts relevant to an issue can be put together and a decision reached. We
can think of a decision as produced by executing a large computer program, eachsubroutine having its special tasks and relying on local sources of information.
No single person or group need be expert on all aspects of the decision.
Thus business organizations, like markets, are vast distributed computers whose
decision processes are substantially decentralized. The top level of a large
corporation, which is typically subdivided into specialized product groups, will
perform only a few functions, most often: (1) the "investment banking" functionof allocating funds for capital projects, (2) selection of top executive personnel,
and (3) long-range planning for capital funds and for possible new activities
outside the scope of existing divisions.
Markets and organizations, however decentralized, are not fully equivalent intheir effects. None of the theorems of optimality in resource
22.

J. G. March and H. A. Simon, Organizations, 2nd ed. (Cambridge, MA: Blackwell, 1993).

Page 42

allocation that are provable for ideal competitive markets can be proved for
hierarchies, but this does not mean that real organizations operate inefficiently ascompared to real markets.
Externalities
Economists sometimes state the case for organizations as opposed to markets interms of externalities. Externalities arise because the price mechanism works asadvertised only when all of the inputs and outputs of an activity are subject tomarket pricing. A traditional example of an externality is a factory that isallowed to spew smoke from its stacks without compensating the surrounding
homeowners. In these circumstances the price mechanism will not secure asocially desirable level of manufacturing activity; the product, priced below its
social cost, will be overused.
The economist's preferred remedy for externalities is to bring the undesiredconsequences within the calculus of the price system: tax the emission of smoke,for example. This raises the question of how the tax is to be set. Although thetechniques of cost-benefit analysis can provide answers, they are administrative
answers and not answers given by an automatic market mechanism.
Similar questions of externalities among corporate divisional operations makelarge corporations less than fully willing to allow transactions among theircomponent divisions and departments to be governed wholly by internal markets.
In the absence of perfect competition, internal market prices are administered ornegotiated prices, not competitive prices.
Uncertainty
Uncertainty often persuades social systems to use hierarchy rather than markets
in making decisions. It is not reasonable to allow the production department andthe marketing department in the widget company to make independent estimatesof next year's demand for widgets if the production department is to make thewidgets that the marketing department is to sell. In matters like this, and also inmatters of product design, it may be preferable that all the relevant departments
operate on the same body of assumptions even if (or perhaps "especially if") the
uncertainties might justify quite a range of different assumptions. In facing
uncertainty, standardization and coordination, achieved through agreed-uponassumptions and specifications, may be more effective than prediction.

Page 43

Uncertainty calls for flexibility, but markets do not always provide the greatest
flexibility in the face of uncertainty. All depends on the sources of theuncertainty. If what is uncertain is a multitude of facts about individual andseparate markets, then decentralized pricing will appear attractive; if theuncertainty encompasses major events that will affect many parts of theorganization in the same direction, then it may be advantageous to centralize themaking of assumptions about the future and to require the decentralized units to
use these assumptions in their decisions.
Uncertainty is especially troublesome when it involves expectations by one unit
about what other units in the same organization will do. Left to the market, thiskind of uncertainty leads directly to the dilemmas of rationality that we describedearlier in terms of game theory and rational expectations. Absorption of theuncertainty by the organization through managerial coordination may be themost effective course. We see in uncertainty a frequent source of advantage oforganizations over markets as decision-making mechanisms.
In a world of bounded rationality there are several ways to magnify thecomputing capabilities of individual human beings and enhance the possibilitiesof their collective survival and prosperity. With the combined use of markets andadministrative hierarchies, the human species has enormously increased its
capabilities for specialization and division of work. It would be too much toattribute the vast growth and spread of human populations to such mechanisms
alone modern medicine and modern technology have had something to do with it
too but the (perhaps temporary) dominance of our species over the globe today iswitness to the augmentation of human reason applied to local, not global,
concerns that has been made possible by these social artifacts.
Organizational Loyalties and Identifications
Brief mention was made earlier of a crucial reason why so much human activity
takes place within organizations: people acquire loyalty, and often a large
amount of loyalty, to the groups, including organizations, to which they belong.
Consequences of Identification
Organizational loyalty is perhaps better labeled identification, for it is both
motivational and cognitive. The motivational component is an attachment togroup goals and a willingness toPage 44

work for them even at some sacrifice of personal goals. (In effect, the group
goals become personal goals.) The ethnic conflict we observe in many parts of
the world provides vivid evidence of this attachment to group goals and thedifferential treatment it generates between "we" and "they."
Identification with an organization also has a cognitive component, for members
are surrounded by information, conceptions and frames of reference quite
different from those of people outside the organization or in a different
organization. As creatures of bounded rationality, incapable of dealing with theworld in all of its complexity, we form a simplified picture of the world, viewing
it from our particular organizational vantage point and our organization's
interests and goals.
This frame of reference and information provided by an organization influencestrongly the processing and outcomes of decisions. The frame of reference
varies, too, from one organization unit to another and from one level to another,so that an employee may identify at one time with his department, at anotherwith his section, at another with the whole company.
Affected by their organizational identifications, members frequently pursue
organizational goals at the expense of their own interests that is to say, behave ina way that is altruistic from a personal standpoint. No organization could survivethat elicited only behavior for which employees felt selfishly rewarded and thatsupervisors could enforce. The added effort that is elicited by identification is amajor and essential source of organizational effectiveness and is a principal
reason for carrying out economic activities in organizations rather than markets.
Evolutionary Basis for Identification
It may be objected that human beings are basically selfish and do not behave inthis altruistic fashion. In fact, neo-Darwinian evolutionary theory has generally
claimed that altruism, except to close relatives, is inconsistent with the basic
postulate that organisms evolve to increase their fitness.23 However, I should liketo show that this widely repeated claim is mistaken.24
23.

The case is stated, for example, in R. Dawkins, The Selfish Gene (New York: Oxford University
Press, 1989).
24.

H. A. Simon, "A Mechanism for Social Selection and Successful Altruism," Science, 250(1990):1665
1668.

Page 45

Because of their bounded rationality, and because they can therefore greatly
enhance their limited knowledge and skill by accepting information and advice
from the social groups to which they belong, individuals who are docile who
tend to accept such information and advice have a great advantage in fitness overthose who are not docile who reject social influence. Docile people do not haveto learn about hot stoves by touching them.
Most social influence does enhance the fitness of the recipient. It provides
information and advice about the world that is generally valid or at least much
more informative and valid than the information the recipient could generate
independently. But docility can be "taxed" by influencing people also to takecertain actions that are not personally beneficial but are beneficial to the group.
As long as the "taxation" is not so heavy as to cancel the advantages of docility,
the altruistic individual will be fitter than the non-docile individual. By thismeans, the fitness of the organization will be enhanced by the docility, hence
altruism, of its members. Although, docility is generally rewarding to theindividual, some fraction of the behavior it induces is altruistic in this sense, andthis altruism is an important factor in the efficacy of organizations.
We can summarize our account of the respective roles of markets andorganizations in a modern society as follows: (1) organizations find their niches
wherever constellations of interdependent activities are best carried out incoordinated fashion in order to remove the need for individuals' outguessing eachother; (2) the human motivation that makes organizations viable and alleviates
the public goods problems that arise when individual efforts cannot be tiedclosely to individual rewards is provided by organizational loyalty andidentification; (3) in both organizations and markets, the bounds on humanrationality are addressed by arranging decisions so that the steps in decision
making can depend largely on information that is locally available to individuals.
The Evolutionary Model
Evolutionary processes are significant not only for explaining organizational
loyalty, but also for describing and explaining the historical development of
economic institutions, including business firms. The simplest

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scheme of evolution depends on two processes: a generator and a test. The
generator produces variety, new forms that have not existed previously, whereas
the test culls out the generated forms so that only those that are well fitted to theenvironment will survive. In modern biological Darwinism, genetic mutation andcrossover of chromosomes are the principal generators, and natural selection is
the test.
The Alternative Theory of Economic Man
No one supposes that a modern organization-&-market economy is the productof deliberate design. Surely it evolved from earlier subsistence economies,
shaped by myriads of decisions made by hosts of actors over thousands of years.
By contrast, most accounts of business firms assume that actors deliberately
select actions appropriate to their goals within the context of the given economic
environment. Adaptation, in the latter accounts, stems from selection by rational
actors, not by natural selection of those actors whose behavior happens to beadaptive. An evolutionary theory of the firm might argue that it does not matter
whether people maximize or satisfice, for in a world of competitive markets only
those who make decisions as if they were maximizing will survive.25 Does thisevolutionary argument in fact imply optimization?
Our discussion will have implications for biology as well as economics, forevolutionary biology uses the language of optimality quite freely and in recent
years has even borrowed linear programming and other OR techniques to predict
the outcomes of natural selection in biological systems. This is legitimate only if
optimization would lead reliably to the same equilibria as would natural
selection.
Local and Global Maxima
For the question before us, the difference between local and global maxima is
crucial. In the landscape of California every tiny hill is a local maxi25.

A. A. Alchian, "Uncertainty, Evolution, and Economic Theory," Journal of Political Economy,
58(1950):211 222; M. Friedman, "The Methodology of Positive Economics," in Essays in Positive
Economics (Chicago: University of Chicago Press, 1953). The identification of selection with
optimization is challenged by S. G. Winter, for example, in his "Economic Natural Selection and theTheory of the Firm," Yale Economic Essays, 4(1964):225 272.

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mum of altitude, but only Mt. Whitney is a global maximum. For many purposes
it makes a difference whether one finds oneself standing on Nob Hill or Mt.
Whitney. Finding a local maximum is usually easy: walk uphill until there is noplace to walk. Finding the global maximum, on the other hand, is usually
exceedingly complex unless the terrain has very special properties (no local
maxima). The world of economic affairs is replete with local maxima. It is quite
easy to devise systems in which each subsystem is optimally adapted to the othersubsystems around it, but in which the equilibrium is only local, and quite
inferior to distant equilibria that cannot be reached by the up-hill climb of
evolution.
The Myopia of Evolution
Darwinian evolution is completely myopic. At each incremental step theevolving organism becomes fitter relative to its current environment, but there is
no reason for the progress to lead to a global maximum of fitness of theindividuals, separately or severally. If we are considering this kind of system,
whose environment has a multitude of local maxima, we cannot understand thesystem unless we know something of the method and history of its evolution.
Nor is there any reasonable sense in which such a system can be regarded as"fittest."
This is not just an in-principle objection to confounding hill climbing with
optimization. In a myopic hill-climbing system, it may be difficult or impossible
to move from a local maximum to another that is in view across a deep valley.
The movement from the English system of measures to the metric system is acase in point. A society starting from scratch, and familiar with both systems,
would surely prefer the metric to the English system. But if future benefits are
discounted at some rate of interest, it might never be economical to switch fromthe one system, once adopted, to the other.
Hence, from the fact that an economic system is evolving, one cannot conclude
that it has reached or is likely to reach a position that bears any resemblance tothe equilibria found in the theory of perfect competition. Each species in theecosystem is adapting to an environment of other species evolving
simultaneously with it. The evolution and future of such systems can only beunderstood from a knowledge of their histories.

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The Mechanisms of Economic Evolution
If the adaptation of both the business firm and biological species to theirrespective environments are instances of heuristic search, hence of local
optimization or satisficing, we still have to account for the mechanisms that bringthe adaptation about. In biology the mechanism is located in the genes and theirsuccess in reproducing themselves. What is the gene's counterpart in the business
firm?
Nelson and Winter suggest that business firms accomplish most of their workthrough standard operating procedures algorithms for making daily decisions that
become routinized and are handed down from one generation of executives andemployees to the next.26 Evolution derives from all the processes that produce
innovation and change in these algorithms. The fitness test is the profitability andgrowth rate of the firm. Profitable firms grow by the reinvestment of their profits
and their attractiveness for new investment.
Nelson and Winter observe that in economic evolution, in contrast to biologicalevolution, successful algorithms may be borrowed by one firm from another.
Thus the hypothesized system is Lamarkian, because any new idea can beincorporated in operating procedures as soon as its success is observed, andhence successful mutations can be transferred between firms. Transfer is ofcourse not costless, but involves learning costs for the adopting firm. It may also
be impeded by patent protection and commercial secrecy. Nevertheless,processes of the kinds just described play a large role in the gradual evolution ofan economic system composed of business firms.
From these considerations, one sees that the evolution of firms and of economies
does not lead to any easily predictable equilibrium, much less an optimum, but isa complex process, probably continuing indefinitely, that is probably best
understood through an examination of its history. As in any dynamic system thathas propensities for following diverging paths from almost identical starting
points, equilibrium theories of an economy can tell us little about either its
present state or its future.
26.

R. R. Nelson and S. G. Winter, An Evolutionary Theory of Economic Change (Cambridge:
Harvard University Press, 1982).

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Human Society
Economics has been unfairly labeled the ''gloomy" science, for in its Ricardianform, incorporating Malthusian views of the pressure of population on resources,
it did not hold out much hope for human progress. The label is unfair, because
economics in fact draws a romantic, almost heroic, picture of the human mind.
Classical economics depicts humankind, individually and collectively, as solving
immensely complex problems of optimizing the allocation of resources. The
artfulness of the economic actors enables them to make the very best adaptationsin their environments to their wants and needs. In this chapter, while keeping theadaptive capabilities of mind in the center of things, I have tried to suggest amore complex state of affairs. A veridical picture of economic actors andinstitutions must incorporate the information processing limits set by their innerenvironments. The picture must also accommodate both the conscious rationalityof economic decision makers and the unplanned but adaptive evolutionary
processes that have molded economic institutions.
Operations research and artificial intelligence have enhanced the procedural
rationality of economic actors, helping them to make better decisions. On alarger scale, markets and organizations are social schemes that facilitate
coordinated behavior, at the same time conserving the critical scarce resource ofhuman ability to handle complexity and great masses of information. In thischapter I have not tried to evaluate these forms of individual and socialorganization, but simply to describe them as commonly used solutions to thecentral human problem of accommodating to our bounded rationality.
The analysis shows that a deeper understanding of the tools of procedural
rationality requires a closer examination of how the human mind works, of thelimits on human rationality. The next two chapters will describe what has beenlearned in the past half century about human information processing. Chapter 3
will focus on problem solving processes and general cognitive architecture,
chapter 4 on memory and learning processes.

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Page 51

3
The Psychology of Thinking: Embedding Artifice in Nature
We watch an ant make his laborious way across a wind- and wave-molded
beach. He moves ahead, angles to the right to ease his climb up a steep dune let,detours around a pebble, stops for a moment to exchange information with acompatriot. Thus he makes his weaving, halting way back to his home. So as not
to anthropomorphize about his purposes, I sketch the path on a piece of paper. It
is a sequence of irregular, angular segments not quite a random walk, for it has
an underlying sense of direction, of aiming toward a goal.
I show the unlabelled sketch to a friend. Whose path is it? An expert skier,perhaps, slaloming down a steep and somewhat rocky slope. Or a sloop, beating
upwind in a channel dotted with islands or shoals. Perhaps it is a path in a more
abstract space: the course of search of a student seeking the proof of a theorem ingeometry.
Whoever made the path, and in whatever space, why is it not straight; why does
it not aim directly from its starting point to its goal? In the case of the ant (andfor that matter the others) we know the answer. He has a general sense of where
home lies, but he cannot foresee all the obstacles between. He must adapt hiscourse repeatedly to the difficulties he encounters and often detour uncrossablebarriers. His horizons are very close, so that he deals with each obstacle as hecomes to it; he probes for ways around or over it, without much thought for
future obstacles. It is easy to trap him into deep detours.
Viewed as a geometric figure, the ant's path is irregular, complex, hard todescribe. But its complexity is really a complexity in the surface of the beach,
not a complexity in the ant. On that same beach another small

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creature with a home at the same place as the ant might well follow a very
similar path.
Many years ago Grey Walter built an electromechanical "turtle," having only
tactile sense of its environment but capable of exploring a room, and periodicallyseeking its nest to recharge its batteries.1 Today, robots with modest visualsensory capabilities roam about in a number of artificial intelligence
alaboratories.2 Suppose we undertook to design an automaton with theapproximate dimensions of an ant, similar means of locomotion, and comparable
sensory acuity. Suppose we provided it with a few simple adaptive capabilities:
when faced with a steep slope, try climbing it obliquely; when faced with aninsuperable obstacle, try detouring; and so on. (Except for problems ofminiaturization of components, the present state of the art would readily support
such a design.) How different would its behavior be from the behavior of the ant?
These speculations suggest a hypothesis, one that could as well have been
derived as corollary from our previous discussion of artificial objects:
An ant, viewed as a behaving system, is quite simple. The apparent complexityof its behavior over time is largely a reflection of the complexity of theenvironment in which it finds itself.
We may find this hypothesis initially plausible or implausible. It is an empiricalhypothesis, to be tested by seeing whether attributing quite simple properties tothe ant's adaptive system will permit us to account for its behavior in the given or
similar environments. For the reasons developed at length in the first chapter, thetruth or falsity of the hypothesis should be independent of whether ants, viewed
more microscopically, are simple or complex systems. At the level of cells ormolecules ants are demonstrably complex, but these microscopic details of theinner environment may be largely irrelevant to the ant's behavior in relation tothe outer
1.
2.

W. Grey Walter, "An Imitation of Life," Scientific American, 185(1950):42.

See, for example, R. Brooks, "A Robust-layered Control System for a Mobile Robot," IEEE Journalof Robotics and Automation, RA-2(1986):14 23. And a motor vehicle, NAVLAB, steered itself in theSummer of 1995 on public highways from Washington, D.C., to San Diego, California, and has also
demonstrated strong capabilities for off-road navigation.

Page 53

environment. That is why an automaton, though completely different at the
microscopic level, might nevertheless simulate the ant's gross behavior.
In this chapter I should like to explore this hypothesis but with the word "humanbeing" substituted for "ant."
Human beings, viewed as behaving systems, are quite simple. The apparent
complexity of our behavior over time is largely a reflection of the complexity ofthe environment in which we find ourselves.
Now I should like to hedge my bets a little. Instead of trying to consider the"whole person," fully equipped with glands and viscera, I should like to limit thediscussion to Homo sapiens, "thinking person." I myself believe that the
hypothesis holds even for the whole person, but it may be more prudent to divide
the difficulties at the outset, and analyze only cognition rather than behavior ingeneral.3
I should also like to hedge my bets in a second way, for a human being can store
away in memory a great furniture of information that can be evoked byappropriate stimuli. Hence I would like to view this information-packed memory
less as part of the organism than as part of the environment to which it adapts.
The reasons for assigning some a priori probability to the hypothesis ofsimplicity have already been set forth in the last two chapters. A thinking humanbeing is an adaptive system; men's goals define the interface between their innerand outer environments, including in the latter their memory stores. To the extent
that they are effectively adaptive, their behavior will reflect characteristics
largely of the outer environment (in the light of their goals) and will reveal only a
few limiting properties of the inner environment of the physiological machinerythat enables a person to think.
3.

I have sketched an extension of this hypothesis to phenomena of emotion and motivation in"Motivational and Emotional Controls of Cognition," Psychological Review, 74(1967):29 39, and tocertain aspects of perception in "An Information-Processing Explanation of Some Perceptual
Phenomena," British Journal of Psychology, 58(1967):1 12. Both papers are reprinted in my Modelsof Thought, vol. 1 (1979), chapters 1.3 and 6.1. The discussion of these issues is continued in''Bottleneck of Attention: Connecting Thought with Motivation," in W. D. Spaulding (ed.),Integrative Views of Motivation, Cognition and Emotion. Lincoln, NE: University of Nebraska Press,
1994.

Page 54

I do not intend to repeat this theoretical argument at length, but rather I want toseek empirical verification for it in the realm of human thought processes.
Specifically I should like to point to evidence that there are only a few "intrinsic"
characteristics of the inner environment of thinking beings that limit theadaptation of thought to the shape of the problem environment. All else inthinking and problem-solving behavior is artificial is learned and is subject toimprovement through the invention of improved designs and their storage inmemory.
Psychology As a Science of the Artificial
Problem solving is often described as a search through a vast maze ofpossibilities, a maze that describes the environment. Successful problem solving
involves searching the maze selectively and reducing it to manageable
proportions. Let us take, by way of specific example, a puzzle of the kind known
as crypt arithmetic problems:4

The task is to replace the letters in this array by numerals, from zero throughnine, so that all instances of the same letter are replaced by the same numeral,
different letters are replaced by different numerals, and the resulting numericalarray is a correctly worked out problem in arithmetic. As an additional hint forthis particular problem, the letter D is to be replaced by the numeral 5.
One way of viewing this task is to consider all the 10!, ten factorial, ways inwhich ten numerals can be assigned to ten letters. The number 10! is not so large
as to strike awe in the heart of a modern computer; it is only a little more than 3
million (3,628,800, to be exact). A program designed to generate all possible
assignments systematically, and requiring
4.

The crypt arithmetic task was first used for research on problem solving by F. Bartlett in his
Thinking (New York: Basic Books, 1958). In the present account I have drawn on his work and on
my research with Allen Newell reported in our book, Human Problem Solving (Englewood Cliffs,
N.J.: Prentice-Hall, 1972), chapters 8 10.

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a tenth of a second to generate and test each, would require at most about ten
hours to do the job. (With the cue D = 5, only an hour would be needed.) I
haven't written the program, but a tenth of a second is far longer than a computer
would need to examine each possibility.
There is no evidence that a human being could do this. It might take a man aslong as a minute to generate and test each assignment, and he would have great
difficulty in keeping track of where he was and what assignments he had alreadytried. He could use paper and pencil to assist him on the latter score, but thatwould slow him down even more. The task, performed in this way, might call forseveral man-years of work I assume a forty-hour week.
Notice that in excluding exhaustive, systematic search as a possible way for ahuman to solve the problem, we are making only very gross assumptions abouthuman capabilities. We are assuming that simple arithmetic operations take times
that are of the order of seconds, that the operations are essentially executed
serially, rather than in parallel, and that large amounts of memory are notavailable in which new information can be stored at split-second speeds. Theseassumptions say something, but not very much, about the physiology of thehuman central nervous system. For example, modifying the brain byincorporating in it a new subsystem with all the properties of a desk calculator
would be a quite remarkable feat of brain surgery or evolution. But even such aradical alteration would change the relevant assumptions only slightly for
purposes of explaining or predicting behavior in this problem environment.
Human beings do frequently solve the DONALD + GERALD = ROBERT
problem. How do they do it? What are the alternative ways of representing theenvironment and conducting the search?
Search Strategies
One way to cut down the search drastically is to make the assignments
systematically, as before, but to assign numerals to the letters one by one so thatinconsistencies can be detected before an assignment is complete, and hence
whole classes of possible assignments can be ruled out at one step. Let me
illustrate how this works.
Suppose we start from the right, trying assignments successively for the letters
D, T, L, R, A, E, N, B, O, and G, and substituting numerals in the

Page 56

order 1, 2, 3, 4, 5, 6, 7, 8, 9, 0. We already know that D = 5, so we strike 5 fromthe list of available numerals. We now try T = 1. Checking in the right-handcolumn, we detect a contradiction, for D + D = T + c, where c is 10 or 0. Hence,since (D = 5, T = 1) is not feasible, we can rule out all the remaining 8!
assignments of the eight remaining numerals to the eight remaining letters. In thesame way all possible assignments for T, except T = 0, can be ruled out without
considering the assignments for the remaining letters.
The scheme can be improved further by the expedient of calculating directly, byaddition, what assignment should be made to the sum of a column whenever thetwo addends are known. With this improvement we shall not need to search forthe assignment for T, for T = 0 can be inferred directly from D = 5. Using thisscheme, the DONALD + GERALD = ROBERT problem can be solved quite
readily, with paper and pencil. Ten minutes should suffice. Figure 3 shows thesearch tree, in slightly simplified form. Each branch is carried to the point wherea contradiction is detected. For example, after the assignments (D = 5, T = 0), theassignment L = 1 leads to the inference R = 3, which yields a contradiction sincefrom the left-hand column of the problem array R = 3 would imply that G is
negative.
Figure 3 is oversimplified in one respect. Each of the branches that terminateswith a contradiction after assignment of a value to E should actually be branched
one step further. For the contradiction in these cases arises from observing thatno assignment for the letter O is now consistent. In each case four assignments
must be examined to determine this. Thus the full search tree would have 68
branches still a far cry from 10! or even 9!.
An enormous space has been cut down to a quite small space by some relatively
small departures from systematic, exhaustive search. It must be confessed thatthe departures are not all as simple as I have made them appear. One step in the
proposed scheme requires finding the contradictions implied by an assignment.
This means of course the "relatively direct" contradictions, for if we had a rapid
process capable of detecting all inconsistent implications, direct or indirect, it
would find the problem solution almost at once. In this problem any set of
assignments other than the single correct one implies a contradiction.

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Figure 3
Possible search tree for DONALD + GERALD = ROBERT

What is meant by searching for direct contradictions is something like this: after
a new assignment has been made, those columns are examined where the newly
substituted letter occurs. Each such column is solved, if possible, for a stillunassigned letter, and the solution checked to see whether this numeral remains
unassigned. If not, there is a contradiction.
In place of brute-force search we have now substituted a combined system ofsearch and "reason." Can we carry this process further; can we eliminate
substantially all trial-and-error search from the solution method? It turns out that
we can for this problem, although not for all crypt arithmetic problems.5
The basic idea that permits us to eliminate most trial-and-error search in solving
the problem before us is to depart from the systematic right-to-left assignment of
numerals. Instead we search for columns of the5.

For example, the method to be described does not eliminate as much search from the crypt
arithmetic problem CROSS + ROADS = DANGER.

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problem array that are sufficiently determinate to allow us to make new
assignments, or at least new inferences about the properties of assignments.
Let me go through the process briefly. From D = 5, we immediately infer T = 0,
as before. We also infer that 1 is carried into the second column, hence that R =2L + 1 is odd. On the extreme left, from D = 5, we infer that R is greater than 5(for R = 5 + G). Putting together these two inferences, we have R = 7 or R = 9,
but we do not try these assignments. Now we discover that the second column
from the left has the peculiar structure O + E = Oa number plus another equals
itself (apart from what is carried into or out of the column). Mathematicalknowledge, or experiment, tells us that this can be true only if E = 0 or E = 9.
Since we already have T = 0, it follows that E = 9. This eliminates one of thealternatives for R, so R = 7.
Since E = 9, it follows that A = 4, and there must be a one carried into the thirdcolumn from the right; hence 2L + 1 = 17, or L = 8. All that remains now is toassign 1, 2, 3, and 6 in some order to N, B, O, and G. We get G = 1 by observing
that for any assignment of O there is a number carried into the leftmost column.
We are now left with only 3! = 6 possibilities, which we may be willing toeliminate by trial and error: N = 6, B = 3, and therefore O = 2.
We have traced a solution path through the problem maze on three different
assumptions about the search strategy. The more sophisticated, in a certain sense,that strategy became, the less search was required. But it is important to notice
that, once the strategy was selected, the course of the search depended only on
the structure of the problem, not on any characteristics of the problem solver. By
watching a person, or an automaton, perform in this problem environment, what
could we learn about him? We might well be able to infer what strategy wasfollowed. By the mistakes made, and the success in recovering from them, wemight be able to detect certain limits of the capacity or accuracy of theindividual's memory and elementary processes. We might learn something about
the speed of these processes. Under favorable circumstances, we might be able tolearn which among the thinkable strategies the individual was able actually toacquire and under what circumstances likely to acquire them. We should
certainly be unlikely to learn anything specific about the neurologicalcharacteristics of the central nervous system, nor would the spe-

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cifics of that system be relevant to his behavior, beyond placing bounds on thepossible.
The Limits on Performance
Let us undertake to state in positive fashion just what we think these bounds andlimits are, as revealed by behavior in problem situations like this one. In doing
so, we shall draw upon both experimental evidence and evidence derived fromcomputer simulations of human performance. The evidence refers to a variety ofcognitive tasks, ranging from relatively complex ones (crypt arithmetic, chess,theorem proving), through an intermediate one (concept attainment), to simple
ones that have been favorites of the psychological laboratory (rote verbal
learning, short-term memory span). It is important that with this great variety of
performance only a small number of limits on the adaptability of the inner
system reveal themselves and these are essentially the same limits over all thetasks. Thus the statement of what these limits are purports to provide a single,
consistent explanation of human performance over this whole range ofheterogeneous task environments.
Limits on Speed of Concept Attainment
Extensive psychological research has been carried out on concept attainment
within the following general paradigm.6 The stimuli are a set of cards bearing
simple geometric designs that vary, from card to card, along a number ofdimensions: shape (square, triangle, circle), color, size, position of figure on
card, and so on. A "concept" is defined extensionally by some set of cards the
cards that are instances of that concept. The concept is defined intensionally by aproperty that all the instances have in common but that is not possessed by any of
the remaining cards.
6.

This account of concept attainment is based on the paper with my late colleague Lee Gregg,"Process Models and Stochastic Theories of Simple Concept Formation," Journal of Mathematical
Psychology, 4(June 1967):246 276. See also A. Newell and H. A. Simon, "Overview: Memory andProcess in Concept Formation," chapter 11 in B. Kleinmuntz (ed.), Concepts and the Structure ofMemory (New York: Wiley, 1967), pp. 241 262. The former paper is reprinted in Models of Thought,
vol. 1, chapter 5.4.

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Examples of concepts are "yellow" or "square" (simple concepts), "green
triangle" or "large, red" (conjunctive concepts), ''small or yellow" (disjunctive
concept), and so on.
In our discussion here I shall refer to experiments using an N-dimensional
stimulus, with two possible values on each dimension, and with a single relevant
dimension (simple concepts). On each trial an instance (positive or negative) ispresented to the subject, who responds "Positive" or "Negative" and is reinforced
by "Right" or "Wrong," as the case may be. In typical experiments of this kind,the subject's behavior is reported in terms of number of trials or number of
erroneous responses before an error-free performance is attained. Some, but notall, experiments ask the subject also to report periodically the intensional concept(if any) being used as a basis for the responses.
The situation is so simple that, as in the crypt arithmetic problem, we canestimate a priori how many trials, on the average, a subject should need to
discover the intended concept provided that the subject used the most efficientdiscovery strategy. On each trial, regardless of response, the subject candetermine from the experimenters reinforcement whether the stimulus was
actually an instance of the concept or not. If it was an instance, the subject knowsthat one of the attribute values of the stimulus its color, size, shape, for example
defines the concept. If it was not an instance, the subject knows that thecomplement of one of its attribute values defines the concept. In either case eachtrial rules out half of the possible simple concepts; and in a random sequence ofstimuli each new stimulus rules out, on the average, approximately half of theconcepts not previously eliminated. Hence the average number of trials required
to find the right concept will vary with the logarithm of the number ofdimensions in the stimulus.
If sufficient time were allowed for each trial (a minute, say, to be generous), andif the subject were provided with paper and pencil, any subject of normal
intelligence could be taught to follow this most efficient strategy and would do
so without much difficulty. As these experiments are actually run, subjects are
not instructed in an efficient strategy, are not provided with paper and pencil, andtake only a short time typically four seconds, say to respond to each successive
stimulus. They also use

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many more trials to discover the correct concept than the number calculated fromthe efficient strategy. Although the experiment has not, to my knowledge, beenrun, it is fairly certain that, even with training, a subject who was required torespond in four seconds and not allowed paper and pencil would be unable toapply the efficient strategy.
What do these experiments tell us about human thinking? First, they tell us thathuman beings do not always discover for themselves clever strategies that they
could readily be taught (watching a chess master play a duffer should also
convince us of that). This is hardly a very startling conclusion, although it may
be an instructive one. I shall return to it in a moment.
Second, the experiments tell us that human beings do not have sufficient means
for storing information in memory to enable them to apply the efficient strategy
unless the presentation of stimuli is greatly slowed down or the subjects are
permitted external memory aids, or both. Since we know from other evidence
that human beings have virtually unlimited semi-permanent storage (as indicatedby their ability to continue to store odd facts in memory over most of a lifetime),
the bottleneck in the experiment must lie in the small amount of rapid-accessstorage (so-called short-term memory) available and the time required to moveitems from the limited short-term store to the large-scale long-term store.7
From evidence obtained in other experiments, it has been estimated that only
some seven items can be held in the fast, short-term memory and that perhaps as
many as five to ten seconds are required to transfer an item from the short-termto the long-term store. To make these statements operational, we shall have to be
more precise, presently, about the meaning of "item." For the moment let us
assume that a simple concept is an item.
Even without paper and pencil a subject might be expected to apply the efficient
strategy if (1) he was instructed in the efficient strategy and7.

The monograph by J. S. Bruner, J. J. Goodnow, and G. A. Austin, A Study of Thinking (New York:
Wiley, 1956) was perhaps the first work to emphasize the role of short-term memory limits (theirterm was "cognitive strain") in performance on concept-attainment tasks. That work also provided
rather definite descriptions of some of the subjects' strategies.

Page 62(2) he was allowed twenty or thirty seconds to respond to and process thestimulus on each trial. Since I have not run the experiment, this-assertion stands
as a prediction by which the theory may be tested.
Again the outcome may appear obvious to you, if not trivial. If so, I remind youthat it is obvious only if you accept my general hypothesis: that in large parthuman goal-directed behavior simply reflects the shape of the environment inwhich it takes place; only a gross knowledge of the characteristics of the human
information-processing system is needed to predict it. In this experiment therelevant characteristics appear to be (1) the capacity of short-term memory,
measured in terms of number of items (or "chunks," as I shall call them); (2) thetime required to fixate an item, or chunk, in long-term memory. In the next
section I shall inquire as to how consistent these characteristics appear to be overa range of task environments. Before I do so, I want to make a concludingcomment about subjects' knowledge of strategies and the effects of training
subjects.
That strategies can be learned is hardly a surprising fact, nor that learned
strategies can vastly alter performance and enhance its effectiveness. All
educational institutions are erected on these premises. Their full implication has
not always been drawn by psychologists who conduct experiments in cognition.
Insofar as behavior is a function of learned technique rather than "innate"characteristics of the human information-processing system, our knowledge of
behavior must be regarded as sociological in nature rather than psychologicalthat is, as revealing what human beings in fact learn when they grow up in aparticular social environment. When and how they learn particular things may be
a difficult question, but we must not confuse learned strategies with built-in
properties of the underlying biological system.
The data that have been gathered, by Bartlett and in our own laboratory, on thecrypt arithmetic task illustrate the same point. Different subjects do indeed apply
different strategies in that task both the whole range of strategies I sketched in
the previous section and others as well. How they learned these, or how they
discover them while performing the task, we do not fully know (see chapter 4),
although we know that the sophistication of the strategy varies directly with asubject's previous exposure to and comfort with mathematics. But apart from thestrategies

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the only human characteristic that exhibits itself strongly in the crypt arithmetic
task is the limited size of short-term memory. Most of the difficulties the subjects
have in executing the more combinatorial strategies (and perhaps their generalaversion to these strategies also) stem from the stress that such strategies place
on short-term memory. Subjects get into trouble simply because they forget
where they are, what assignments they have made previously, and whatassumptions are implicit in assignments they have made conditionally. All ofthese difficulties would necessarily arise in a processor that could hold only a
few chunks in short-term memory and that required more time than was available
to transfer them to long-term memory.
The Parameters of Memory Eight Seconds per Chunk
If a few parameters of the sort we have been discussing are the main limits of theinner system that reveal themselves in human cognitive behavior, then it
becomes an important task for experimental psychology to estimate the values ofthese parameters and to determine how variable or constant they are among
different subjects and over different tasks.
Apart from some areas of sensory psychology, the typical experimental
paradigms in psychology are concerned with hypothesis testing rather thanparameter estimating. In the reports of experiments one can find many assertions
that a particular parameter value is or is not "significantly different" from anotherbut very little comment on the values themselves. As a matter of fact thepernicious practice is sometimes followed of reporting significance levels, or
results of the analysis of variance, without reporting at all the numerical valuesof the parameters that underlie these inferences.
While I am objecting to publication practices in experimental psychology, I shall
add another complaint. Typically little care is taken in choosing measures of
behavior that are the most relevant to theory. Thus in learning experiments "rateof learning" is reported, almost indifferently, in terms of "number of trials tocriterion," "total number of errors," "total time to criterion," and perhaps othermeasures as well. Specifically the practice of reporting learning rates in terms of
trials rather than time, prevalent through the first half of this century, and almost
up to thePage 64

present time, not only hid from view the remarkable constancy of the parameter I
am about to discuss but also led to much meaningless dispute over "one-trial"
versus "incremental" learning.8
Ebbinghaus knew better. In his classic experiments on learning nonsense
syllables, with himself as subject, he recorded both the number of repetitions andthe amount of time required to learn sequences of syllables of different length. If
you take the trouble to calculate it, you find that the time per syllable in his
experiments works out to about ten to twelve seconds.9
I see no point in computing the figure to two decimal places or even to one. The
constancy here is a constancy to an order of magnitude, or perhaps to a factor of
two more nearly comparable to the constancy of the daily temperature, which inmost places stays between 263° and 333° Kelvin, than to the constancy of the
speed of light. There is no reason to be disdainful of a constancy to a factor of
two. Newton's original estimates of the speed of sound contained a fudge factorof 30 per cent (eliminated only a hundred years later), and today some of thenewer physical "constants" for elementary particles are even more vague.
Beneath any approximate, even very rough, constancy, we can usually expect tofind a genuine parameter whose value can be defined accurately once we knowwhat conditions we must control during measurement.
If the constancy simply reflected a parameter of Ebbinghausalbeit one that held
steady over several years it would be more interesting to biography thanpsychology. But that is not the case. When we examine some of the HullHovland experiments of the 1930s, as reported, for ex8.

The evidence of the constancy of the fixation parameter is reviewed in L. W. Gregg and H. A.
Simon, "An Information-Processing Explanation of One-Trial and Incremental Learning," Journal of
Verbal Learning and Verbal Behavior, 6(1967):780 787; H. A. Simon and E. A. Feigenbaum, "An
Information-Processing Theory of Verbal Learning," ibid., 3(1964):385 396; Feigenbaum andSimon, "A Theory of the Serial Position Effect," British Journal of Psychology, 53(1962):307 320;
E. A. Feigenbaum, "An Information-Processing Theory of Verbal Learning," unpublished doctoral
dissertation, Pittsburgh: Carnegie Institute of Technology, 1959; and references cited therein. All
these papers save the last are reprinted in Models of Thought, vol. 1
9.

Herman Ebbinghaus, Memory (New York: Dover Publications, 1964), translated from the German
edition of 1885, especially pp. 35 36, 40, 51.

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ample, in Carl Hovland's chapter in S. S. Stevens's Handbook, we find again(after we calculate them, for trials are reported instead of times) times in theneighborhood of ten or fifteen seconds for college sophomores to fixate nonsense
syllables of low meaningfulness by the serial anticipation method. When the
drum speed increases (say from four seconds per syllable to two seconds per
syllable), the number of trials to criterion increase proportionately, but the total
learning time remains essentially constant.
There is a great deal of gold in these hills. If past nonsense-syllable experiments
are re-examined from this point of view, many are revealed where the basic
learning parameter is in the neighborhood of fifteen seconds per syllable. You
can make the calculation yourself from the experiments reported, for example in
J. A. McGeoch's Psychology of Human Learning. B. R. Bugelski, however,seems to have been the first to make this parameter constancy a matter of publicrecord and to have run experiments with the direct aim of establishing it.10
I have tried not to exaggerate how constant is "constant." On the other hand,efforts to purify the parameter measurement have hardly begun. We do know
about several variables that have a major effect on the value, and we have atheoretical explanation of these effects that thus far has held up well.
We know that meaningfulness is a variable of great importance. Nonsense
syllables of high association value and unrelated one-syllable words are learnedin about one-third the time required for nonsense syllables of low association
value. Continuous prose is learned in about one-third the time per word required
for sequences of unrelated words. (We can get the latter figure also from
Ebbinghaus' experiments in memorizing Doll Juan. The times per symbol areroughly 10 percent of the corresponding times for nonsense syllables.)
We know that similarity particularly similarity among stimuli has an effect on
the fixation parameter somewhat less than the effect of meaningfulness, and we
can also estimate its magnitude on theoretical grounds.
10.

B. R. Bugelski, "Presentation Time, Total Time, and Mediation in Paired-Associate Learning,"
Journal of Experimental Psychology, 63(1962):409 412.

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The theory that has been most successful in explaining these and otherphenomena reported in the literature on rote verbal learning is an informationprocessing theory, programmed as a computer simulation of human behavior,dubbed EPAM.11 Since EPAM has been reported at length in the literature, I shallnot discuss it here, except for one point that is relevant to our analysis. The
EPAM theory gives us a basis for understanding what a "chunk" is. A chunk is a
maximal familiar substructure of the stimulus. Thus a nonsense syllable like
"QUV" consists of the chunks "Q," "U," "V,''; but the word "CAT" consists of asingle chunk, since it is a highly familiar unit. EPAM postulates constancy in thetime required to fixate a chunk. Empirically the constant appears to be about
eight seconds per chunk, or perhaps a little more. Virtually all the quantitative
predictions that EPAM makes about the effects of meaningfulness, familiarity,and similarity upon learning speed follow from this conception of the chunk andof the constancy of the time required to fixate a single chunk.
In fixation of new information, EPAM first adds new branches to its
discrimination net then adds information to images at terminal nodes of the
branches. There is growing evidence that the eight seconds for fixation in longterm memory is required only for expanding the net, and that information can be
added in a second or two to locations (variable-places) in images that are alreadypresent in an expert's long-term memory. Such images are called retrieval
structures or templates. We will return to this point in discussing expert memory.
EPAM's architecture and memory processes are described in H. B. Richman, J. J.
Staszewski and H. A. Simon, "Simulation of Expert Memory Using EPAM IV,"
Psychological Review, 102 (1995):305 330.
The Parameters of Memory Seven Chunks, or Is It Two?
The second limiting property of the inner system that shows up again and again
in learning and problem-solving experiments is the amount of
11.

For a survey of the range of phenomena for which EPAM has been tested, see E. A. Feigenbaumand H. A. Simon, "EPAM-like Models of Recognition and Learning" Cognitive Science, 8(1984):
305 336, reprinted in Models of Thought, vol. 2 (1989), chapter 3.4.

Page 67

information that can be held in short-term memory. Here again the relevant unit
appears to be the chunk, where this term has exactly the same meaning as in thedefinition of the fixation constant.
Attention was attracted to this parameter, known previously from digit span,
numerosity-judging, and discrimination tasks, by George Miller's justly
celebrated paper on "The Magical Number Seven, Plus or Minus Two."12 It is nolonger as plausible as it was when he wrote his paper that a single parameter isinvolved in the three kinds of task, rather than three different parameters: weshall consider here only tasks of the digit-span variety. Today we would express
the parameter as the amount of information that can be rehearsed in about two
seconds, which is, in fact, about seven syllables or short words.
The facts that appear to emerge from recent experiments on short-term memory
are these. If asked to read a string of digits or letters and simply to repeat themback, a subject can generally perform correctly on strings up to seven or even ten
items in length. If almost any other task, however simple, is interposed between
the subject's hearing the items and repeating them, the number retained drops totwo. From their familiarity in daily life we could dub these numbers the"telephone directory constants." We can generally retain seven numbers fromdirectory to phone if we are not interrupted in any way not even by our own
thoughts.
Where experiments appear to show that more than two chunks are retained
across an interruption, the phenomena can almost always be explained
parsimoniously by mechanisms we have already discussed in the previous
section. In some of these experiments the explanation as already pointed out byMilleris that the subject recodes the stimulus into a smaller number of chunks
before storing it in short-term memory. If ten items can be recoded as two
chunks, then ten items can be retained. In the other experiments where "toomuch" appears to be retained in short-term memory, the times allowed thesubjects permit them in fact to fixate the excess of items in long-term memory.
For experts who have acquired retrieval structures or templates in their domainof expertise into which the new information can be inserted, these times can bequite short a second or two per item.
12.

Psychological Review, 63(1956):81 97.

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Putting aside expert performance for the moment, I shall cite just two examples
from the literature. N. C. Waugh and D. A. Norman report experiments, theirown and others', that show that only the first two of a sequence of items isretained reliably across interruption, but with some residual retention of theremaining items.13 Computation of the fixation times available to the subjects inthese experiments shows that a transfer rate to long-term memory of one chunkper five seconds would explain most of the residuals. (This explanation isentirely consistent with the theoretical model that Waugh and Norman
themselves propose.)
Roger Shepard has reported that subjects shown a very long sequence ofphotographs mostly landscape scan remember which of these they have seen
(when asked to choose from a large set) with high reliability.14 When we note thatthe task is a recognition task, requiring storage only of differentiating cues, andthat the average time per item was about six seconds, the phenomenon becomesentirely understandable indeed predictable within the framework of the theory
that we are proposing.
The Organization of Memory
I have by no means exhausted the list of experiments I could cite in support of
the fixation parameter and the short-term capacity parameter and in support of
the hypothesis that these parameters are the principal, and almost only,
characteristics of the information-processing system that are revealed, or could
be revealed, by these standard psychological experiments.
This does not imply that there are not other parameters, and that we cannot findexperiments in which they are revealed and from which they can be estimated.
What it does imply is that we should not look for great complexity in the laws
governing human behavior, in situations where the behavior is truly simple andonly its environment is complex.
In our laboratory we have found that mental arithmetic tasks, for instance,
provide a useful environment for teasing out other possible pa13.
14.

N. C. Waugh and D. A. Norman, "Primary Memory," Psychological Review, 72(1965):89 104.

Roger N. Shepard, "Recognition Memory for Words, Sentences, and Pictures," Journal of Verbal
Learning and Verbal Behavior, 6(1957):156 163.

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rameters. Work that Dansereau has carried forward shows that the times required
for elementary arithmetic operations and for fixation of intermediate results
account for only part perhaps one-half of the total time for performing mental
multiplications of four digits by two. Much of the remaining time appears to be
devoted to retrieving numbers from the memory where they have been
temporarily fixated, and "placing" them in position in short-term memory where
they can be operated upon.15
Stimulus Chunking
I should like now to point to another kind of characteristic of the inner system
more "structural" and also less quantitative that is revealed in certain
experiments. Memory is generally conceived to be organized in an "associative"
fashion, but it is less clear just what that term is supposed to mean. One thing itmeans is revealed by McLean and Gregg. They gave subjects lists to learn
specifically 24 letters of the alphabet in scrambled order. They encouraged, or
induced, chunking of the lists by presenting the letters either one at a time, or
three, four, six, or eight on a single card. In all of the grouped conditions,subjects learned in about half the time required in the one-at-a-time condition.16
McLean and Gregg also sought to ascertain whether the learned sequence wasstored in memory as a single long list or as a hierarchized list of chunks, each ofwhich was a shorter list. They determined this by measuring how subjects
grouped items temporally when they recited the list, and especially when they
recited it backwards. The results were clear: the alphabets were stored as
sequences of short sub sequences; the sub sequences tended to correspond to
chunks presented by the experimenter, or sub lengths of those chunks; left to his
own devices, the subject tended to prefer chunks of three or four letters. (Recall
the role of chunks of this length in the experiments on effects of meaningfulness
in rote learning.)
15.

See Donald F. Dansereau and Lee W. Gregg, "An Information Processing Analysis of Mental
Multiplication," Psychonomic Science, 6(1966):71 72. The parameters of memory are discussed in
more detail in Models of Thought, vol. 1, chapters 2.2, and 2.3; and vol. 2, chapter 2.4; and inRichman, Staszewski and Simon, op. cit.
16.

R. S. McLean and L. W. Gregg, "Effects of Induced Chunking on Temporal Aspects of Serial
Recitation," Journal of Experimental Psychology, 74(1967): 455 459.

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Visual Memory
The materials in the McLean-Gregg experiments were strings of symbols. We
might raise similar questions regarding the form of storage of information about
two-dimensional visual stimuli.17 In what sense do memory and thinking
represent the visual characteristics of stimuli? I do not wish to revive the debate
on "imageless thought "certainly not in the original form that debate took. But
perhaps the issue can now be made more operational than it was at the turn of the
century.
As I enter into this dangerous ground, I am comforted by the thought that eventhe most fervent opponents of mentalism have preceded me. I quote, forexample, from B. F. Skinner's Science and Human Behavior (1952, p. 266):
A man may see or hear "stimuli which are not present" on the pattern of the conditioned reflexes: hemay see X, not only when X is present, but when any stimulus which has frequently accompanied X
is present. The dinner bell not only makes our mouth water, it makes us see food.

I do not know exactly what Professor Skinner meant by "seeing food," but his
statement gives me courage to say what an information-processing theory mightmean by it. I shall describe in a simplified form one kind of experiment that has
been used to throw light on the question. Suppose we allow a subject tomemorize the following visual stimulus a magic square:
4 9 2
3 5 7
8 1 6
Now we remove the stimulus and ask the subject a series of questions about it,
timing his or her answers. What numeral lies to the right of 3, to the right of 1?
What numeral lies just below 5? What numeral is diagonally above and to theright of 3? The questions are not all of the same difficulty in fact I have arranged
them in order of increasing difficulty
17.

The letters in the stimuli of the McLean-Gregg experiment are, of course, also two-dimensional
visual stimuli. Since they are familiar chunks, however, and can be immediately recognized andrecoded, there is no reason to suppose that their two-dimensional character plays any role in thesubject's behavior in the experiment. Again this is "obvious" but only if we already have a general
theory of how stimuli are processed "inside."Page 71

and would expect a subject to take substantially longer to answer the last
question than the first.
Why should this be? If the image stored in memory were isomorphic to a
photograph of the stimulus, we should expect no large differences in the times
required to answer the different questions. We must conclude that the stored
image is organized quite differently from a photograph. An alternative
hypothesis is that it is a list structure a hypothesis that is consistent, for example,
with the data from the McLean-Gregg experiment and that is much in the spiritof information-processing models of cognition.
For example, if what was stored were a list of lists: "TOP," "MIDDLE,""BOTTOM," where "TOP" is 4-9-2, ''MIDDLE" is 3-5-7, and "BOTTOM" is 81-6; the empirical results would be easy to understand. The question "Whatnumeral lies to the right of 3?" is answered by searching down lists. The question"What numeral lies just below 5?" is answered, on the other hand, by matching
two lists, item by item a far more complex process than the previous one.
There is no doubt, of course, that a subject could learn the up-down relations or
the diagonal relations as well as the left-right relations. An EPAM-like theory
would predict that it would take the subject about twice as long to learn both leftright and up-down relations as the former alone. This hypothesis can be easily
tested, but, to the best of my knowledge, it has not been.
Evidence about the nature of the storage of "visual" images, pointing in the same
direction as the example I have just given, is provided by the well-known
experiments of A. de Groot and others on chess perception.18 De Groot put chesspositions taken from actual games before subjects for, say, five seconds; then heremoved the positions and asked the subjects to reconstruct them. Chessgrandmasters and masters could reconstruct the positions (with perhaps 20 to 24
pieces on the board) almost without error, while duffers were able to locate
hardly any of the pieces correctly, and the performance of players of intermediateskill fell somewhere18.

Adriaan D. de Groot, "Perception and Memory versus Thought: Some Old Ideas and Recent
Findings," in B. Kleinmuntz (ed.), Problem Solving (New York: Wiles; 1966), pp. 19 50. See also
the work by Chase and Simon reported in chapters 6.4 and 6.5 of Models of Thought vol. 1.

Page 72

between masters and duffers. But the remarkable fact was that, when masters andgrandmasters were shown other chessboards with the same numbers of pieces
arranged at random, their abilities to reconstruct the boards were only
marginally better than the duffers' with the boards from actual games, while theduffers performed as well or poorly as they had before.
What conclusion shall we draw from the experiment? The data are inconsistent
with the hypothesis that the chess masters have some special gift of visual
imagery or else why the deterioration of their performance? What the datasuggest strongly is that the information about the board is stored in the form of
relations among the pieces, rather than a "television scan" of the 64 squares. It isinconsistent with the parameters proposed earlier seven chunks in short-term
memory and five seconds to fixate a chunk to suppose that anyone, even a
grandmaster, can store 64 pieces of information (or 24) in ten seconds. It is quiteplausible that he can store (in short-term and long-term memory) information
about enough relations (supposing each one to be a familiar chunk) to permit him
to reproduce the board of figure 4:
1. Black has castled on the K's side, with a fianchettoed K's bishop defending the K's Knight.
2. White has castled on the Q's side, with his Queen standing just before his King.
3. A Black pawn on his K5 and a White pawn on his Q5 are attacked and defended by their respective K's
and Q's Knights, the White Queen also attacking the Black pawn on the diagonal.
4. White's Q-Bishop attacks the Knight from KN5.
5. The Black Queen attacks the White K's position from her QN3.
6. A Black pawn stands on its QB4.
7. A White pawn on K3 blocks that advance of the opposing Black pawn.
8. Each side has lost a pawn and a Knight.
9. White's K-Bishop stands on K2.

Pieces not mentioned are assumed to be in their starting positions. Since some of
the relations as listed are complex, I shall have to provide reasons forconsidering them unitary "chunks." I think most strong chess players would
regard them as such. Incidentally I wrote down these relations from my own
memory of the position, in the order in which they

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Figure 4
Chess position used in memory experimentoccurred to me. Eye-movement data for an expert chess player looking at thisposition tend to support this analysis of how the relations are analyzed andstored.19 The eye-movement data exhibit with especial clarity the relations 3 and
5.
The expert can store the information about the position even more rapidly if heor she recognizes the standard opening to which it belongs in this case, theGruenfeld Defense there by accessing a familiar template that gives the positionsof about a dozen pieces.
The implication of this discussion of visual memory for my main theme is thatmany of the phenomena of visualization do not depend in any
19.

O. K. Tikhomirov and E. D. Poznyanskaya, "An Investigation of Visual Search as a Means ofAnalysing Heuristics," English translation from Voprosy psikhologii, 1966, vol. 12, in Soviet
Psychology, 2(Winter 1966 1967):3 15. See also Models of Thought, vol. 1, chapters 6.2 and 6.3.

Page 74

detailed way upon underlying neurology but can be explained and predicted on
the basis of quite general and abstract features of the organization of memory
features which are essentially the same ones that were postulated in order tobuild information-processing theories of rote learning and of concept attainment
phenomena.
Specifically, we are led to the hypothesis that memory is an organization of list
structures (lists whose components can also be lists), which include descriptive
components (two-termed relations) and short (three-element or four-element)
component lists. A memory with this form of organization appears to have theright properties to explain storage phenomena in both visual and auditorymodalities, and of pictorial and diagrammatic as well as propositional (verbal
and mathematical) information.
The Mind's Eye
The experiments we have been discussing relate not only to visual long-term
memory, but also to the Mind's Eye, the short-term memory where we hold andprocess mental images. In the mind's eye we can often substitute "seeing" forreasoning. Consider the economist's common supply-and-demand diagram,which shows, by one curve, the quantity of a commodity that will be supplied tothe market at each price, and by another curve, the quantity that will be
demanded at each price. If we notice that the two curves intersect, we caninterpret the intersection as the point at which the supply and demand quantities
are equal, a point of market equilibrium; and we can read off directly from the xaxis and y-axis of the diagram the equilibrium quantity and price (the x and ycoordinates of the intersection). All this processing goes on in the mind's eye,using the information read from the diagram.
Alternatively, we could write down the equations for the two lines and solve
them simultaneously to find the same equilibrium quantity and price. Usingvisual processes and algebraic ones we attain the same knowledge, but bycompletely different computational paths (and perhaps with vastly different
amounts of labor and insight). In many scientific fields, inferences are made witha combination of verbal, mathematical and diagrammatic reasoning certain
inferences being reached more easily in one form, others in another. In Alfred
Marshall's famousPage 75

text book, Principles of Economics, the text is wholly verbal, the diagrams areprovided in footnotes, and the corresponding algebra is given in a mathematical
appendix, thus allowing readers full freedom to adopt their preferred
representation in each instance.
To understand the interplay of these and other modes of human inference, weneed to study the computational processes required to reach conclusions in each
representation. Currently, this is a very active area of cognitive research.20
Processing Natural Language
A theory of human thinking cannot and should not avoid reference to that mostcharacteristic cognitive skill of human beings the use of language. How does
language fit into the general picture of cognitive processes that I have beensketching and into my general thesis that psychology is a science of the artificial?
Historically the modern theory of transformational linguistics and theinformation-processing theory of cognition were born in the same matrix thematrix of ideas produced by the development of the modern digital computer,and in the realization that, though the computer was embodied in hardware, itssoul was a program. One of the initial professional papers on transformational
linguistics and one of the initial professional papers on information-processing
psychology were presented, the one after the other, at a meeting at MIT in
September 1956.21 Thus the two bodies of theory have had cordial relations froman early date, and quite rightly, for they rest conceptually on the same view ofthe human mind.
20.

J. Larkin and H. A. Simon, "Why a Diagram is (Sometimes) Worth 10,000 Words," Cognitive
Science, 11(1987):65 100; A. M. Leonardo, H. J. M. Tabachneck and H. A. Simon, "A
Computational Model of Diagram Reading and Reasoning" Proceedings of the 17th Annual
Conference of the Cognitive Science Society (1995); Y. Qin and H. A. Simon, "Imagery and Mental
Models of Problem Solving," in J. Glasgow, N. H. Narayanan and B. Chandrasekaran (eds.),
Diagrammatic Reasoning: Computational and Cognitive Perspectives (Menlo Park, CA: AAAI/The
MIT Press, 1995).
21.

N. Chomsky, "Three Models for the Description of Language," and A. Newell and H. A. Simon, "The
Logic Theory Machine," both in IRE Transactions on Information Theory, IT-2, no. 3 (September
1956).

Page 76

Now some may object that this is not correct and that they rest on almost
diametrically opposed views of the human mind. For I have stressed the artificial
character of human thinking how it adapts itself, through individual learning andsocial transmission of knowledge, to the requirements of the task environment.
The leading exponents of the formal linguistic theories, on the other hand, havetaken what is sometimes called a "nativist" position. They have argued that achild could never acquire any skill so complex as speaking and understanding
language if he did not already have built into him at birth the basic machinery forthe exercise of these skills.
The issue is reminiscent of the debate on language universals on whether there
are some common characteristics shared by all known tongues. We know that thecommonalities among languages are not in any sense specific but that they relate
instead to very broad structural characteristics that all languages seem to share insome manner. Something like the distinction between noun and verb between
object and action or relation appears to be present in all human languages. All
languages appear to have the boxes-within-boxes character called phrase
structure. All languages appear to derive certain strings from others bytransformation.22
Now if we accept these as typical of the universals to which the nativist
argument appeals, there are still at least two different possible interpretations ofthat argument. The one is that the language competence is purely linguistic, that
language is sui generis, and that the human faculties it calls upon are not all
employed also in other performances.
An alternative interpretation of the nativist position is that producing utterances
and understanding the utterances of others depend on some characteristics of thehuman central nervous system which are common in all languages but alsoessential to other aspects of human thinking besides speech and listening.
The former interpretation does not, but the latter does, provide an explanation for
the remarkable parallelism holding between the underlying
22.

On language universals see Joseph H. Greenberg (ed.), Universals of Language (Cambridge: The
MIT Press, 1963), particularly Greenberg's own chapter, pp. 58 90. On the "nativist" position, seeJerrold J. Katz, The Philosophy of Language (New York: Harper and Row, 1966), pp. 240 282.

Page 77

assumptions about human capabilities embedded in modern linguistic theory andthe assumptions embedded in information-processing theories of humanthinking. The kinds of assumptions that I made earlier about the structure ofhuman memory are just the kinds of assumptions one would want to make for aprocessing system capable of handling language. Indeed there has been extensive
borrowing back and forth between the two fields. Both postulate hierarchicallyorganized list structures as a basic principle of memory organization. Both areconcerned with how a serially operating processor can convert strings of symbols
into list structures or list structures into strings. In both fields the same generalclasses of computer-programming languages have proved convenient formodeling and simulating the phenomena.
Semantics in Language Processing
Let me suggest one way in which the relation between linguistic theories andinformation-processing theories of thinking is going to be even closer in the
future than it was in the past. Linguistic theory has thus far been largely a theoryof syntax, of grammar. In practical application to such tasks as automatictranslation, it has encountered difficulties when translation depended on morethan syntactic cues when it depended on context and meaning. It seems pretty
clear that one of the major directions that progress in linguistics will have to takeis toward development of an adequate semantics to complement syntax.
The theory of thinking I have been outlining can already provide an importantpart of such a semantic component. The principles of memory organization I
have described can be used as a basis for discussing the internal representation ofboth linguistic strings and two-dimensional visual stimuli, or other non-linguistic
stimuli. Given these comparable bases for the organization of the several kinds ofstimuli, it becomes easier to conceptualize the cooperation of syntactic andsemantic cues in the interpretation of language.
Several research projects have been carried out at Carnegie Mellon Universitythat bear on this point. I should like to mention just two of these, which illustrate
how this approach might be used to explain the resolution of syntactic
ambiguities by use of semantic cues.

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L. Stephen Coles, in a dissertation completed in 1967, described a computer
program that uses pictures on a cathode ray tube to resolve syntactic
ambiguities.23 I shall paraphrase his procedure with an example that is easier tovisualize than any he actually used. Consider the sentence:
I saw the man on the hill with the telescope.
This sentence has at least three acceptable interpretations; a linguist could, no
doubt, discover others. Which of the three obvious ones we pick depends on
where we think the telescope is: Do I have it? Does the man on the hill have it?
Or is it simply on the hill, not in his hands?
Now suppose that the sentence is accompanied by figure 5. The issue is nolonger in doubt. Clearly it is I who have the telescope.
Coles's program is capable of recognizing objects in a picture and relations
among objects; and it is capable of representing the picture as a list structure,
which, in the example before us, we might describe thus:
SAW ((I, WITH (telescope)), (man, ON (hill))).
I have not tried to reproduce the actual details of the scheme he used, but I have
simply shown that a picture, so represented, could readily be matched against
alternate parsings of a verbal string and thus used to resolve the ambiguity of thelatter.
Another program, completed by Laurent Siklóssy, illustrates how semantic
information can aid in the acquisition of a language.24 The reader may be familiar
with the "Language through Pictures" books developed by Professor I. A.
Richards and his associates. These books have been prepared for a large numberof languages. On each page is a picture and beneath it one or more sentences thatsay something about the picture in the language to be learned. The sequence ofpictures and accompanying sentences is arranged to proceed from very simple
situations ("I am here," "That is a man") to more complex ones (''The book is on
the shelf").
Siklóssy's program takes as its input an analogue to one of the "Language
through Pictures" books. The picture is assumed to have already23.

L. Stephen Coles, Syntax Directed Interpretation of Natural Language, doctoral dissertation,
Carnegie Institute of Technology, 1967. A slightly abridged version is reprinted in H.A. Simon and
L. Siklóssy (eds.), Representation and Meaning (Englewood Cliffs, N.J.: Prentice-Hall, 1972).
24.

Also reprinted in Representation and Meaning.

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Figure 5
A syntactically ambiguous sentence;"I saw the man on the hill with the telescope"

been transformed into a list structure (not unlike the one illustrated earlier for
Coles's system) as its internal representation. The program's task is to learn,
when confronted with such a picture, to utter the appropriate sentence in thenatural language it is learning a sentence that says what the picture shows. In thecase of the sentence about the telescope (somewhat more complicated than any
on which the scheme has actually been tested), one would hope that the program
would respond to the picture with "I saw the man on the hill with the telescope,"
if it were learning English, or Ich habe den Mann auf dem Berg mit dem
Fernglas gesehen, if it were learning German.
Of course the program could respond correctly only if it had learned earlier, inthe context of other sentences, the lexical and syntactical components required
for the translation. A child trying to understand the sentence must meet the samerequirement. In other cases the program would use the sentence associated withthe picture to add to its vocabulary and syntax.25
25.

I may mention in passing that Siklóssy's system refutes John Searle's notorious "Chinese RoomParadox," which purports to prove that a computer cannot understand language. As Siklóssy's
program shows, if the room has windows on the world (which Searle's room doesn't) the system
matches words, phrases and sentences to their meanings by comparing sentences with the scenes
they denote.

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I do not wish to expand some pioneering experiments into a comprehensive
theory of semantics. The point of these examples is that they show that the kindof memory structure that has been postulated, for other reasons, to explain
human behavior in simpler cognitive tasks is suitable for explaining how
linguistic strings might be represented internally, how other kinds of stimuli
might be similarly represented, and how the communalities in representation theuse of hierarchically organized list structures for both may explain how languageand "meanings" come together in the human head.
There is no contradiction, then, between the thesis that a human being possesses,
at birth, a competence for acquiring and using language and the thesis that
language is the most artificial, hence also the most human of all humanconstructions. The former thesis is an assertion that there is an inner environment
and that it does place limits on the kinds of information processing of which theorganism is capable. The structure of language reveals these limits; and theselimits in turn account for such commonality as exists among the Babel of human
tongues.
The latter thesis, of the artificiality of language, is an assertion that the limits on
adaptation, on possible languages, imposed by the inner environment are verybroad limits on organization, not very specific limits on syntax. Moreover,according to the thesis, they are limits imposed not only on language but also on
every other mode of representing internally experience received through stimuli
from outside.
Such a view of the relation of language and thinking puts a new cast on the"Whorfian" hypothesis that stating it in over strong form only the expressible isthinkable. If the view is valid, it would be as correct to say. "Only the thinkable
is expressible "a view that, I suppose, Kant would have found quite congenial.
Conclusion
The thesis with which I began this chapter was the following:
Human beings viewed as behaving systems, are quite simple. The apparent
complexity of our behavior over time is largely a reflection of the complexity ofthe environment in which we find ourselves.

Page 81That hypothesis was based in turn on the thesis of the first chapter: that behavior
is adapted to goals, hence is artificial, hence reveals only those characteristics ofthe behaving system that limit the adaptation.
To illustrate how we have begun to test these theses and at the same time to build
up a theory of the simple principles that underlie human behavior, I have
surveyed some of the evidence from a range of human performances, particularly
those that have been studied in the psychological laboratory.
The behavior of human subjects in solving crypt arithmetic problems, inattaining concepts, in memorizing, in holding information in short-term memory,in processing visual stimuli, and in performing tasks that use natural languages
provides strong support for these theses. The artificiality hence variability ofhuman behavior hardly calls for evidence beyond our observation of everyday
life. The experiments are therefore mostly significant in what they show about
the broad commonalities in organizations of the human information-processing
system as it engages in different tasks.
The evidence is overwhelming that the system is basically serial in its operation:that it can process only a few symbols at a time and that the symbols being
processed must be held in special, limited memory structures whose content canbe changed rapidly. The most striking limits on subjects' capacities to employ
efficient strategies arise from the very small capacity of the short-term memory
structure (seven chunks) and from the relatively long time (eight seconds)
required to transfer a chunk of information from short-term to long-term
memory.
The claim that the human cognitive system is basically serial has beenchallenged in recent years by advocates of neural nets and parallel connectionist
models of the nervous system. I would make the following cautionary
observations. Although there is clearly a lot of parallelism in the sensory organs(especially eyes and ears), after stimuli have been recognized seriality is
enforced by the small capacity of the short-term memory that is employed in thesubsequent stages of processing. There is also a moderate degree of parallelism
in the processing of motor signals, but again, only after the initial signals havepassed through the STM bottleneck. Third, seriality of processing at the symboliclevel, the level with which we are concerned here, says nothing, one way or theother, about

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the extent of seriality or parallelism in the neural implementation of the symbolic
processing at the next level below. (By an ironic reverse twist, parallel
connectionist networks are routinely simulated by programs run on serial
computers of standard von Neumann architecture.)
Finally, a large part of the discernible parallel neural activity in the brain may
well consist only in passive maintenance of memory, the active processes being
largely localized and serial. (Evidence now coming from magnetic resonanceimaging [MRI] of the brain is consistent with this view.) The speeds at whichpeople can perform cognitive tasks and the usual limits on the numbers of tasks
they can perform concurrently do not provide much evidence for (or need for)parallel processing capacity. Until connectionism has demonstrated, which it has
not yet done, that complex thinking and problem-solving processes can bemodeled as well with parallel connectionist architectures as they have been with
serial architectures, and that the experimentally observed limits on concurrent
cognitive activity can be represented in the connectionist models, the case formassive parallelism outside the sensory functions remains dubious.
When we turn from tasks that exercise mainly the short-term memory and serialprocessing capabilities of the central nervous system to tasks that involve
retrieval of stored information, we encounter new limits of adaptation, andthrough these limits we acquire new information about the organization of mind
and brain. Studies of visual perception and of tasks requiring use of natural
language show with growing clarity that memory is indeed organized inassociative fashion, but that the "associations" have the properties of what, in thecomputer trade, are usually called "list structures." I have indicated briefly what
those properties are, and more will be said about them in the next chapter.
These are the sorts of generalizations about human thinking that are emerging
from the experimental evidence. They are simple things, just as our hypothesis
led us to expect. Moreover, though the picture will continue to be enlarged andclarified, we should not expect it to become essentially more complex. Only
human pride argues that the apparent intricacies of our path stem from a quite
different source than the intricacy of the ant's path.
One of the curious consequences of my approach of my thesis is that I have said
almost nothing about physiology. But the mind is usually

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thought to be located in the brain. I have discussed the organization of the mind
without saying anything about the structure of the brain.
The main reason for this disembodiment of mind is of course the thesis that I
have just been discussing. The difference between the hardware of a computer
and the "hardware" of the brain has not prevented computers from simulating awide spectrum of kinds of human thinking just because both computer and brain,when engaged in thought, are adaptive systems, seeking to mold themselves tothe shape of the task environment.
It would be unfortunate if this conclusion were altered to read thatneurophysiology has nothing to contribute to the explanation of human behavior.
That would be of course a ridiculous doctrine. But our analysis of the artificial
leads us to a particular view of the form that the physiological explanation of
behavior must take. Neurophysiology is the study of the inner environment of theadaptive system called Homo sapiens. It is to physiology that we must turn for an
explanation of the limits of adaptation: Why is short-term memory limited toseven chunks; what is the physiological structure that corresponds to a "chunk";
what goes on during the eight seconds that a chunk is being fixated; how areassociational structures realized in the brain?
As our knowledge increases, the relation between physiological and informationprocessing explanations will become just like the relation between quantummechanical and physiological explanations in biology (or the relation between
solid-state physics and programming explanations in computer science). They
constitute two linked levels of explanation with (in the case before us) the
limiting properties of the inner system showing up at the interface between them.
Finally, we may expect also that, as we link information-processing psychology
to physiology on the inner side, we shall also be linking psychology to thegeneral theory of search through large combinatorial spaces on the outer side theside of the task environment. But that is the topic of my fifth chapter, for thetheory of design is that general theory of search. Before we take up that topic wemust say more about how the large bodies of information used by designers are
stored in the human mind and accessed.

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Page 85

4
Remembering and Learning: Memory As Environment for Thought
In developing the proposition in chapter 3 that human thought processes are
simple, the cards were perhaps stacked by the examples selected to illustrate thethesis. A task like DONALD + GERALD = ROBERT is difficult enough for an
intelligent adult, but it does not call on much information stored in one's
memory. The solver must know the numbers, how to add and subtract them, andperhaps a few facts about parity, but that is about all. Contrast this with the taskof driving a taxi in Pittsburgh or in the East Bay. No amount of intelligence willtake the cab driver from here to there unless he has stored in memory an
enormous amount of information about the names of streets, their locations, andmutual intersections. (The street index of my Pittsburgh atlas contains about
8,500 entries.) If this information is available in memory, however, choosing aroute probably does not call for a very complex strategy.1
The hypothesis that human thought processes are simple emerged from theinformation-processing research of the 1950s and 1960s. Most of that research
employed puzzle like tasks, similar to the crypt arithmetic problems and concept
attainment tasks discussed in the last chapter, which could be performed without
great dependence on memory or skills previously learned. Additional examples
are the Missionaries and Cannibals puzzle, the Tower of Hanoi puzzle, andproblems of logical inference, all of which have been studied extensively in thepsychological laboratory
1.

I believe this is true, but it is not obvious. Exercise for the reader: write a computer program that,given the street map of an area and some knowledge of which streets are trunk routes, will choose areasonable path to deliver a passenger from one point to another.

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and support the picture of human thinking that was drawn in the last chapter.
It is reasonable that research on human thinking should begin with relatively
content less tasks of these kinds but not that it should end there. And so in recent
decades research in both cognitive psychology and artificial intelligence has beenturning more and more to semantically rich domains domains that havesubstantial, meaningful content, where skillful performance calls upon large
amounts of specialized knowledge retrieved from memory. Does human thinking
still look simple in such domains?
In pursuing this question, we shall be interested especially in high-level
performance of the kinds of tasks that confront professionals in their everyday
work or college students who are preparing for professional practice. Among theprofessional-level domains that have been studied fairly extensively in thelaboratory, and hence some of whose parameters are known, are chess playing,making medical diagnoses, solving college physics problems, and discovering
regularities in empirical data. We will use these and others as examples.
Except for chess playing, long-term memory played only a modest role inperformance of the tasks examined in chapter 3. The simplicity we discovered
there was largely a simplicity of process (only a few basic symbol manipulatingprocesses had to be postulated to account for the behavior) and a simplicity of
the architecture of the mind (its seriality and its limited short-term memory). A
few parameters, especially the chunk capacity of STM and the storage time for
new chunks in LTM, played a dominant role in fixing the limits of the system's
performance.
As we move to semantically rich domains, new questions of simplicity andcomplexity arise. Does the richness of the contents of long-term memory imply
complexity of structure, or can that richness be accommodated by the simple
organizations of list structures that were described briefly in chapter 3? Is ahigher level of complexity required for programs that exploit these large stores ofmemory, or are the same processes in evidence as those that account for problem
solving in the puzzle like tasks of chapter 3? Do the learning programs required
to store new data and processes in long-term memory introduce new levels ofcomplexity? We will see that the evidence from studies of human performanceand fromPage 87

its simulation by computer generally supports the hypothesis of simplicity. Morememory does not necessarily mean more complexity.
Semantically Rich Domains
There is a certain arbitrariness in drawing the boundary between inner and outerenvironments of artificial systems. In our discussion of economic behavior inchapter 2, we might well have considered the business firm's cost function to bepart of the inner environment. Instead we abstracted the decision-making processfrom the production technology and regarded only the limits on rational
calculation as inner constraints on adaptivity. The cost function was treated,along with the demand function, as part of the outer environment to which thefirm was seeking to adapt.
We can adopt a similar viewpoint toward the human problem solver, whose basic
tool for solving problems is a small repertory of information processes of thesorts described in the last chapter. This processor operates on an outerenvironment that has two major components: the "real world," sensed througheye, ear, and touch, and acted upon by leg, hand, and tongue, and a large store of(correct and incorrect) information about that world, held in long-term memory
and retrievable by recognition or by association. When the processor is solving
puzzle like problems, the memory plays a limited role. The structure of the
problem rather than the organization of memory steers the problem-solving
search. When it is solving problems in semantically rich domains, a large part ofthe problem-solving search takes place in long-term memory and is guided byinformation discovered in that memory. Hence an explanation of problem
solving in such domains must rest on an adequate theory of memory.
Long-Term Memory
Certain facts about human long-term memory (LTM) were set forth in the lastchapter. It is of essentially unlimited size no one appears ever to have been able
to fill his memory to overflowing, although in senility new items cannot be
stored. About eight seconds are required to store a new chunk in LTM, except
when an expert has an already stored template with which the chunk can beassociated, in which case only a second orPage 88

two is required."2 A rather shorter time (a few hundred milliseconds to a coupleof seconds) is needed to retrieve information previously stored. The memory isusually described as "associative" because of the way in which one thought
retrieved from it leads to another. Information is stored in linked list structures.
In terms of our present-day knowledge of LTM, we can extend this description abit. We can think of the memory as a large encyclopedia or library, theinformation stored by topics (nodes), liberally cross-referenced (associational
links), and with an elaborate index (recognition capability) that gives directaccess through multiple entries to the topics. Long-term memory operates like asecond environment, parallel to the environment sensed through eyes and ears,
through which the problem solver can search and to whose contents he canrespond.
Medical diagnosis is a semantically rich domain that has now been investigatedextensively, with the aims both of understanding the diagnostic processes usedby physicians and of building systems for diagnosis by computer. The thicknessof medical textbooks and reference books attests to the large volume of
information that is required for accurate diagnosis. When the diagnostic
strategies of physicians are studied, two kinds of processes are prominent in theirthinking-aloud protocols: processes of direct recognition, where presence of asymptom leads almost immediately to hypothesizing a disease that might be its
cause, and processes of search quite like those identified in the simpler problemsolving tasks that were described in chapter 3.3 The diagnosis generally proceedsfrom symptoms to hypothesized disease entities, to tests for resolving doubts andweeding out alternatives, to new symptoms, and so on. Thus the search is
conducted alternately in each of two environments: the physician's mental libraryof medical knowledge and the patient's body. Information gleaned from one
environment is used to guide the next step of search in the other.
2.
3.

But see the discussion of retrieval structures in chapter 3.

Arthur Elstein et al., Medical Problem Solving (Cambridge, Mass.: Harvard University Press, 1978).
There is now on the market a fully automated diagnostic system for internal medicine, Doctor'sAssistant, that is based largely on this model of the diagnostic process and that has performed well inclinical trials.

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Intuition
What about the sudden flashes of ''intuition" that sometimes allow the expert toarrive immediately at the answer that the novice can find (if at all) only after
protracted search? (I put "intuition" in quotes to emphasize that it is a label for aprocess, not an explanation of it.) Intuition is a genuine enough phenomenon
which can be explained rather simply: most intuitive leaps are acts ofrecognition. Let me illustrate this with the game of chess.
In chapter 3 I described the remarkable ability of chess masters and grandmasters
to reproduce chess positions almost faultlessly after seeing them for five or ten
seconds. This performance was reconciled with the known limits of short-term
memory by observing that for the chess master a position from a game does notconsist of 25 isolated pieces but of five or six chunks, each one a familiar
configuration that may be a template of a dozen pieces or a smaller chunkconsisting of two to five or more related pieces. Since we can estimate, at least
roughly, the amount of variety in chess positions from well-played games, we
can also estimate the number of familiar chunks that must be stored in themaster's long-term memory to make his performance possible. Several different
methods of estimation all lead to numbers of the general magnitude of 50,000.
We need not take the exact number seriously, but it is interesting that it is of thesame order of magnitude as the natural language recognition vocabulary of acollege-educated reader.4
Hence we can say that one part of the grandmaster's chess skill resides in the
50,000 chunks stored in memory, and in the index (in the form of a structure offeature tests) that allows him to recognize any one of these chunks on the chessboard and to access the information in long-term memory that is associated with
it. The information associated with familiar patterns may include knowledge
about what to do when the pattern is encountered. Thus the experienced chessplayer who recognizes the feature called an open file thinks immediately of thepossibility of moving a rook to that file. The move may or may not be the best
one, but it is one that should be considered whenever an open file is present. The
expert4.

See Simon, Models of Thought, vol. 1, chapters 6.2 and 6.3.

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recognizes not only the situation in which he finds himself, but also what action
might be appropriate for dealing with it.
A feature-testing system capable of discriminating among 50,000 different items
might make its discriminations quite rapidly. Even if each test weredichotomous, which they probably are not, only about 16 tests would have to be
performed to achieve each recognition. (The game of "twenty questions" is basedon the fact that 20 dichotomous tests will discriminate among a million items.) If
each test required 10 milliseconds, the whole process could be performed in lessthan 200 milliseconds well within the time limits of human recognition
capabilities.
When playing a "rapid transit" game, at ten seconds a move, or fifty opponents
simultaneously, going rapidly from one board to the next, a chess master isoperating mostly "intuitively," that is, by recognizing board features and themoves that they suggest. The master will not play as well as in a tournament,
where about three minutes, on the average, can be devoted to each move, butnonetheless will play relatively strong chess. A person's skill may decline from
grandmaster level to the level of a master, or from master to expert, but it will by
no means vanish. Hence recognition capabilities, and the information associatedwith the patterns that can be recognized, constitute a very large component ofchess skill.5
How Much Information?
The amount of information stored by chess masters appears to be roughly
consonant with the amount of information that professionals in other
5.

Strong empirical evidence for this claim will be found in F. Gobet and H. A. Simon, "The Roles of
Recognition Processes and Look-Ahead Search in Time-Constrained Expert Problem Solving:
Evidence from Grandmaster Level Chess," Psychological Science, 7(1)(January 1996): 52 55. Mycolleague Hans Berliner has built a powerful backgammon program, which has beaten the world's
champion human player in a match, using pattern recognition capabilities rather than search
processes as the basis for its skill. (See "Computer Backgammon," Scientific American, 242(6)(June1980):64 85.) By contrast, most extant computer chess programs carry out enormous searches forlack of strong recognition capabilities. For a survey of the current status of computer chess programs
see H. A. Simon and J. Schaeffer, ''The Game of Chess" in R. J. Aumann and S. Hart (eds.),
Handbook of Game Theory, vol. 1, (Netherlands: Elsevier, 1992).

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domains possess although only the roughest measures of these quantities areavailable. At first it might appear unlikely that disciplines as disparate as chess,
medicine, mathematics, and chemistry should call on memory stores ofcomparable size. But their comparability says little about the nature of thedomains. No one knows everything there is to know about chess, medicine,chemistry, or any other serious domain. Here, as elsewhere, man must be themeasure of skill. A professional's knowledge is adequate when he or she knows
about as much as other professionals in the same discipline. What places anupper limit on professional knowledge is the amount of time that can be devoted
to acquiring and maintaining it some fraction of a human waking lifetime.
From what is known about the rates at which people can store new information
in long-term memory, 50,000 chunks is not an unconscionable amount ofknowledge to acquire in a decade, say, of professional training. Of course 50,000
chunks is an underestimate of what the chess master (or other professional)
knows, but even if we raise the estimate by one or two orders of magnitude, thatmuch information could probably be acquired in a decade. If it takes thirty
seconds of attention to store a new chunk in long-term memory (8 seconds for
initial acquisition, say, plus 22 seconds to overlearn for permanent retention),then ten years of intensive study at 1,500 hours per year (about four hours perday) could produce a memory store of 1.8 million chunks. Even a dedicatedprofessional who worked around the clock with a minimum of daydreaming
would be unlikely to learn more than that, for a substantial part of the time would
probably be spent not in learning but in practicing what had already beenlearned.
In a couple of domains where the matter has been studied, we do know that eventhe most talented people require approximately a decade to reach top
professional proficiency. Except for Bobby Fisher and Judit Polgar, who reachedgrandmaster status in nine years and some months from the time they first began
to play chess, there is no record of anyone achieving that level in less than adecade. Unless we except Mozart, there is no record of a composer producing
first-rate music before he had completed a decade of serious study and practice;and even in the case of Mozart the music that he composed between the seventhand tenth years

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after he began writing is notable as Mozart juvenalia rather than the music of a"grandmaster."6
When a domain reaches a point where the knowledge for skillful professional
practice cannot be acquired in a decade, more or less, then several adaptive
developments are likely to occur. Specialization will usually increase (as it has,for example, in medicine), and practitioners will make increasing use of books
and other external reference aids in their work.
Architecture is a good example of a domain where much of the information aprofessional requires is stored in reference works, such as catalogues of available
building materials, equipment, and components, and official building codes. No
architect expects to keep all of this in his head or to design without frequent
resort to these information sources. In fact architecture can almost be taken as aprototype for the process of design in a semantically rich task domain. The
emerging design is itself incorporated in a set of external memory structures:sketches, floor plans, drawings of utility systems, and so on. At each stage in thedesign process, the partial design reflected in these documents serves as a major
stimulus for suggesting to the designer what he should attend to next. This
direction to new sub goals permits in turn new information to be extracted frommemory and reference sources and another step to be taken toward thedevelopment of the design.7 I will have a little more to say about this cycle ofdesign activities, and its implications for style, in the next chapter.
It should not be supposed that every advance in human knowledge increases theamount of information that has to be mastered by professionals. On the contrary,some of the most important progress in science is the discovery and testing of
powerful new theories that allow large numbers of facts to be subsumed under afew general principles. There is a constant competition between the elaborationof knowledge and its compression into more parsimonious form by theories.
Hence it is not safe to say that the professional chemist must learn more today
than a half century ago, before the general laws of quantum mechanics wereannounced.
6.

The information about composing was compiled by my colleague, John R. Hayes (personalcommunication). From preliminary data he has also gathered, it appears that similar statements canbe made about painting.
7.

Ömer Akin, Psychology of Architectural Design (London: Pion Limited, 1986).

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It is probably safe to say that the chemist must know as much as a diligent person
can learn in about a decade of study.
Memory for Processes
Memory has been discussed here as though it consisted mainly of a body of data.
But experts possess skills as well as knowledge. They acquire not only the ability
to recognize situations or to provide information about them; they also acquire
powerful special skills for dealing with situations as they encounter them.
Physicians prescribe and operate as well as diagnose.
The boundary between knowledge and skill is subtle. For example, when we
write a computer program in any language except machine language, we arereally not writing down processes but data structures. These data structures arethen interpreted or compiled into processes that is, into machine-language
instructions that the computer can understand and execute. Nevertheless for most
purposes it is convenient for us simply to ignore the translation step and to treat
the computer programs in higher-level languages as representing processes.
We can think of a medical diagnostic system (human or computer) as having alarge body of medical knowledge, together with a few general processes fordrawing inferences from it. Or we can think of the knowledge as organized inprocesses, instructing the expert how to proceed with the diagnosis, for example:
If you find that the patient has a high fever, then test for the following additional
symptoms.
Similarly, a student's knowledge of geometry could be stored as theorems:
If two triangles have the three pairs of corresponding sides equal, then they arecongruent.
or, alternatively, as condition-action pairs (called productions):
Test the corresponding sides of two triangles for pairwise equality; if all are
equal, store the assertion that the triangles are congruent.
Whether expertness is stored as data or process, or some combination of both,
does not alter what we have said about complexity. The

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specialized knowledge and skill can still be regarded as residing in the external
environment of long-term memory, to be drawn upon by general processes that
control and steer problem-solving search processes like means-ends analysis andrecognition that we have already identified in the simpler task environments
discussed in chapter 3.
Understanding and Representation
Efforts to solve a problem must be preceded by efforts to understand it. Here isan example of a puzzle-like task that most people find reasonably difficult:
A Tea Ceremony
In the inns of certain Himalayan villages is practiced a most civilized and refined tea ceremony. The
ceremony involves a host and exactly two guests, neither more nor less. When his guests havearrived and have seated themselves at his table, the host performs five services for them. These
services are listed below in the (increasing) order of the nobility which the Himalayans attribute tothem.
StokingtheFire
Fanning the Flames
Passing the Rice Cakes
PouringtheTea
Reciting Poetry
During the ceremony, any of those present may ask another, "Honored Sir, may I perform thisonerous task for you?" However, a person may request of another only the least noble of the tasks
the other is performing. Further, if a person is performing any tasks, then he may not request a task
which is nobler than the least noble task he is already performing. Custom requires that by the time
the tea ceremony is over, all the tasks will have been transmitted from the host to the most senior of
the guests. How may this be accomplished?

Before a General Problem Solver (see chapter 5) can go to work on the Tea
Ceremony problem, it has to extract from the written statement a description of
the problem in terms of constructs that a GPS can deal with: symbol structures,
tests for differences between structures, operators that alter structures, andsymbolized goals and tests for their achievement. A GPS understands a problem
when the problem has been presented to it in terms of such entities, so that its
processes for detecting differences, finding relevant operators, applying
operators, and evaluating progress toward the solution can go into action.

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Now the Tea Ceremony problem really has nothing to do with inns in Himalayan
villages. Underneath it is an abstract problem about two classes of objects(participants and tasks), relations between objects (each task is assigned to aparticipant), an ordering of the tasks (by nobility), and operators (transferring atask from one participant to another). Understanding the problem requires
extracting these entities from the natural language text.
A Program That Understands
A computer program, UNDERSTAND, simulates the processes that people use
to generate an internal representation of (to understand) a problem like A Tea
Ceremony.8 UNDERSTAND proceeds in two phases: it parses the sentences ofthe problem instructions, and then constructs the representation from theinformation it has extracted from the parsed sentences.
The task of analysing natural-language sentences has already been discussed in
the last chapter: it involves inferring from the linear string of words the impliedhierarchic structure of phrases and clauses. The UNDERSTAND programaccomplishes this in a quite orthodox way, similar to that of other extant parsing
programs. The second phase (construction) is more interesting. Here, the parsed
sentences are examined to discover what objects and sets of objects are beingreferred to, what properties of objects are mentioned and what are the relations
among them, which of the predicates and relations describe states and which
describe moves, and what the goal state is. UNDERSTAND then proceeds toconstruct a format for representing states and to generate programs for making
legal moves by changing one state into another.
For example, in A Tea Ceremony a state could be represented by a list of thethree participants, each described by a list of the tasks he is performing. Anotherlist could indicate the ordering of the five tasks by nobility. The legal moveprogram would delete a task from the list of a particular participant (the donor)
and add it to the list of another (the8.

UNDERSTAND is described and its behavior discussed in chapters 7.1 to 7.3 of my Models of
Thought, vol. 1. The program and chapters were produced jointly by John R. Hayes and myself.

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donee), after checking to see that the task was not more noble than others on thedonor's or donee's lists.
Since (as was argued in the last chapter) list structures have a quite generalcapacity for representing symbolic information of all kinds, a program like
UNDERSTAND is capable, in principle, of constructing a representation forvirtually any kind of puzzle like problem that does not require real-world
knowledge for its understanding; for any such problem can be described in termsof objects, their relations, and changes in their relations.9
Understanding Physics
In contrast with understanding a problem like A Tea Ceremony, understanding
problems in domains that have rich semantics requires prior knowledge of thedomain. Consider the simple statics problem:
The foot of a ladder rests against a vertical wall and on a horizontal floor. The top of the ladder is
supported from the wall by a horizontal rope 30 ft long. The ladder is 50 ft long, weighs 100 lb with
its center of gravity 20 ft from the foot, and a 150-lb man is 10 ft from the top.
Determine the tension in the rope.

In order to go to work on this problem, a person must know what a coefficient offriction is, that a ladder may be regarded as a lever with a fulcrum and withforces applied to it, that a man may be abstracted to a mass or a fulcrum, andmany facts of a similar sort. What distinguishes this kind of problem from A Tea
Ceremony is not that it has real-world reference, but that it makes reference tomatters that are supposed already to be known.
Gordon Novak has written a very interesting program, ISAAC, that canunderstand physics (statics) problems like the one described above.10 ISAAC isable to do this because it has stored in memory information about levers, masses,
inclined planes, and the like in the form of simple schemas that describe objectsof these kinds and that indicate the kinds of9.

Of course the UNDERSTAND program actually implemented is only a prototype for the engine
that would be needed to accomplish this in all generality.
10.

G. S. Novak, "Representation of Knowledge in a Program for Solving Physics Problems;"
Proceedings of the Fifth International Joint Conference on Artificial Intelligence, 1977, pp. 286 291.

Page 97

information associated with them. A ladder schema, for example, looks
something like this:
LadderType:
ladderLocations: (of foot, top, other points mentioned)
Supports:
Length:Weight:Attachments: (to other objects)
When a problem is presented to ISAAC, it begins, like UNDERSTAND, to parse thesentences of the problem statement. In ISAAC's case, however, more is involved thanidentifying objects and relations and representing them appropriately. Particular kinds ofobjects whose meanings are already known (i.e., provided with schemas in ISAAC's
memory) must be recognized and identified with their schemas, and the "slots" in theschemas must be filled in with the requisite information. A ladder must be recognized as alever, and a copy must be constructed of the lever schema specifying the length of theladder, its weight, its center of gravity, the location of its fulcrum and of the forces
impinging on it, and so on.

Having identified the appropriate object schemas and accumulated theappropriate information about them, ISAAC is then able to assemble theindividual schemas (describing the ladder, the man, the surfaces on which theladder rests) into a composite problem schema. Using the latter schema as guide,
the program then constructs and solves the equations that are appropriate fordescribing the equilibria of forces.
ISAAC is prototypic of systems for understanding problems in semantically richdomains. Knowledge of physics is stored in the program in two ways: in thecomponent schemas, which guide the process of generating a representation of
the problem state (the problem schema), and in the procedures for generating theequations of equilibrium (the laws of statics which correspond to the processes
for creating the operators in a program like UNDERSTAND).
When we compare the two understanding programs, we see that UNDERSTAND
has to create its problem representation and operators out of whole cloth, guided
only by the information in the problem

Page 98

instructions, while ISAAC has to discover a match between the things mentioned
in the problem statement and the schemas and physical laws it has stored inmemory. A more sophisticated understanding system would combine these twocapabilities. One component of the system (corresponding to UNDERSTAND)
would generate state representations when confronted with new problem
domains and would store these as sets of schemas. The other component(corresponding to ISAAC) would endeavor to use representations already stored
to interpret new problems presented to it.
However primitive the existing understanding programs may be, they do provide
a set of basic mechanisms, a theory, to explain how human beings are able tograsp problems, both in new domains about which they have no knowledge andin domains about which they have a greater or lesser amount of previous
semantic knowledge. The two particular systems I have described are membersof a growing family of such programs that explicate the processes ofunderstanding in an ever-widening collection of tasks. Some of these systems areconcerned with relatively ill-defined tasks. For example, there has been someresearch on the processes that may be used for understanding children's tales ornewspaper stories. Unlike the problem-solving tasks discussed here, where it iseasy to test whether the system has understood the problem or not (i.e., whether
it has constructed a representation that the problem solver can use to find an
answer), the tests of whether a story has been "understood" are ambiguous. In thelatter tasks understanding can be achieved to various degrees and various depths.
The understanding programs provide us with additional insights about visual
imagery, a topic discussed in chapter 3. The state descriptions produced by
UNDERSTAND and the element schemas and problem schemas of ISAAC are
excellent examples of the kinds of symbolic structures proposed in that previous
discussion as mental images. As a matter of fact Novak has written a subsidiary
program, as part of ISAAC, that produces from the problem schema it hasconstructed an actual (if simple) drawing of the problem situation that can bedisplayed on a cathode-ray tube.
Size and Simplicity
The problem-understanding programs take information from the world outside(in these cases in the form of natural-language text) and transformPage 99

it into knowledge that is stored in long-term memory as list structures orprocedures. When memory has acquired a photograph, highly fragmentary andoften fogged, of the external world, the problem-solving processes can carry out
some of their work on this internal world instead of the outside one. This isadvantageous whenever it is costly to gain access to the information in theexternal world.
As more knowledge is acquired about more subjects, the memory store grows,
essentially without limit. Regardless of the size reached by the memory,
however, it continues to be constructed of the same basic components, to beorganized and indexed according to the same principles, and to be operated uponby processes having the same basic form. We may say that the system becomes
more complex because it grows in size, or we may say that it remains simple
since its fundamental structure does not change.
We could make exactly the same comments if we were discussing the simplicity
or complexity of the Library of Congress. As the number of books increases
from thousands to millions to tens of millions, the number of miles of shelves
required to hold them increases correspondingly. So does the number of cards inthe catalogue. But in terms of the library's architecture, the growth, however
impressive, can hardly be characterized as a growth in complexity. As I shallargue in chapter 8, the transition from a single-cell to a multi cell organism
represents a step upward in complexity; increase in the weight of a steer or in the
population of a colony of algae does not.
Human beings carry around in their heads knowledge of many domains and often
even expertness in several. To the extent that the domains are distinct, as theyoften are, their multiplicity contributes nothing to the complexity of operating in
any one of them. Studying a Greek textbook in the library is made no moredifficult (and no easier) by the fact that the library also contains books on Latin,
Sanskrit, and classical Chinese.
Human memory is best regarded as an extension (sometimes a large extension)of the environment in which human thought processes take place and not as anincrement in the complexity of these processes. What is remarkable about thewhole architecture is precisely the fact that memory enables the system tooperate effectively in a wide array of different task domains using the same basic
equipment that it employs toPage 100

understand and solve Tea Ceremony problems or simple statics problems inphysics.
Learning
The external environments of thought, both the real world and long-term
memory, undergo continual change. In memory the change is adaptive. It updates
the knowledge about the real world and adds new knowledge. It adds newprocedures that contribute to the skills in particular task domains and improves
existing procedures. A scientific theory of human thinking must take account of
this process of change in the contents of memory.
If the human cognitive system is truly simple, that simplicity can only be
revealed by discovering the invariants that underlie change. Among theseinvariants are the basic parameters of memory (parameters of the innerenvironment) and the general search and control processes described in chapter
3. In addition to these we may look for a basic set of processes that bring about
the adaptation of long-term memory that we call learning. We may hypothesizethat these learning processes are the unmoved movers that can account for thechange processes in a simple and invariant way.
Learning is any change in a system that produces a more or less permanentchange in its capacity for adapting to its environment. Understanding systems,
especially systems capable of understanding problems in new task domains, arelearning systems. So is EPAM, the system described in chapter 3 that simulates
human rote verbal learning. So is Siklóssy's system, also described in thatchapter, for simulating first-language learning.
Any multicomponent system can be improved in a large number of ways. Nor isthere any single kind of change in the human cognitive system to which the term''learning" applies exclusively. However, the multiplicity of forms of learning
need not bewilder us, for they are reducible to a few fundamental species,
corresponding to the main components of the cognitive system.
Along one dimension we can distinguish between acquiring information (stored
data structures) and acquiring skills (stored procedures). The

Page 101

UNDERSTAND program illustrates both. The state descriptions that
UNDERSTAND builds constitute new knowledge, the operators, new skills. To
these categories we can add the learning of new perceptual discriminations, as
exemplified by EPAM. Motor skills, although based partly on the kinds oflearning already enumerated, probably also have additional components.
It is too early in the development of research on learning to attempt an
exhaustive taxonomy of the kinds of learning processes that will be required toaccount for all the sorts of learning of which the human organism is capable, butthere is reason to believe that human learning of most kinds can be explained
within the framework of the symbol-processing system we have been describing.
Learning with Understanding
Every teacher knows that there is a profound difference between a student
learning a lesson by rote and learning it with understanding, or meaningfully.
When something has been learned by rote, it can be regurgitated more or less
literally, but it cannot be used as a cognitive tool. Laboratory experiments haveshown that material can usually be learned more rapidly with understanding thanby rote, is retained over longer periods of time, and can be transfered better to
new tasks.11
In spite of the great pragmatic importance of the distinction between rote andmeaningful learning, the difference between them is not thoroughly understoodin information processing terms. Partly it is a matter of indexing: meaningfulmaterial is indexed in such a way that it can be accessed readily when it isrelevant. Partly it is a matter of redundancy: meaningful material is stored
redundantly, so that if any fraction of it is forgotten, it can be reconstructed fromthe remainder. Partly it is a matter of representation: meaningful material isstored in the form of procedures rather than "passive" data, or if stored as data, it
is represented in such a way that general problem-solving processes and otherprocedures can readily make use of it. All of these are aspects of understanding
and meaningfulness that need further exploration.
11.

4.

George Katona, Organizing and Memorizing (New York: Hafner Publishing Co., 1967), chapter

Page 102

Production Systems
In an information-processing system that consists of data structures andprograms, it has usually been easier to devise methods for adding new schemas
and other data structures to the existing system than to add new programs. In theearly years of AI research, artificial intelligence and simulation programs wereusually organized as hierarchies of routines and subroutines. Modification of aprogram involved modification of one or more subroutines, a task not easily
accomplished.
In the past several decades a new form of program structure has become popular:
the production system.12 What commends it, especially for building systems thatlearn, is the simplicity and uniformity of its structure. A production system is aset of arbitrarily many productions. Each production is a process that consists of
two parts a set of tests or conditions and a set of actions. The actions contained ina production are executed whenever the conditions of that production aresatisfied. In that sense, the productions operate in complete independence of eachother. Productions are usually represented by the notation:
Condition  Action,
which is reminiscent of the familiar SR pairs of stimulus-response psychology.
Although productions are more complex objects than SR pairs, it is sometimes
possible to use the latter as a metaphor for the former.
A system to simulate human cognition might be constructed with productions of
two kinds: those in which the conditions are tests on the contents of short-term
memory and those in which the conditions are perceptual tests on the outside
world. An example of a condition of the former kind might be: "If your goal is toenter the house, open the door." Here, the goal of entering the house would be
represented by a symbol structure in STM, and STM would be tested for thepresence or absence of that structure.
An example of a perceptual production might be: "If the door is locked, use yourkey." Here the condition is tested in the real world (by determining if the door islocked).
12.

See Newell and Simon, Human Problem Solving.

Page 103

A system whose behavior is governed by perceptual productions is sometimes
called stimulus driven or data driven; one governed by goal symbols in STM,
goal driven. A problem solver that is mainly goal driven will give the appearanceof working backward from the desired goal. One that is mainly stimulus driven
will give the appearance of working forward from what it knows toward thedesired goal. Of course goal-directed systems will usually employ bothproductions with perceptual conditions and productions with goals as conditions.
Many cognitive simulations have now been modeled as production systems. But
what makes production systems especially attractive for modeling is that it isrelatively easy to endow them with learning capabilities to build so-called
adaptive production systems. Since production systems are simply sets ofproductions, they can be modified by deleting productions or by inserting new
ones. The consequences of such changes may or may not be adaptive, but at least
there is no question of how the change is to be made.
Learning from Examples
In chapters of science and mathematics textbooks that explain new procedures,
one almost always finds examples that have been worked out in detail, step bystep. In an elementary algebra text, for example, we might find the following:
9X + 17=6X + 23,
3X + 17 = 23 (subtract 6X from both sides),3X = 6 (subtract 17 from both sides),X = 2 (divide both sides by 3).
At each step the algebraic equation is modified and a "justification" given for themodification. The process terminates when an expression is found of the form:<Variable> = <Numeral>
This, and similar, equations could be solved by the following production system:
If expression has the form, <variable> = <real number>  Halt.
If expression has variable term on right side  Subtract variable term from bothsides, and simplify.

Page 104

If expression has numerical term on left side  Subtract numerical term fromboth sides, and simplify.
If variable term has coefficient other than unity  Divide both sides bycoefficient.
Now a clever student who encountered the worked-out example in the text, but
who had not previously acquired a procedure for solving it, could learn one in thefollowing manner. Examining the first two steps in the example, he notices what
action has been performed to transform the first line into the second. He also
compares the pairs of equations and notices that the term "6X" has disappeared
from the right side and the coefficient of X has been changed on the left. By
trying the action, he discovers that it produces exactly this effect. Moreover the
expression from which the "6X" has been removed is closer in form to the final
equation than is the initial expression. He now learns a new production, by taking
the feature of the initial expression that is eliminated as the condition for theaction. This production is the second one in our production system. By
comparing the second and third equations, he similarly infers and acquires thethird production, and by comparing the third and fourth equations, the fourth
production. Presumably he has already acquired the first production, which
represents his understanding of what the ''solution" of an algebraic equation is.
I have omitted from this account some essential details, such as how the student
selects the proper degree of generalization for his productions (why "variable
term" instead of "6X" in the condition and action of the second production?). But
the simplified example conveys the general idea of how an adaptive production
system can acquire new skills. This particular scheme has been devised andprogrammed by David Neves.13
A set of highly effective computer tutoring systems in geometry, algebra, andprogramming (LISP), employing primarily a learning-from-examples paradigm,
has been developed by John Anderson and his colleagues and tested successfullyin high school classrooms. In the People's Republic of China, the Psychology
Institute of the Chinese Acad13.

D. M. Neves, "A Computer Program that Learns Algebraic Procedures by Examining Examples
and Working Problems in a Textbook," Proceedings of the Second National Conference of theCanadian Society for Computational Studies of Intelligence (1978), pp. 191 195.

Page 105

emy of Sciences has developed paper and pencil materials for a full three-year
middle school curriculum in algebra and geometry based upon learning fromexamples rather than lectures or textbook exposition. These materials are now
being used successfully in several hundred schools in China. In both cases, thematerials were developed by analyzing the tasks to determine what productions
the students would need to acquire for effective performance and then what
sequence of examples would induce the learning of these productions. These two
extensive projects, in the U.S. and China, provide strong evidence for the
relevance to human learning of the processes that Neves and others postulated on
the basis of computer simulation.14
The idea of learning from examples can be extended to a method of learning "bydoing." Suppose a problem-solving system is able to solve a particular problem
but does it inefficiently after a great deal of search. The path to a solution finallydiscovered, stripped of all the extraneous branchings in the search, could serve asa worked-out example to which the procedures of the previous paragraphs could
be applied. Anzai and Simon have constructed a "learning by doing" scheme ofthis kind for the Tower of Hanoi puzzle which, by solving the problem several
times in succession, gradually acquires an efficient and general strategy.15
Discovery Processes
No sharp line divides learning things that are already known to others fromlearning things that are new to the world. What constitutes novelty depends on
what knowledge is already in the mind of the problem solver and what help isreceived from the environment in adding to this knowledge. We should expect,
therefore, that processes very similar to those employed in learning systems canbe used to construct systems that discover new knowledge.
14.

J. R. Anderson, A. T. Corbett, K. R. Koedinger and R. Pelletier, "Cognitive Tutors: Lessons
Learned," Journal of the Learning Sciences, 4(1995):167 207; X. Zhu and H. A. Simon, "Learning
Mathematics from Examples and by Doing," Cognition and Instruction, 4(1987):137 166.
15.

Y. Anzai and H. A. Simon, "The Theory of Learning by Doing," Psychological Review, 86(1979):124
140.

Page 106

Problem Solving Without a Goal
Making discoveries belongs to the class of ill-structured problem-solving tasks
that have relatively ill-defined goals. To discover gold, one does not even have to
be looking for it (although frequently one is), and if silver or copper shows up
instead of gold, that outcome will usually be welcome too. The test thatsomething has been discovered is that something new has emerged that could nothave been predicted with certainty and that the new thing has value or interest ofsome kind.
An early discovery program, developed in 1963, performed the letter series
completion task. Given a sequence like "A B M C D M E F M," what letterscome next? The answer that would be scored as correct is "G H M I J M," etc. To
find the pattern, one looks for pattern in the sequence. Every third letter is M.
The first letter in every triad is next in the alphabet to the second letter in thepreceding triad, and the second letter in the triad is next in the alphabet to thefirst. The answer is simply an extrapolation of this pattern.16 The sequence
extrapolator was a harbinger of scientific law discovery programs of the 1980s
and 1990s. The central idea was to use a hypothesis generator to search forpattern in data and to use indications of pattern, as detected, to guide thecontinuation of the search "nothing but" our old friend, heuristic search.
Another early discovery program was AM.17 Its task was to discover interesting
new concepts and interesting conjectures about them. AM had criteria for
judging what was interesting, a set of search heuristics (based on best-firstsearch), and elementary knowledge of some task domain (e.g., elementary set
theory). The program showed a considerable ability to discovery interesting new(to it) concepts, like the concept of prime number; but there was some
controversy about the bases for its discoveries, some critics claiming that the newconcepts were implicit in the LISP language in which AM was programmed. I do
not share this dismissive view, but it would divert us from our path to discuss theissues here.
16.
17.

Models of Thought, vol. 1, chapters 5.1 and 5.2 (with Kenneth Kotovsky).

D. B. Lenat, "Automated Theory Formation in Mathematics," Proceedings of the Fifth International
Joint Conference on Artificial Intelligence (1977), pp. 833 842. See also W. Shen, "Functional
Transformation in Al Discovery Systems," Artificial Intelligence, 41(1989):257 272.

Page 107

Rediscovering Classic Physics
Another discovery system of considerable interest is the BACON program, in its
several forms,18 which is capable of discovering invariants in bodies of numerical
data. Given data on the distances of the planets from the Sun and on the periodsof their orbits, it discovers that the ratio of the cubes of the periods to the squaresof the distances is the same for all the planets (Kepler's third law). From data on
the variation of electric current with the length of the resistance wire in a circuit,it infers Ohm's law. In a similar manner it finds the gas laws, Galileo's law offalling bodies, and many others.
BACON will introduce new concepts in order to explain the invariants it has
found. Given data showing that, when two bodies accelerate each other, theratios of the accelerations are always the same, it invents the concept of mass andassociates a mass with each of the bodies. In a similar manner it invents theconcept of refractive index (in Snell's law), of specific heat, and of chemicalvalence.
As with AM there is little that is novel in the basic construction of BACON.
Given two data sets, if it finds that one datum varies monotonically (directly orinversely) with the other, it tests whether their ratio (or product) is invariant. If it
succeeds, it has discovered a lawful relation in the data; if it fails, it has defined a
new variable, which can then be added to the others and the process repeated.
What is remarkable about the system's behavior is that finding laws of the sorts
mentioned above by this procedure does not require extensive search. Seldom is
it necessary to examine more than a dozen functions of the original variables inorder to find an invariant.
AM and BACON have been followed by a whole host of other discovery
programs that are illuminating many different aspects of scientific discovery; anumber of these are discussed in Scientific Discovery. Not only are such systems
capable of discovering new concepts but also they can plan sequences ofexperiments, postulate reaction paths for complex chemical reactions, induce
rules for interpreting data from mass
18.

P. Langley, H. A. Simon, G. L. Bradshaw and J. M. Zytkow, Scientific Discovery: Computational
Explorations of the Creative Process (Cambridge, MA: The MIT Press, 1987).

Page 108spectrogram analysis, and enlarge the state space of a system to accommodate
variables that are not directly observable.
Part of this research has been conceived as adding artificial intelligence to thetools of science. For example, the DENDRAL and MECHEM programs havegenerated discoveries that have been published in chemical journals as
contributions to chemistry. Much of the research, however, has aimed atdeepening our understanding of human discovery processes. The BACON
simulations, for example, have been compared with historical cases of discovery
in physics and chemistry, and some parallel experiments have been run withhuman subjects in the laboratory to compare their attempts at discovery withthose of BACON and with those recorded in the histories of science.19
The AM and BACON programs and their successors give us some reason tobelieve that discovery processes do not introduce new kinds of complexity into
human cognition. The demonstration will become more convincing when one ofthese systems discovers something of interest that is novel not only to it but tothe world. That test has not yet been passed.
Finding New Problem Representations
Every problem-solving effort must begin with creating a representation for the
problem a problem space in which the search for the solution can take place. Of
course, for most of the problems we encounter in our daily personal or
professional lives, we simply retrieve from memory a representation that wehave already stored and used on previous occasions. Sometimes, we have toadapt the representation a bit to the new situation, but that is usually a rather
simple matter.
Occasionally, however, we encounter a situation that doesn't seem to fit any ofthe problem spaces we have encountered before, even with some stretching andshaping. Then we are faced with a task of discovery that may be as formidable asfinding a new natural law. Newton was able to discover the law of gravitation
because he had previously found a new representation, the differential calculus,that Wren and Hooke, among others who were also searching for the law, didn'thave. More often, prob19.

Y. Qin and H. A. Simon, "Laboratory Replication of Scientific Discovery Processes," Cognitive
Science, 14(1990):281 312.

Page 109lems of representation arise that are midway in difficulty between simply
adapting a known representation and inventing the calculus.
In the difficult "insight" problem known as the "Mutilated Checkerboard," acheckerboard is covered by 32 dominoes, each of which covers exactly twosquares. Someone mutilates the board by snipping off the upper-left and lowerright corner squares. Can the mutilated board be covered by (31) dominoes? If
so, how; if not, why not?
Typically, subjects work for hours in the given problem space of the board anddominoes to find a covering. After frustration sets in, they begin to consider
changing their representation. But how? One doesn't come equipped with a"space" of representations, or even a generator of possible representations. The
few successful subjects notice, at some point, that in their unsuccessful attempts
at covering the board the uncovered squares are always of one color. Once they
notice this, they frequently notice also that each domino covers exactly one
square of each color, and the idea follows quickly that dominoes can only cover aboard that has equal numbers of squares of each color, which the mutilated board
does not.
Observation of subjects solving (or not solving) the mutilated checkerboard give
us some important insights into the problem of discovering new representations.
Focus of attention is the key to success focusing on the particular features of thesituation that are relevant to the problem, then building a problem spacecontaining these features but omitting the irrelevant ones. This single idea falls
far short of a theory of representation change but takes a first step towardbuilding such a theory. The process of discovering new representations is a major
missing link in our theories of thinking and is currently a major area of researchin cognitive psychology and artificial intelligence.20
Conclusion
Nothing that we have discovered about memory requires us to revise our basic
verdict about the complexity or simplicity of human cognition. We can still
maintain that,
20.

C. A. Kaplan and H. A. Simon, "In Search of Insight," Cognitive Psychology, 22(1990):374 419.

Page 110

Human beings, viewed as behaving systems, are quite simple. The apparent
complexity of our behavior over time is largely a reflection of thecomplexity of the environment in which we find ourselves . . .
provided that we include in what we call the human environment the cocoon of
information, stored in books and in long-term memory, that we spin about
ourselves.
That information, stored both as data and as procedures and richly indexed foraccess in the presence of appropriate stimuli, enables the simple basic
information processes to draw upon a very large repertory of information andstrategies, and accounts for the appearance of complexity in their behavior. The
inner environment, the hardware, is simple. Complexity emerges from therichness of the outer environment, both the world apprehended through thesenses and the information about the world stored in long-term memory.
A scientific account of human cognition describes it in terms of several sets ofinvariants. First, there are the parameters of the inner environment. Then, there
are the general control and search-guiding mechanisms that are used over andover again in all task domains. Finally, there are the learning and discovery
mechanisms that permit the system to adapt with gradually increasing
effectiveness to the particular environment in which it finds itself. The
adaptiveness of the human organism, the facility with which it acquires new
representations and strategies and becomes adept in dealing with highly
specialized environments, makes it an elusive and fascinating target of ourscientific inquiries and the very prototype of the artificial.

Page 111

5
The Science of Design: Creating the Artificial
Historically and traditionally, it has been the task of the science disciplines toteach about natural things: how they are and how they work. It has been the taskof engineering schools to teach about artificial things: how to make artifacts thathave desired properties and how to design.
Engineers are not the only professional designers. Everyone designs who devises
courses of action aimed at changing existing situations into preferred ones. The
intellectual activity that produces material artifacts is no different fundamentallyfrom the one that prescribes remedies for a sick patient or the one that devises a
new sales plan for a company or a social welfare policy for a state. Design, so
construed, is the core of all professional training; it is the principal mark that
distinguishes the professions from the sciences. Schools of engineering, as well
as schools of architecture, business, education, law, and medicine, are all
centrally concerned with the process of design.
In view of the key role of design in professional activity, it is ironic that in thiscentury the natural sciences almost drove the sciences of the artificial fromprofessional school curricula, a development that peaked about two or three
decades after the Second World War. Engineering schools gradually became
schools of physics and mathematics; medical schools became schools of
biological science; business schools became schools of finite mathematics. The
use of adjectives like "applied" concealed, but did not change, the fact. It simply
meant that in the professional schools those topics were selected frommathematics and the natural sciences for emphasis which were thought to be
most nearly relevant to professional practice. It did not mean that designcontinued to be taught, as distinguished from analysis.

Page 112

The movement toward natural science and away from the sciences of theartificial proceeded further and faster in engineering, business, and medicine thanin the other professional fields I have mentioned, though it has by no means beenabsent from schools of law, journalism, and library science. The stronger
universities were more deeply affected than the weaker, and the graduate
programs more than the undergraduate. During that time few doctoral
dissertations in first-rate professional schools dealt with genuine design
problems, as distinguished from problems in solid-state physics or stochastic
processes. I have to make partial exceptions for reasons I shall mention ofdissertations in computer science and management science, and there wereundoubtedly some others, for example, in chemical engineering.
Such a universal phenomenon must have had a basic cause. It did have a very
obvious one. As professional schools, including the independent engineeringschools, were more and more absorbed into the general culture of the university,
they hankered after academic respectability. In terms of the prevailing norms,
academic respectability calls for subject matter that is intellectually tough,analytic, formalizable, and teachable. In the past much, if not most, of what we
knew about design and about the artificial sciences was intellectually soft,
intuitive, informal, and cook-booky. Why would anyone in a university stoop toteach or learn about designing machines or planning market strategies when hecould concern himself with solid-state physics? The answer has been clear: heusually wouldn't.
The damage to professional competence caused by the loss of design fromprofessional curricula gradually gained recognition in engineering and medicine
and to a lesser extent in business. Some schools did not think it a problem (and a
few still do not), because they regarded schools of applied science as a superioralternative to the trade schools of the past. If that were the choice, we could
agree.1 But neither alternative is1.

That was in fact the choice in our engineering schools a generation ago. The schools needed to bepurged of vocationalism; and a genuine science of design did not exist even in a rudimentary form as
an alternative. Hence, introducing more fundamental science was the road forward. This was a main
theme in Karl Taylor Compton's presidential inaugural address at MIT in 1930:
I hope . . . that increasing attention in the Institute may be given to the fundamental sciences; that they
may achieve as never before the spirit and results of re-(footnote continued on next page)Page 113

satisfactory. The older kind of professional school did not know how to educate
for professional design at an intellectual level appropriate to a university; thenewer kind of school nearly abdicated responsibility for training in the coreprofessional skill. Thus we were faced with a problem of devising a professional
school that could attain two objectives simultaneously: education in both
artificial and natural science at a high intellectual level. This too is a problem ofdesign organizational design.
The kernel of the problem lies in the phrase "artificial science." The previous
chapters have shown that a science of artificial phenomena is always in imminent
danger of dissolving and vanishing. The peculiar properties of the artifact lie on
the thin interface between the natural laws within it and the natural laws without.
What can we say about it? What is there to study besides the boundary sciences
those that govern the means and the task environment?
The artificial world is centered precisely on this interface between the inner andouter environments; it is concerned with attaining goals by adapting the former
to the latter. The proper study of those who are concerned with the artificial is
the way in which that adaptation of means to environments is brought about andcentral to that is the process of design itself. The professional schools canreassume their professional responsibilities just to the degree that they discoverand teach a science of design, a body of intellectually tough, analytic, partly
formalizable, partly empirical, teachable doctrine about the design process.
It is the thesis of this chapter that such a science of design not only is possible
but also has been emerging since the mid-1970s. In fact, it is fair to say that thefirst edition of this book, published in 1969, was influential in its development,
serving as a call to action and outlining the form that the action could take. At
Carnegie Mellon University, one of the first engineering schools to move toward
research on the process of design, the(footnote continued from previous page)
search; that all courses of instruction may be examined carefully to see where training in details has
been unduly emphasized at the expense of the more powerful training in all-embracing fundamental
principles.
Notice that President Compton's emphasis was on "fundamental" an emphasis as sound today as it wasin 1930. What is called for is not a departure from the fundamental but an inclusion in the curriculum of
the fundamental in engineering along with the fundamental in natural science. That was not possible in
1930; but it is possible today.

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initial step was to form a Design Research Center, about 1975. The Center (since1985 called the ''Engineering Design Research Center") facilitated collaboration
among the faculty and students undertaking research on the science and practiceof design and developed elements of a theory of design that found their way backinto the undergraduate and graduate curricula. The Center continues to play animportant role in the modernization and strengthening of education and researchin design at Carnegie Mellon and elsewhere in the United States.
In substantial part, design theory is aimed at broadening the capabilities ofcomputers to aid design, drawing upon the tools of artificial intelligence andoperations research. Hence, research on many aspects of computer-aided design
is being pursued with growing intensity in computer science, engineering andarchitecture departments, and in operations research groups in business schools.
The need to make design theory explicit and precise in order to introduce
computers into the process has been the key to establishing its academic accept
ability its appropriateness for a university. In the remainder of this chapter I willtake up some of the topics that need to be incorporated in a theory of design andin instruction in design.
The Logic of Design: Fixed Alternatives
We must start with some questions of logic.2 The natural sciences are concernedwith how things are. Ordinary systems of logic the standard propositional andpredicate calculi, say serve these sciences well. Since the concern of standard
logic is with declarative statements, it is well suited for assertions about theworld and for inferences from those assertions.
Design, on the other hand, is concerned with how things ought to be, withdevising artifacts to attain goals. We might question whether the2.

I have treated the question of logical formalism for design at greater length in two earlier papers:"The Logic of Rational Decision," British Journal for the Philosophy of Science, 16(1965):169 186;
and "The Logic of Heuristic Decision Making," in Nicholas Rescher (ed.), The Logic of Decision andAction (Pittsburgh: University of Pittsburgh Press, 1967), pp. 1 35. The present discussion is basedon these two papers, which have been reprinted as chapters 3.1 and 3.2 in my Models of Discovery(Dordrecht: D. Reidel Pub. Co., 1977).

Page 115

forms of reasoning that are appropriate to natural science are suitable also fordesign. One might well suppose that introduction of the verb "should" mayrequire additional rules of inference, or modification of the rules already
imbedded in declarative logic.
Paradoxes of Imperative Logic
Various "paradoxes" have been constructed to demonstrate the need for a distinct
logic of imperatives, or a normative, deontic logic. In ordinary logic from "Dogsare pets" and "Cats are pets," one can infer "Dogs and cats are pets." But from
"Dogs are pets,'' "Cats are pets;" and "You should keep pets," can one infer "Youshould keep cats and dogs"? And from "Give me needle and thread!" can one
deduce, in analogy with declarative logic, "Give me needle or thread!"? Easily
frustrated people would perhaps rather have neither needle nor thread than one
without the other, and peace-loving people, neither cats nor dogs, rather thanboth.
As a response to these challenges of apparent paradox, there have beendeveloped a number of constructions of modal logic for handling "shoulds,""shalts;" and "oughts" of various kinds. I think it is fair to say that none of thesesystems has been sufficiently developed or sufficiently widely applied todemonstrate that it is adequate to handle the logical requirements of the processof design.
Fortunately, such a demonstration is really not essential, for it can be shown thatthe requirements of design can be met fully by a modest adaptation of ordinary
declarative logic. Thus a special logic of imperatives is unnecessary.
I should like to underline the word "unnecessary," which does not mean
"impossible." Modal logics can be shown to exist in the same way that giraffes
can namely, by exhibiting some of them. The question is not whether they exist,
but whether they are needed for, or even useful for, design.
Reduction to Declarative Logic
The easiest way to discover what kinds of logic are needed for design is to
examine what kinds of logic designers use when they are being careful about
their reasoning. Now there would be no point in doing this if designers werealways sloppy fellows who reasoned loosely, vaguely, andPage 116

intuitively. Then we might say that whatever logic they used was not the logic
they should use.
However, there exists a considerable area of design practice where standards ofrigor in inference are as high as one could wish. I refer to the domain of so-called"optimization methods," most highly developed in statistical decision theory andmanagement science but acquiring growing importance also in engineering
design theory. The theories of probability and utility, and their intersection, havereceived the painstaking attention not only of practical designers and decision
makers but also of a considerable number of the most distinguished logicians andmathematicians of recent generations. F. P. Ramsey, B. de Finetti, A. Wald, J.
von Neumann, J. Neyman, K. Arrow, and L. J. Savage are examples.
The logic of optimization methods can be sketched as follows: The "innerenvironment" of the design problem is represented by a set of given alternativesof action. The alternatives may be given in extenso: more commonly they are
specified in terms of command variables that have defined domains. The "outerenvironment" is represented by a set of parameters, which may be known withcertainty or only in terms of a probability distribution. The goals for adaptationof inner to outer environment are defined by a utility function a function, usuallyscalar, of the command variables and environmental parameters perhaps
supplemented by a number of constraints (inequalities, say, between functions of
the command variables and environmental parameters). The optimization
problem is to find an admissible set of values of the command variables,
compatible with the constraints, that maximize the utility function for the given
values of the environmental parameters. (In the probabilistic case we might say,"maximize the expected value of the utility function," for instance, instead of"maximize the utility function.")A stock application of this paradigm is the so-called "diet problem" shown infigure 6. A list of foods is provided, the command variables being quantities ofthe various foods to be included in the diet. The environmental parameters are
the prices and nutritional contents (calories, vitamins, minerals, and so on) of
each of the foods. The utility function is the cost (with a minus sign attached) of
the diet, subject to the constraints, say, that it not contain more than 2,000
calories per day, that itPage 117

Figure 6
The paradigm for imperative logic

meet specified minimum needs for vitamins and minerals, and that rutabaga not
be eaten more than once a week. The constraints may be viewed ascharacterizing the inner environment. The problem is to select the quantities offoods that will meet the nutritional requirements and side conditions at the given
prices for the lowest cost.
The diet problem is a simple example of a class of problems that are readily
handled, even when the number of variables is exceedingly large, by themathematical formalism known as linear programming. I shall come back to thetechnique a little later. My present concern is with the logic of the matter.
Since the optimization problem, once formalized, is a standard mathematicalproblem to maximize a function subject to constraints it is evident that the logic
used to deduce the answer is the standard logic of the predicate calculus onwhich mathematics rests. How does the formalism avoid making use of a special
logic of imperatives? It does so by dealing with sets of possible worlds: First
consider all the possible worlds that meet the constraints of the outerenvironment; then find the particular world in the set that meets the remaining
constraints of the goal andPage 118

maximizes the utility function. The logic is exactly the same as if we were toadjoin the goal constraints and the maximization requirement, as new "naturallaws," to the existing natural laws embodied in the environmental conditions. 3
We simply ask what values the command variables would have in a world
meeting all these conditions and conclude that these are the values the commandvariables should have.
Computing the Optimum
Our discussion thus far has already provided us with two central topics for the
curriculum in the science of design:
1. Utility theory and statistical decision theory as a logical framework forrational choice among given alternatives.
2. The body of techniques for actually deducing which of the available
alternatives is the optimum.
Only in trivial cases is the computation of the optimum alternative an easy matter(Recall Chapter 2). If utility theory is to have application to real-life design
problems, it must be accompanied by tools for actually making the computations.
The dilemma of the rational chess player is familiar to all. The optimal strategy
in chess is easily demonstrated: simply assign a value of + 1 to a win, 0 to adraw, 1 to a loss; consider all possible courses of play; mini max backward fromthe outcome of each, assuming each player will take the most favorable move at
any given point. This procedure will determine what move to make now. The
only trouble is that the computations required are astronomical (the number 10 120
is often mentioned in this context) and hence cannot be carried out not byhumans, not by existing computers, not by prospective computers.
A theory of design as applied to the game of chess would encompass not only theutopian mini max principle but also some practicable pro3.

The use of the notion of "possible worlds" to embed the logic of imperatives in declarative logic
goes back at least to Jørgen Jørgensen, "Imperatives and Logic," Erkenntnis, 7(1937 1938):288 296.
See also my Administrative Behavior (New York: Macmillan, 1947), chapter 3. Typed logics can beused to distinguish, as belonging to different types, statements that are true under different conditions(i.e., in different possible worlds), but, as my example shows, even this device is not usually needed.
Each new equation or constraint we introduce into a system reduces the set of possible states to a
subset of those previously possible.

Page 119cedures for finding good moves in actual board positions in real time, within thecomputational capacities of real human beings or real computers. The bestprocedures of this kind that exist today are still those stored in the memories ofgrandmasters, having the characteristics I described in chapters 3 and 4. But
there are now several computer programs that can rather regularly defeat all but a
few of the strongest human grandmasters. Even these programs do not possess
anything like the chess knowledge of human masters, but succeed by a
combination of brute-force computation (sometimes hundreds of millions ofvariations are analysed) with a good deal of "book" knowledge of opening
variations and a reasonably sophisticated criterion function for evaluating
positions.
The second topic then for the curriculum in the science of design consists in theefficient computational techniques that are available for actually finding
optimum courses of action in real situations, or reasonable approximations to realsituations. As I mentioned in chapter 2, that topic has a number of importantcomponents today, most of them developed at least to the level of practicalapplication within the past years. These include linear programming theory,
dynamic programming, geometric programming, queuing theory, and control
theory.
Finding Satisfactory Actions
The subject of computational techniques need not be limited to optimization.
Traditional engineering design methods make much more use of inequalities
specifications of satisfactory performance than of maxima and minima. So-called"figures of merit" permit comparison between designs in terms of "better" and"worse" but seldom provide a judgment of "best." For example, I may cite theroot-locus methods employed in the design of control systems.
Since there did not seem to be any word in English for decision methods thatlook for good or satisfactory solutions instead of optimal ones, some years ago I
introduced the term "satisficing" to refer to such procedures. Now no one in his
right mind will satisfice if he can equally well optimize; no one will settle forgood or better if he can have best. But that is not the way the problem usuallyposes itself in actual design situations.
In chapter 2 I argued that in the real world we usually do not have a choice
between satisfactory and optimal solutions, for we only rarely havePage 120a method of finding the optimum. Consider, for example, the well-known
combinatorial problem called the traveling salesman problem: given thegeographical locations of a set of cities, find the routing that will take a salesman
to all the cities with the shortest mileage.4 For this problem there is astraightforward optimizing algorithm (analogous to the mini max algorithm forchess): try all possible routings, and pick the shortest. But for any considerable
number of cities, the algorithm is computationally infeasible (the number ofroutes through N cities will be N!). Although some ways have been found for
cutting down the length of the search, no algorithm has been discovered
sufficiently powerful to solve the traveling salesman problem with a tolerable
amount of computing for a set of, say, fifty cities.
Rather than keep our salesman at home, we shall prefer of course to find asatisfactory, if not optimal, routing for him. Under most circumstances, common
sense will probably arrive at a fairly good route, but an even better one can often
be found by one or another of several heuristic methods.
An earmark of all these situations where we satisfice for inability to optimize isthat, although the set of available alternatives is "given" in a certain abstract
sense (we can define a generator guaranteed to generate all of them eventually),
it is not "given" in the only sense that is practically relevant. We cannot within
practicable computational limits generate all the admissible alternatives andcompare their respective merits. Nor can we recognize the best alternative, evenif we are fortunate enough to generate it early, until we have seen all of them.
We satisfice by looking for alternatives in such a way that we can generally findan acceptable one after only moderate search.
Now in many satisficing situations, the expected length of search for analternative meeting specified standards of acceptability depends on how high thestandards are set, but it depends hardly at all on the total size of the universe to
be searched. The time required for a search through a haystack for a needle sharp
enough to sew with depends on the density of distribution of sharp needles but
not on the total size of the stack.
4.

"The traveling salesman problem" and a number of closely analogous combinatorial problems suchas-the "warehouse location problem "have considerable practical importance, for instance, in siting
central power stations for an interconnected grid.

Page 121

Hence, when we use satisficing methods, it often does not matter whether or not
the total set of admissible alternatives is "given" by a formal but impracticablealgorithm. It often does not even matter how big that set is. For this reason
satisficing methods may be extendable to design problems in that broad range
where the set of alternatives is not "given" even in the quixotic sense that it is"given" for the traveling salesman problem. Our next task is to examine thispossibility.
The Logic of Design: Finding Alternatives
When we take up the case where the design alternatives are not given in any
constructive sense but must be synthesized, we must ask once more whether any
new forms of reasoning are involved in the synthesis, or whether again thestandard logic of declarative statements is all we need.
In the case of optimization we asked: "Of all possible worlds (those attainable forsome admissible values of the action variables), which is the best (yields thehighest value of the criterion function)?" As we saw, this is a purely empiricalquestion, calling only for facts and ordinary declarative reasoning to answer it.
In this case, where we are seeking a satisfactory alternative, once we have founda candidate we can ask: "Does this alternative satisfy all the design criteria?"
Clearly this is also a factual question and raises no new issues of logic. But how
about the process of searching for candidates? What kind of logic is needed forthe search?
Means-Ends Analysis
The condition of any goal-seeking system is that it is connected to the outsideenvironment through two kinds of channels: the afferent, or sensory, channels
through which it receives information about the environment and the efferent, or
motor, channels through which it acts on the environment.5 The system musthave some means of storing in its memory information about states of the world
afferent, or sensory, information
5.

Notice that we are not saying that the two kinds of channels operate independently of each other,since they surely do not in living organisms, but that we can distinguish conceptually, and to someextent neurologically, between the incoming and outgoing flows.

Page 122and information about actions efferent, or motor, information. Ability to attaingoals depends on building up associations, which may be simple or very
complex, between particular changes in states of the world and particular actions
that will (reliably or not) bring these changes about. In chapter 4 we described
these associations as productions.
Except for a few built-in reflexes, an infant has no basis for correlating its
sensory information with its actions. A very important part of its early learning isthat particular actions or sequences of actions will bring about particular changes
in the state of the sensed world. Until the infant builds up this knowledge, theworld of sense and the motor world are two entirely separate, entirely unrelated
worlds. Only as it begins to acquire experience as to how elements of the one
relate to elements of the other can it act purposefully on the world.
The computer problem-solving program called GPS, designed to model some of
the main features of human problem solving, exhibits in stark form how goaldirected action depends on building this kind of bridge between the afferent andthe efferent worlds. On the afferent, or sensory, side, GPS must be able torepresent desired situations or desired objects as well as the present situation. It
must be able also to represent differences between the desired and the present.
On the efferent side, GPS must be able to represent actions that change objects orsituations. To behave purposefully, GPS must be able to select from time to timethose particular actions that are likely to remove the particular differences
between desired and present states that the system detects. In the machinery ofGPS, this selection is achieved through a table of connections, which associateswith each kind of detectable difference those actions that are relevant to reducingthat difference. These are its associations, in the form of productions, which
relate the afferent to the efferent world. Since reaching a goal generally requiresa sequence of actions, and since some attempts may be ineffective, GPS mustalso have means for detecting the progress it is making (the changes in the
differences between the actual and the desired) and for trying alternate paths.
The Logic of Search
GPS then is a system that searches selectively through a (possibly large)
environment in order to discover and assemble sequences of actions that

Page 123

will lead it from a given situation to a desired situation. What are the rules oflogic that govern such a search? Is anything more than standard logic involved?
Do we require a modal logic to rationalize the process?
Standard logic would seem to suffice. To represent the relation between theafferent and the efferent worlds, we conceive GPS as moving through a large
maze. The nodes of the maze represent situations, described afferently; the paths
joining one node to another are the actions, described as motor sequences, thatwill transform the one situation into the other. At any given moment GPS isalways faced with a single question: "What action shall I try next?" Since GPS
has some imperfect knowledge about the relations of actions to changes in thesituation, this becomes a question of choice under uncertainty of a kind alreadydiscussed in a previous section.
It is characteristic of the search for alternatives that the solution, the complete
action that constitutes the final design, is built from a sequence of component
actions. The enormous size of the space of alternatives arises out of theinnumerable ways in which the component actions, which need not be very
numerous, can be combined into sequences.
Much is gained by considering the component actions in place of the sequencesthat constitute complete actions, because the situation when viewed afferently
usually factors into components that match at least approximately the component
actions derived from an efferent factorization. The reasoning implicit in GPS is
that, if a desired situation differs from a present situation by differences D , D , . .
. , D , and if action A removes differences of type D , action A removes
differences of type D , and so on, then the present situation can be transformed
into the desired situation by performing the sequence of actions A A , . . . A .
1

n

1

1

2

2

2

1

2

n

This reasoning is by no means valid in terms of the rules of standard logic in all
possible worlds. Its validity requires some rather strong assumptions about theindependence of the effects of the several actions on the several differences. One
might say that the reasoning is valid in worlds that are "additive" or "factorable"
in a certain sense. (The air of paradox about the cat-dog and needle-thread
examples cited earlier arises precisely from the non additivity of the actions inthese two cases. The first is, in economists' language, a case of decreasing
returns; the second, a case of increasing returns.)

Page 124

Now the real worlds to which problem solvers and designers address themselves
are seldom completely additive in this sense. Actions have side consequences(may create new differences) and sometimes can only be taken when certain side
conditions are satisfied (call for removal of other differences before they become
applicable). Under these circumstances one can never be certain that a partial
sequence of actions that accomplishes certain goals can be augmented to provide
a solution that satisfies all the conditions and attains all the goals (even though
they be satisficing goals) of the problem.
For this reason problem-solving systems and design procedures in the real world
do not merely assemble problem solutions from components but must search forappropriate assemblies. In carrying out such a search, it is often efficient todivide one's eggs among a number of baskets that is, not to follow out one line
until it succeeds completely or fails definitely but to begin to explore several
tentative paths, continuing to pursue a few that look most promising at a given
moment. If one of the active paths begins to look less promising, it may bereplaced by another that had previously been assigned a lower priority.
Our discussion of design when the alternatives are not given has yielded at least
three additional topics for instruction in the science of design:
3. Adaptation of standard logic to the search for alternatives. Design solutions
are sequences of actions that lead to possible worlds satisfying specified
constraints. With satisficing goals the sought-for possible worlds are seldom
unique; the search is for sufficient, not necessary, actions for attaining goals.
4. The exploitation of parallel, or near-parallel, factorizations of differences.
Means-end analysis is an example of a broadly applicable problem-solving
technique that exploits this factorization.
5. The allocation of resources for search to alternative, partly explored action
sequences. I should like to elaborate somewhat on this last-mentioned topic.
Design As Resource Allocation
There are two ways in which design processes are concerned with the allocationof resources. First, conservation of scarce resources may be one of the criteria fora satisfactory design. Second, the design process itself

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involves management of the resources of the designer, so that his efforts will not
be dissipated unnecessarily in following lines of inquiry that prove fruitless.
There is nothing special that needs to be said here about resource conservation
cost minimization, for example, as a design criterion. Cost minimization has
always been an implicit consideration in the design of engineering structures, but
until a few years ago it generally was only implicit, rather than explicit. Moreand more cost calculations have been brought explicitly into the design
procedure, and a strong case can be made today for training design engineers inthat body of technique and theory that economists know as ''cost-benefit
analysis."
An Example from Highway Design
The notion that the costs of designing must themselves be considered in guiding
the design process began to take root only as formal design procedures havedeveloped, and it still is not universally applied. An early example, but still avery good one, of incorporating design costs in the design process is theprocedure, developed by Marvin L. Manheim as a doctoral thesis at MIT, forsolving highway location problems.6
Manheim's procedure incorporates two main notions: first, the idea of specifying
a design progressively from the level of very general plans down to determining
the actual construction; second, the idea of attaching values to plans at the higher
levels as a basis for deciding which plans to pursue to levels of greater
specificity.
In the case of highway design the higher-level search is directed toward
discovering "bands of interest" within which the prospects of finding a good
specific route are promising. Within each band of interest one or more locations
is selected for closer examination. Specific designs are then developed forparticular locations. The scheme is not limited of course to this specific threelevel division, but it can be generalized as appropriate.
Manheim's scheme for deciding which alternatives to pursue from one level tothe next is based on assigning costs to each of the design activities andestimating highway costs for each of the higher-level plans. The
6.

Marvin L. Manheim, Hierarchical Structure: A Model of Design and Planning Processes(Cambridge: The MIT Press, 1966).

Page 126highway cost associated with a plan is a prediction of what the cost would be forthe actual route if that plan were particularized through subsequent design
activity. In other words, it is a measure of how "promising" a plan is. Those
plans are then pursued to completion that look most promising after theprospective design costs have been offset against them.
In the particular method that Manheim describes, the "promise" of a plan isrepresented by a probability distribution of outcomes that would ensue if it were
pursued to completion. The distribution must be estimated by the engine era
serious weakness of the method but, once estimated, it can be used within theframework of Bayesian decision theory. The particular probability model used is
not the important thing about the method; other methods of valuation without theBayesian superstructure might be just as satisfactory.
In the highway location procedure the evaluation of higher-level plans performs
two functions. First, it answers the question, "Where shall I search next?"
Second, it answers the question, "When shall I stop the search and accept asolution as satisfactory?" Thus it is both a steering mechanism for the search anda satisficing criterion for terminating the search.
Schemes for Guiding Search
Let us generalize the notion of schemes for guiding search activity beyond
Manheim's specific application to a highway location problem and beyond his
specific guidance scheme based on Bayesian decision theory. Consider the
typical structure of a problem-solving program. The program begins to searchalong possible paths, storing in memory a "tree" of the paths it has explored.
Attached to the end of each branch each partial path is a number that is supposed
to express the "value" of that path.
But the term "value" is really a misnomer. A partial path is not a solution of the
problem, and a path has a "true" value of zero unless it leads toward a solution.
Hence it is more useful to think of the values as estimates of the gain to be
expected from further search along the path than to think of them as ''values" inany more direct sense. For example, it may be desirable to attach a relatively
high value to a partial exploration that may lead to a very good solution but witha low probability. If the prospect fades on further exploration, only the cost of
the search has been lost. The disappointing outcome need not be accepted, but analternative

Page 127

path may be taken instead. Thus the scheme for attaching values to partial paths
may be quite different from the evaluation function for proposed complete
solutions.7
When we recognize that the purpose of assigning values to incomplete paths is toguide the choice of the next point for exploration, it is natural to generalize evenfurther. All kinds of information gathered in the course of search may be of value
in selecting the next step in search. We need not limit ourselves to valuations ofpartial search paths.
For example, in a chess-playing program an exploration may generate acontinuation move different from any that was proposed by the initial movegenerator. Whatever the context the branch of the search tree on which the move
was actually generated, it can now be removed from the context and consideredin the context of other move sequences. Such a scheme was added on a limited
basis by Baylor to MATER, a program for discovering check-mating
combinations in chess, and it proved to enhance the program's powersignificantly.8
Thus search processes may be viewed as they have been in most discussions ofproblem solving as processes for seeking a problem solution. But they can beviewed more generally as processes for gathering information about problem
structure that will ultimately be valuable in discovering a problem solution. The
latter viewpoint is more general than the former in a significant sense, in that itsuggests that information obtained along any particular branch of a search tree
may be used in many contexts besides the one in which it was generated. Only afew problem-solving programs exist today that can be regarded as moving even a
modest distance from the earlier, more limited viewpoint to the newer one.9
7.

That this point is not obvious can be seen from the fact that most chess-playing programs haveused similar or identical evaluation procedures both to guide search and to evaluate the positions
reached at the ends of paths.
8.

George W. Baylor and Herbert A. Simon, "A Chess Mating Combinations Program" Proceedings of
the Spring Joint Computer Conference, Boston, April 26 28, (1966):431 447 (Washington: Spartan
Books, 1966), reprinted in Models of Thought, chapter 4.3.
9.

A formal theory of the optimal choice of search paths can be found in H. A. Simon and J. B. Kadane,"Optimal Problem-Solving Search: All-or-none Solutions;" Artificial Intelligence, 6(1975):235 247.

Page 128

The Shape of the Design: Hierarchy
In my first chapter I gave some reasons why complex systems might be expected
to be constructed in a hierarchy of levels, or in a boxes-within-boxes form. The
basic idea is that the several components in any complex system will performparticular sub functions that contribute to the overall function. Just as the "innerenvironment" of the whole system may be defined by describing its functions,
without detailed specification of its mechanisms, so the "inner environment" ofeach of the subsystems may be defined by describing the functions of thatsubsystem, without detailed specification of its submechanisms.10
To design such a complex structure, one powerful technique is to discover viable
ways of decomposing it into semi-independent components corresponding to its
many functional parts. The design of each component can then be carried out
with some degree of independence of the design of others, since each will affectthe others largely through its function and independently of the details of themechanisms that accomplish the function.11
There is no reason to expect that the decomposition of the complete design intofunctional components will be unique. In important instances there may exist
alternative feasible decompositions of radically different kinds. This possibility
is well known to designers of administrative organizations, where work can bedivided up by sub functions, by sub processes, by subareas, and in other ways.
Much of classical organization theory in fact was concerned precisely with thisissue of alternative decompositions of a collection of interrelated tasks.
The Generator-Test Cycle
One way of considering the decomposition, but acknowledging that theinterrelations among the components cannot be ignored completely, is to think of
the design process as involving, first, the generation of alterna10.

I have developed this argument at greater length in my essay "The Architecture of Complexity,"
chapter 8.
11.

For a recent discussion of functional analysis in design, see Clive L. Dym, Engineering Design (NewYork, NY: Cambridge University Press, 1994), pp. 134 139.

Page 129

tives and, then, the testing of these alternatives against a whole array of
requirements and constraints. There need not be merely a single generate-test
cycle, but there can be a whole nested series of such cycles. The generators
implicitly define the decomposition of the design problem, and the tests
guarantee that important indirect consequences will be noticed and weighed.
Alternative decompositions correspond to different ways of dividing the
responsibilities for the final design between generators and tests.
To take a greatly oversimplified example, a series of generators may generate
one or more possible outlines and schemes of fenestration for a building, whiletests may be applied to determine whether needs for particular kinds of rooms
can be met within the outlines generated. Alternatively the generators may be
used to evolve the structure of rooms, while tests are applied to see whether they
are consistent with an acceptable over-all shape and design. The house can be
designed from the outside in or from the inside out.12
Alternatives are also open, in organizing the design process, as to how far
development of possible subsystems will be carried before the over-all
coordinating design is developed in detail, or vice-versa, how far the overalldesign should be carried before various components, or possible components, aredeveloped. These alternatives of design are familiar to architects. They arefamiliar also to composers, who must decide how far the architectonics of amusical structure will be evolved before some of the component musical themes
and other elements have been invented. Computer programmers face the same
choices, between working downward from executive routines to subroutines or
upward from component subroutines to a coordinating executive.
A theory of design will include principles for deciding such questions ofprecedence and sequence in the design process. As examples, the approach todesigning computer programs called structured programming is concerned inconsiderable part with attending to design sub problems
12.

I am indebted to John Grason for many ideas on the topic of this section. J. Grason, "Fundamental
Description of a Floor Plan Design Program," EDRA1, Proceedings of the First Environmental
Design Association Conference, H. Sanoff and S. Cohn (eds.), North Carolina State University,
1970.

Page 130in the proper order (usually top-down); and much instruction in schools ofarchitecture focuses on the same concerns.
Process As a Determinant of Style
When we recall that the process will generally be concerned with finding asatisfactory design, rather than an optimum design, we see that sequence and thedivision of labor between generators and tests can affect not only the efficiency
with which resources for designing are used but also the nature of the final
design as well. What we ordinarily call "style" may stem just as much from thesedecisions about the design process as from alternative emphases on the goals tobe realized through the final design.13 An architect who designs buildings fromthe outside in will arrive at quite different buildings from one who designs fromthe inside out, even though both of them might agree on the characteristics that asatisfactory building should possess.
When we come to the design of systems as complex as cities, or buildings, or
economies, we must give up the aim of creating systems that will optimize somehypothesized utility function, and we must consider whether differences in styleof the sort I have just been describing do not represent highly desirable variantsin the design process rather than alternatives to be evaluated as "better" or"worse." Variety, within the limits of satisfactory constraints, may be a desirable
end in itself, among other reasons, because it permits us to attach value to thesearch as well as its out come to regard the design process as itself a valued
activity for those who participate in it.
We have usually thought of city planning as a means whereby the planner's
creative activity could build a system that would satisfy the needs of a populace.
Perhaps we should think of city planning as a valuable creative activity in which
many members of a community can have the opportunity of participating if wehave wits to organize the process that way. I shall have more to say on these
topics in the next chapter.
However that may be, I hope I have illustrated sufficiently that both the shape ofthe design and the shape and organization of the design process
13.

H. A. Simon, "Style in Design," Proceedings of the 2nd Annual Conference of the Environmental
Design Research Association, Pittsburgh, PA: Carnegie Mellon University (1971), pp. 1 10.

Page 131are essential components of a theory of design. These topics constitute the sixth
item in my proposed curriculum in design:
6. The organization of complex structures and its implication for theorganization of design processes.
Representation of the Design
I have by no means surveyed all facets of the emerging science of design. In
particular I have said little about the influence of problem representation on
design. Although the importance of the question is recognized today, we are still
far from a systematic theory of the subject in particular, a theory that would tell
us how to generate effective problem representations.14 I shall cite one example,
to make clear what I mean by "representation."
Here are the rules of a game, which I shall call number scrabble. The game isplayed by two people with nine cards let us say the ace through the nine ofhearts. The cards are placed in a row, face up, between the two players. The
players draw alternately, one at a time, selecting any one of the cards that remainin the center. The aim of the game is for a player to make up a "book," that is, aset of exactly three cards whose spots add to 15, before his opponent can do so.
The first player who makes a book wins; if all nine cards have been drawn
without either player making a book, the game is a draw.
What is a good strategy in this game? How would you go about finding one? If
the reader has not already discovered it for himself, let me show how a change in
representation will make it easy to play the game well. The magic square here,
which I introduced in the third chapter, is made up of the numerals from 1
through 9.
4 9 2
3 5 7
8 1 6
14.

As examples of current thinking about representation see chapters 5 ("Representing Designed
Artifacts") and 6 ("Representing Design Processes") in C. L. Dym, op. cit., and chapter 6("Representation in Design") in Ömer Akin, op. cit. For a more general theoretical discussion, see R.
E. Korf, "Toward a Model of Representational Changes,'' Artificial Intelligence, 14(1980):41 78.

Page 132

Each row, column, or diagonal adds to 15, and every triple of these numerals that
add to 15 is a row, column, or diagonal of the magic square. From this, it isobvious that "making a book" in number scrabble is equivalent to getting "three
in a row" in the game of tic-tac-toe. But most people know how to play tic-tactoe well, hence can simply transfer their usual strategy to number scrabble. 15
Problem Solving As Change in Representation
That representation makes a difference is a long-familiar point. We all believethat arithmetic has become easier since Arabic numerals and place notation
replaced Roman numerals, although I know of no theoretic treatment that
explains why.
That representation makes a difference is evident for a different reason. All
mathematics exhibits in its conclusions only what is already implicit in its
premises, as I mentioned in a previous chapter. Hence all mathematicalderivation can be viewed simply as change in representation, making evident
what was previously true but obscure.
This view can be extended to all of problem solving. Solving a problem simply
means representing it so as to make the solution transparent.16 If the problem
solving could actually be organized in these terms, the issue of representation
would indeed become central. But even if it cannot if this is too exaggerated aview a deeper understanding of how representations are created and how they
contribute to the solution of problems will become an essential component in the
future theory of design.
Spatial Representation
Since much of design, particularly architectural and engineering design, isconcerned with objects or arrangements in real Euclidean two15.

Number scrabble is not the only isomorph of tic-tac-toe. John A. Michon has described another,
JAM, which is the dual of tic-tac-toe in the sense of projective geometry. That is, the rows, columns,
and diagonals of tic-tac-toe become points in JAM, and the squares of the former become line
segments joining the points. The game is won by "jamming" all the segments through a point a moveconsists of seizing or jamming a single segment. Other isomorphs of tic-tac-toe are known as well.
16.

Saul Amarel, "On the Mechanization of Creative Processes," IEEE Spectrum 3 (April 1966):112 114.

Page 133dimensional or three-dimensional space, the representation of space and of thingsin space will necessarily be a central topic in a science of design. From ourprevious discussion of visual perception, it should be clear that "space" inside thehead of the designer or the memory of a computer may have very different
properties from a picture on paper or a three-dimensional model.
These representational issues have already attracted the attention of thoseconcerned with computer-aided design the cooperation of human and computerin the design process. As a single example, I may mention Ivan Sutherland's
pioneering SKETCHPAD program which allowed geometric shapes to be
represented and conditions to be placed on these shapes in terms of constraints,
to which they then conformed.17
Geometric considerations are also prominent in the attempts to automatecompletely the design, say, of printed or etched circuits, or of buildings. Grason,for example, in a system for designing house floor plans, constructs an internal
representation of the layout that helps one decide whether a proposed set ofconnections among rooms, selected to meet design criteria for communication,and so on, can be realized in a plane.18
The Taxonomy of Representation
An early step toward understanding any set of phenomena is to learn what kindsof things there are in the set to develop a taxonomy. This step has not yet beentaken with respect to representations. We have only a sketchy and incomplete
knowledge of the different ways in which problems can be represented and muchless knowledge of the significance of the differences.
In a completely pragmatic vein we know that problems can be describedverbally, in natural language. They often can be described mathematically, using
standard formalisms of algebra, geometry, set theory, analysis, or topology. If the
problems relate to physical objects, they (or their solutions) can be represented
by floor plans, engineering drawings,
17.

I. E. Sutherland, "SKETCHPAD, A Man-Machine Graphical Communication System,"
Proceedings, AFIPS Spring Joint Computer Conference, 1963 (Baltimore: Spartan Books), pp. 329
346.
18.

See also C. E. Pfefferkorn, "The Design Problem Solver: A System for Designing Equipment orFurniture Layouts," in C. M. Eastman (ed.), Spatial Synthesis in Computer-Aided Building Design(London: Applied Science Publishers, 1975).

Page 134

renderings, or three-dimensional models. Problems that have to do with actions
can be attacked with flow charts and programs.
Other items most likely will need to be added to the list, and there may exist
more fundamental and significant ways of classifying its members. But eventhough our classification is incomplete, we are beginning to build a theory of theproperties of these representations. The growing theories of computerarchitectures and programming languages for example, the work on functional
languages and object-oriented languages illustrate some of the directions that atheory of representations can take. There has also been closely parallel progress,
some of it reviewed in chapters 3 and 4, toward understanding the human use ofrepresentations in thinking. These topics begin to provide substance for the final
subject in our program on the theory of design:
7. Alternative representations for design problems.
Summary Topics in the Theory of Design
My main goal in this chapter has been to show that there already exist today a
number of components of a theory of design and a substantial body ofknowledge, theoretical and empirical, relating to each. As we draw up our
curriculum in design in the science of the artificial to take its place by the side ofnatural science in the whole engineering curriculum, it includes at least thefollowing topics:
THE EVALUATION OF DESIGNS

1. Theory of evaluation: utility theory, statistical decision theory
2. Computational methods:a. Algorithms for choosing optimal alternatives such as linear programming computations, control theory,
dynamic programmingb. Algorithms and heuristics for choosing satisfactory alternatives
3. THE FORMAL LOGIC OF DESIGN: imperative and declarative logics
THE SEARCH FOR ALTERNATIVES
4. Heuristic search: factorization and means-ends analysis
5. Allocation of resources for search
6. THEORY OF STRUCTURE AND DESIGN ORGANIZATION: hierarchic systems
7. REPRESENTATION OF DESIGN PROBLEMSPage 135

In small segments of the curriculum the theory of evaluation, for example, andthe formal logic of design it is already possible to organize the instruction withina framework of systematic, formal theory. In many other segments the treatment
would be more pragmatic, more empirical.
But nowhere do we need to return or retreat to the methods of the cookbook thatoriginally put design into disrepute and drove it from the engineering curriculum.
For there exist today a considerable number of examples of actual design
processes, of many different kinds, that have been defined fully and cast in themetal, so to speak, in the form of running computer programs: optimizing
algorithms, search procedures, and special-purpose programs for designingmotors, balancing assembly lines, selecting investment portfolios, locating
warehouses, designing highways, diagnosing and treating diseases, and so forth.19
Because these computer programs describe complex design processes incomplete, painstaking detail, they are open to full inspection and analysis, or totrial by simulation. They constitute a body of empirical phenomena to which thestudent of design can address himself and which he can seek to understand.
There is no question, since these programs exist, of the design process hiding
behind the cloak of "judgment" or "experience." Whatever judgment orexperience was used in creating the programs must now be incorporated in themand hence be observable. The programs are the tangible record of the variety ofschemes that man has devised to explore his complex outer environment and to
discover in that environment the paths to his goals.
Role of Design in the Life of the Mind
I have called my topic "the theory of design" and my curriculum a "program indesign." I have emphasized its role as complement to the natural
19.

A number of these programs are described in Dym, op. cit., and others are discussed in aforthcoming book on Engineering Design in the Large, written by faculty associated with theEngineering Design Research Center at Carnegie Mellon University. Dym concludes each chapter ofhis book with a commentary on other relevant publications. Dym's book has a bibliography of morethan 200 items, a majority of them referring to specific design projects and systems; its extent gives
some indication of the rate at which the science of design is now progressing.

Page 136

science curriculum in the total training of a professional engineer or of any
professional whose task is to solve problems, to choose, to synthesize, to decide.
But there is another way in which the theory of design may be viewed in relation
to other knowledge. My third and fourth chapters were chapters on psychology
specifically on man's relation to his biological inner environment. The present
chapter may also be construed as a chapter on psychology: on man's relation to
the complex outer environment in which he seeks to survive and achieve.
All three chapters, so construed, have import that goes beyond the professional
work of the person we have called the "designer." Many of us have been
unhappy about the fragmentation of our society into two cultures. Some of us
even think there are not just two cultures but a large number of cultures. If weregret that fragmentation, then we must look for a common core of knowledge
that can be shared by the members of all cultures a core that includes moresignificant topics than the weather, sports, automobiles, the care and feeding ofchildren, or perhaps even politics. A common understanding of our relation to
the inner and outer environments that define the space in which we live andchoose can provide at least part of that significant core.
This may seem an extravagant claim. Let me use the realm of music to illustrate
what I mean. Music is one of the most ancient of the sciences of the artificial,
and was so recognized by the Greeks. Anything I have said about the artificial
would apply as well to music, its composition or its enjoyment, as to theengineering topics I have used for most of my illustrations.
Music involves a formal pattern. It has few (but important) contacts with theinner environment; that is, it is capable of evoking strong emotions, its patterns
are detectable by human listeners, and some of its harmonic relations can begiven physical and physiological interpretations (though the aesthetic import ofthese is debatable). As for the outer environment, when we view composition asa problem in design, we encounter just the same tasks of evaluation, of search foralternatives, and of representation that we do in any other design problem. If it
pleases us, we can even apply to music some of the same techniques of automatic
design by computer that have been used in other fields of design. If

Page 137

computer-composed music has not yet reached notable heights of aesthetic
excellence, it deserves, and has already received, serious attention fromprofessional composers and analysts, who do not find it written in tongues aliento them.20
Undoubtedly there are tone-deaf engineers, just as there are mathematicallyignorant composers. Few engineers and composers, whether deaf, ignorant, ornot, can carry on a mutually rewarding conversation about the content of eachother's professional work. What I am suggesting is that they can carry on such a
conversation about design, can begin to perceive the common creative activity inwhich they are both engaged, can begin to share their experiences of the creative,
professional design process.
Those of us who have lived close to the development of the modern computer
through gestation and infancy have been drawn from a wide variety ofprofessional fields, music being one of them. We have noticed the growing
communication among intellectual disciplines that takes place around thecomputer. We have welcomed it, because it has brought us into contact with newworlds of knowledge has helped us combat our own multiple-cultures isolation.
This breakdown of old disciplinary boundaries has been much commented upon,and its connection with computers and the information sciences often noted.
But surely the computer, as a piece of hardware, or even as a piece ofprogrammed software, has nothing to do directly with the matter. I have alreadysuggested a different explanation. The ability to communicate across fields thecommon ground comes from the fact that all who use computers in complex
ways are using computers to design or to participate in the process of design.
Consequently we as designers, or as designers of design processes, have had to
be explicit as never before about what is involved in creating a design and whattakes place while the creation is going on.
The real subjects of the new intellectual free trade among the many cultures are
our own thought processes, our processes of judging, deciding,
20.

L. A. Hillier and L. M. Isaacson's Experimental Music (New York: McGraw-Hill, 1959), reporting
experiments begun more than four decades ago, still provides a good introduction to the subject ofmusical composition, viewed as design. See also Walter R. Reitman, Cognition and Thought (NewYork: Wiley, 1965), chapter 6, "Creative Problem Solving: Notes from the Autobiography of a
Fugue."

Page 138choosing, and creating. We are importing and exporting from one intellectual
discipline to another ideas about how a serially organized information-processing
system like a human being or a computer, or a complex of men and women andcomputers in organized cooperation solves problems and achieves goals in outerenvironments of great complexity.
The proper study of mankind has been said to be man. But I have argued thatpeople or at least their intellective component may be relatively simple, that mostof the complexity of their behavior may be drawn from their environment, fromtheir search for good designs. If I have made my case, then we can conclude that,in large part, the proper study of mankind is the science of design, not only as theprofessional component of a technical education but as a core discipline forevery liberally educated person.

Page 139

6
Social Planning: Designing the Evolving Artifact
In chapter 5 I surveyed some of the modern tools of design that are used byplanners and artificers. Even before most of these tools were available to them,
ambitious planners often took whole societies and their environments as systems
to be refashioned. Some recorded their utopias in books Plato, Sir Thomas More,Marx. Others sought to realize their plans by social revolution in America,
France, Russia, China. Many or most of the large-scale designs have centered onpolitical and economic arrangements, but others have focused on the physicalenvironment river development plans, for example, reaching from ancient Egypt
to the Tennessee Valley to the Indus and back to today's Nile.
As we look back on such design efforts and their implementation, and as we
contemplate the tasks of design that are posed in the world today, our feelings arevery mixed. We are energized by the great power our technological knowledge
bestows on us. We are intimidated by the magnitude of the problems it creates oralerts us to. We are sobered by the very limited success and sometimes disastrous
failure of past efforts to design on the scale of whole societies. We ask, "If we
can go to the Moon, why can't we . . . ?"not expecting an answer, for we knowthat going to the Moon was a simple task indeed, compared with some others wehave set for ourselves, such as creating a humane society or a peaceful world.
Wherein lies the difference?
Going to the Moon was a complex matter along only one dimension: it
challenged our technological capabilities. Though it was no mean
accomplishment, it was achieved in an exceedingly cooperative environment,
employing a single new organization, NASA, that was charged with a single,
highly operational goal. With enormous resources provided toPage 140

it, and operating through well-developed market mechanisms, that organization
could draw on the production capabilities and technological sophistication of ourwhole society. Although several potential side effects of the activity (notably its
international political and military significance, and the possibility oftechnological spinoffs) played a major role in motivating the project, they did nothave to enter much into the thoughts of the planners once the goal of placing
human beings on the Moon had been set. Moreover these by-product benefits andcosts are not what we mean when we say the project was a success. It was a
success because people walked on the surface of the Moon. Nor did anyone
anticipate what turned out to be one of the more important consequences of thesevoyages: the vivid new perspective we gained of our place in the universe when
we first viewed our own pale, fragile planet from space.
Consider now a quite different example of human design. Some twenty years ago
we celebrated the 200th birthday of our nation, and about a decade ago wecelebrated the 200th anniversary of the framing of its political constitution.
Almost all of us in the free world regard that document as an impressive example
of success in human planning. We regard it as a success because theConstitution, much modified and much interpreted, still survives as theframework for our political institutions, and because the society that operates
within its framework has provided most of us with a broad range of freedoms
and a high level of material comfort.
Both achievements the voyages to the Moon and the survival of the American
Constitution are triumphs of bounded rationality. A necessary, though not a
sufficient, condition of their success was that they were evaluated against limited
objectives. I have already argued that point with respect to NASA. As to thefounding fathers it is instructive to examine their own views of their goals,
reflected in The Federalist and the surviving records of the constitutional
convention.1 What is striking about these documents is their practical sense andthe awareness they exude of the limits of foresight about large human affairs.
Most of the framers of the Constitution accepted very restricted objectives fortheir artifact princi1.

The authors of The Federalist were Madison, Hamilton, and Jay, but principally the first named.
My edition is that edited by P. L. Ford (New York: Holt, 1898). Madison's notes are our chief sourceon the proceedings of the convention.

Page 141

pally the preservation of freedom in an orderly society. Moreover they did not
postulate a new man to be produced by the new institutions but accepted as oneof their design constraints the psychological characteristics of men and women as
they knew them, their selfishness as well as their common sense. In their own
cautious words (The Federalist, no. 55), ''As there is a degree of depravity inmankind which requires a certain degree of circumspection and distrust, so there
are other qualities in human nature which justify a certain portion of esteem andconfidence."
These examples illustrate some of the characteristics and complexities ofdesigning artifacts on a societal scale. The success of planning on such a scale
may call for modesty and restraint in setting the design objectives and drastic
simplification of the real-world situation in representing it for purposes of thedesign process. Even with restraint and simplification difficult obstacles must
usually be surmounted to reach the design objectives. The obstacles and some of
the techniques for overcoming them provide the main subject of this chapter.
Our first topic will be problem representation; our second, ways ofaccommodating to the inadequacies that can be expected in data; our third, how
the nature of the client affects planning; our fourth, limits on the planner's timeand attention; and our fifth, the ambiguity and conflict of goals in societal
planning. These topics, which can be viewed as a budget of obstacles oralternatively as a budget of planning requirements, will suggest to us some
additions to the curriculum in design outlined in the last chapter.
Representing the Design Problem
In the previous chapter representation was discussed mainly in the context ofrelatively well-structured, middle-sized tasks. Representation problems take on
new dimensions where social design is involved.
Organization As Representation
In 1948 the U.S. government took a bold initiative to restore the postwar
economies of the nations of western Europe, the so-called "Marshall Plan,"which was implemented through the Economic Cooperation

Page 142

Administration (ECA).2 An initial task in carrying out the plan was to shape the
ECA organization. The answer to the organizational question depended on how
one conceptualized the program. At least six different, and largely contradictory,
conceptions were offered for the agency by the persons who were initially
recruited to organize and manage it.
Congress had appropriated $5.3 billion for the first year's operations. Somethought that the task was to screen shopping lists proposed by the European
nations to make sure the lists contained what was really "needed" (commodity
screening approach). Others thought the task was to determine the "dollar gap" ineach nation's balance of payments and to authorize funds to close that gap(balance of trade approach). Others thought that the main task was to build up astrong deliberative institution in Europe, so that the recipient nations could maketheir own plans for use of the funds and thereby strengthen their collaboration(European cooperation approach). Others thought that decisions should be madeprimarily through bilateral agreements between the United States and each of therecipient nations (bilateral pledge approach). Others thought that at least the
portion of the appropriation that was earmarked for loans ($1 billion) should be
handled on a project basis, each project being evaluated for its soundness as aninvestment (investment bank approach). Others thought that the ECA shouldhave a policy organ for making broad decisions, then a number of administrative
organs for implementing them (policy and administration approach). Each ofthese representations had some basis in the congressional legislation establishing
the ECA.
With a little reflection it is easy to see that very different assistance plans would
result from implementing these different approaches, with very different
economic and political consequences for the European nations and the UnitedStates. Conceptualizing the problem in a particular way implied organizing theagency in a manner consistent with that conceptualization. And different
organizations would lead inevitably to the implementation of quite different
programs, emphasizing certain goals and subordinating others, even if all thealternative policies were in some general sense consistent with the congressional
intent.
2.

I have told this story in more detail in "The Birth of an Organization," chapter 16 in Administrative
Behavior, 3rd ed. (New York: The Free Press, 1976).

Page 143

As matters worked out, although vestigial organs representing each of the six
approaches were still visible in the ECA after a year of operation, the balance oftrade and European cooperation approaches generally prevailed, creating ameasure of European economic stability and laying the groundwork for what
later became the Common Market and ultimately the European Union. Whileeach of the six approaches to the organization of ECA had some rational basis,
trying to implement all of them simultaneously could (and almost did) create
thorough confusion in the agency and among its clients. What was needed was
not so much a "correct" conceptualization as one that could be understood by all
the participants and that would facilitate action rather than paralyze it. The
organization of ECA, as it evolved, provided a common problem representation
within which all could work.
Finding the Limiting Resource
A second example illustrates the importance, in choosing a representation for adesign problem, of identifying correctly the limiting resource or resources. A few
years ago, the State Department was troubled by the congestion that affected its
incoming communication lines whenever there was a crisis abroad. The
teletypes, unable to output messages as rapidly as they were received, would fall
many hours behind. Important messages to Washington were seriously delayedin transmission.
Since printing capacity was identified as the limiting factor, it was proposed to
remedy the situation by substituting line printers for the teletypes, thereby
increasing output by several orders of magnitude. No one asked about the next
link in the chain: the capacity of officers at the country desks to process themessages that would come off the line printers. A deeper analysis would haveshown that the real bottleneck in the process was the time and attention of thehuman decision makers who had to use the incoming information. Identificationof the bottleneck would have generated in turn a more sophisticated design
problem: How can incoming messages during a crisis be filtered in such a waythat important information will have priority and will come to the attention of thedecision makers, while unimportant information will be shunted aside until thecrisis is past? Stated in this way, the design problem is not an easy

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one, but if a solution is found, even a partial one, it will at least tend to alleviate
the real problem instead of aggravating it.
This is not an isolated example. The first generation of management information
systems installed in large American companies were largely judged to havefailed because their designers aimed at providing more information to managers,
instead of protecting managers from irrelevant distractions of their attention. 3 A
design representation suitable to a world in which the scarce factor is information
may be exactly the wrong one for a world in which the scarce factor is attention.
As of the mid-1990s the lesson has still not been learned. An "information
superhighway" is proclaimed without any concern about the traffic jams it can
produce or the parking spaces it will require. Nothing in the new technology
increases the number of hours in the day or the capacities of human beings toabsorb information. The real design problem is not to provide more information
to people but to allocate the time they have available for receiving information sothat they will get only the information that is most important and relevant to thedecisions they will make. The task is not to design information-distributing
systems but intelligent information-filtering systems.4
Representations Without Numbers
Many of the formal planning tools available to us call for representation of thedesign problem in quantitative form. Bayesian decision analysis, for example,
requires that numerical utilities and "prior" probabilities be assigned to thepossible decision outcomes and that "posterior" probabilities then be calculated
for them on the basis of the estimated probability distributions of external events.
With the assigned utilities and estimated probabilities in hand, the expectedutility of each alternative can be computed and the best chosen.
Design problems often involve setting one or more parameters at values that will
be neither too high nor too low. Such problems can often be conceptualized ineconomic terms as requiring the balancing of marginal
3.

See H. A. Simon, The New Science of Management Decision (Englewood Cliffs, N.J.: PrenticeHall, 1977), chapter 4.
4.

H. A. Simon, "The Impact of Electronic Communications on Organizations, in R. Wolff (ed.),Organizing Industrial Development (Berlin: Walter de Gruyter, 1986).

Page 145

benefits against marginal costs. Consider, for example, the task of regulating
automobile emission standards.5 The problem can be represented rationally as
follows: (1) the quantity of emissions is a function of the number of cars, how far
they are driven, and their design (hence cost); (2) the quality of air is a functionof the level of emissions and of various geographical and meteorologicalparameters; (3) effects on human health depend on the quality of the air and the
population exposed to it. An appropriate juxtaposition of these three functions
produces a relation in which health is the dependent variable and the cost ofautomobiles the independent variable. If a dollar value is now assigned to healtheffects, all the ingredients will be present for a straightforward cost-benefit
analysis of emission standards.
It is only necessary to state the problem in this way to show the preposterousness
of attempting such calculations. Nevertheless, when this problem was presented
to the National Academy of Sciences not because it was solvable, but because
the Congress had to make a decision about emission standards the conceptual
scheme for cost-benefit analysis proved to be an excellent representation fororganizing the subcommittees of experts who were asked to contribute theiradvice. One subcommittee, mainly of engineers, examined the cost ofredesigning cars to reduce emissions. A second committee, experts in
atmospheric chemistry and meteorology, analysed the relations between
emissions and air quality. A whole set of committees of medical experts
reviewed the evidence on the health effects of the principal pollutants. Yetanother committee, staffed by economists, undertook to make estimates of thevalues that should be assigned to health effects.
None of these committees was able to arrive at estimates that were believable inmore than an order-of-magnitude sense, unless they were the estimates of auto
costs which might have been accurate within a factor of two. In general the
medical committees were unwilling or unable to make any marginal estimates atall, confining themselves to finding threshold levels of air quality at which thehealth effects of pollutants were
5.

Coordinating Committee on Air Quality Studies, National Academy of Sciences and National
Academy of Engineering, Air Quality and Automobile Emission Control, Vols. 1 4, no. 93 23(Washington: Government Printing Office, 1974).

Page 146

detectable. Given these kinds of findings and assessments, there was no way inwhich the hypothetical cost-benefit analysis scheme could be applied literally.
Nevertheless the scheme provided a conceptual framework in which the findings
could be related to each other, and in which the coordinating committee that had
to assemble the pieces of the puzzle could judge the reasonableness of proposed
standards. Even in this complex setting reasonable men could set upper andlower bounds on emission levels that, if not dictated by the evidence, at least
were consistent with it. If optimizing was out of the question, the framework
allowed the committee to arrive at a satisficing decision that was not outrageous
or indefensible.
One may regard "defensibility" as a weak standard for a decision on a matter as
consequential as automobile emissions. But it is probably the strictest standard
we can generally satisfy with real-world problems of this complexity. Even insituations of this kind (perhaps it would be better to say "especially in situationsof this kind") an appropriate representation of the problem may be essential toorganizing efforts toward solution and to achieving some kind of clarity about
how proposed solutions are to be judged. Numbers are not the name of this gamebut rather representational structures that permit functional reasoning, however
qualitative it may be.
Data for PlanningIf, given a good problem representation, rational analysis can sometimes be
carried out even in the absence of most of the relevant numbers, still we should
not make a virtue of this necessity. The quality of design is likely to depend
heavily on the quality of the data available. The task is not to design without data
but to incorporate assessments of the quality of the data, or its lack of quality, inthe design process itself. Setting automobile emission standards may call for adifferent approach to data than calculating the optimal profile for an airplanewing.
What paths are open to us when we must plan in the face of extremely poor data?
One minimal strategy, which scientists have generally followed for several
hundred years but planners sometimes ignore, is to associate

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with every estimated quantity a measure of its precision. Labeling estimates inthis way does not make them more reliable, but it does remind us how hard or
soft they are and hence how much trust to place in them.
Prediction
Data about the future predictions are commonly the weakest points in our armorof fact. Good predictions have two requisites that are often hard to come by.
First, they require either a theoretical understanding of the phenomena to bepredicted, as a basis for the prediction model, or phenomena that are sufficiently
regular that they can simply be extrapolated. Since the latter condition is seldom
satisfied by data about human affairs (or even about the weather), our predictions
will generally be only as good as our theories.
The second requisite for prediction is having reliable data about the initialconditions the starting point from which the extrapolation is to be made. Systems
vary in the extent to which their paths are sensitive to small changes in initialconditions. Weather prediction is difficult in good part because the course of
meteorological events is highly sensitive to the details of initial conditions. We
have every reason to think that social phenomena are similarly sensitive.
Since the consequences of design lie in the future, it would seem that forecastingis an unavoidable part of every design process. If that is true, it is cause for
pessimism about design, for the record in forecasting even such "simple"
variables as population is dismal. If there is any way to design without forecasts,
we should seize on it.
Consider the much-discussed Club of Rome report, which predicted a twentyfirst century doomsday of overpopulation, resource exhaustion, and famine. 6
Since the specifics of the model used to make the Club of Rome predictions havealready been much criticized, I don't have to examine those specifics here. My
point is a broader one. The Club of Rome report predicted both too much and too
little. It predicted too much, because its specific doomsday dates are notbelievable, and if believable, would not be important. We do not want to know
when disaster is going
6.

Donella Meadows et al., The Limits to Growth (N.Y.: Universe Books, 1972).

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to strike but how to avoid it. Without any specific predictions we know that asystem with exponential population growth and limited resources will sooner orlater come to some bad end. For planning purposes we wish only to have somesense of the time scale of events, to know at least whether we are talking about
years, decades, generations, or centuries. For most design purposes that is as
much prediction as we need.
The Club of Rome report predicted too little because it emphasized a single
possible time path rather than focusing upon alternative futures. The heart of thedata problem for design is not forecasting but constructing alternative scenarios
for the future and analyzing their sensitivity to errors in the theory and data.
What is said here about environmental modeling applies as well to efforts aimed
specifically at modeling climate change caused by global warming. Predicting
the exact course of global warming is a thankless task. Much more feasible anduseful is generating alternative policies which can be introduced at theappropriate times for slowing the warming, mitigating its unfavorable effects andtaking advantage of favorable effects.7
How can we go about designing an acceptable future for the energy andenvironmental needs of a society? First, we select some planning horizons:
perhaps five years for short-term plans, a generation for middle-term plans, and a
century or two for long-term plans. There is no need to construct detailed
forecasts for each of these time perspectives. Instead we can concentrate ouranalytic resources on examining alternative target states for the system for the
short, middle, and long run. By a target state I mean upper bounds on thequantities of energy used and pollutants produced. Having chosen a desirable (oracceptable) target state, and having satisfied ourselves that its realizability is not
unduly sensitive to unpredictables, we can then turn our attention to constructing
paths that lead from the present to that desired future.
Design for distant futures would be wholly impossible if remote events had to beenvisioned in detail. What makes such design even conceivable is that we needto know or guess about the future only enough to guide the commitments wemust make today. Future contingencies that have no7.

H. A. Simon, "Prediction and Prescription in Systems Modeling," Operations Research,
38(1990):7 14.

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implications for present commitment have no relevance to design. I will havemore to say on this point presently.
Feedback
Few of the adaptive systems that have been forged by evolution or shaped by
man depend on prediction as their main means for coping with the future. Twocomplementary mechanisms for dealing with changes in the external
environment are often far more effective than prediction: homeostaticmechanisms that make the system relatively insensitive to the environment andretrospective feedback adjustment to the environment's variation.
Thus a stock of inventories permits a factory to operate without concern for very
short-run fluctuations in product orders. Energy storage in the tissues of apredator enables it to cope with uncertainties in the availability of prey. A
modest excess of capacity in electric generating plants avoids the need for
precise estimation of peak loads. Homeostatic mechanisms are especially usefulfor handling short-range fluctuations in the environment, hence for making shortrange prediction unnecessary.
Feedback mechanisms, on the other hand, by continually responding todiscrepancies between a system's actual and desired states, adapt it to long-range
fluctuations in the environment without forecasting. In whatever directions theenvironment changes, the feedback adjustment tracks it, with of course somedelay.
In domains where some reasonable degree of prediction is possible, a system's
adaptation to its environment can usually be improved by combining predictive
control with homeostatic and feedback methods. It is well known in control
theory, however, that active, feed forward control, using predictions, can throw asystem into undamped oscillation unless the control responses are carefully
designed to maintain stability. Because of the possible destabilizing effects oftaking inaccurate predictive data too seriously, it is sometimes advantageous toomit prediction entirely, relying wholly on feedback, unless the quality of thepredictions is high.88.

The design of dynamic programming schemes that use a combination of prediction and feedback tocontrol factory systems is discussed in Holt, Modigliani, Muth, and Simon, Planning, Production,
Inventories, and Work Force (Englewood Cliffs, N.J., Prentice-Hall, 1960).

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Who is the Client?
It may seem peculiar to ask, "Who is the client?" when speaking of the design oflarge social systems. The question need not be raised about smaller-scale designtasks, since the answer is built into the definitions of the professional roles ofdesigners. At micro social levels of design it is tacitly assumed that the
professional architect, attorney, civil engineer, or physician works for a specifiedclient and that the needs and wishes of the client determine the goals of theprofessional's work. In this model of professional activity the architect designs ahouse that meets the living requirements of the client, while the physician plans acourse of treatment for the patient's ailments. Although in practice matters are
not so simple, this definition of the professional role greatly facilitates thedevelopment of technologies for each of the professions, for it means thatconsequences going beyond the client's goals don't have to enter into the design
calculations. The architect need not decide if the funds the client wants to spend
for a house would be better spent, from society's standpoint, on housing for lowincome families. The physician need not ask whether society would be better off
if the patient were dead.
Thus the traditional definition of the professional's role is highly compatible withbounded rationality, which is most comfortable with problems having clear-cut
and limited goals. But as knowledge grows, the role of the professional comesunder questioning. Developments in technology give professionals the power toproduce larger and broader effects at the same time that they become moreclearly aware of the remote consequences of their prescriptions.
In part this complication in the professional's role comes about simply as a direct
by-product of the growth of knowledge. Whether through the modification of
professional norms or through direct intervention of government, newobligations are placed on the professional to take account of the external effects
the consequences beyond the client's concern that are produced by the designs.
These same developments cause the professional to redefine the concept of theclient. The psychiatrist working with an individual patient becomes a family
counselor. The engineer begins to take into account the environmental impact of
new products. Finally, as the society and its central

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government take on a wider range of responsibilities, more and more
professionals find they no longer serve individual clients but are employed
directly by agencies of the state. Almost all of the professions today are
undergoing self-examination as they experience the pressures generated by thesecomplications in their roles. Architecture, medicine, and engineering all exhibit
the stresses engendered by this process.
Professional-Client Relations
Architects are especially conflicted for several reasons. First, they have always
assigned themselves the dual role of artist and professional, two roles that often
make inconsistent demands. As artists they wish to realize esthetic goals that
may be quite independent of clients' expressed or understood desires. If a clientcomports himself as an (idealized) Renaissance patron, there may be no
difficulty, for the patron does not impose his views of beauty on the artist. But if
the client's approach to building takes a more utilitarian bent, and he is notwilling to sacrifice what he conceives as usefulness for what the architect
conceives as beauty, then the relation between them may be tainted with mistrustand deception. At best, the architect becomes teacher and advocate, not simple
executor of the client's purposes.
I once asked Mies van der Rohe, then my faculty colleague at Illinois Institute ofTechnology, how he got the opportunity to build the Tugend-hat house astartlingly modern design at the time of its construction. The prospective owner
had come to Mies after seeing some of the quite conventional houses he had
earlier designed in the Netherlands when he was still an apprentice. ''Wasn't theclient shocked," I asked, "when you put before him your glass and metal
design?" "Yes," said Mies, viewing the tip of his cigar reflectively, "he wasn't
very happy at first. But then we smoked some good cigars, . . . and we dranksome glasses of a good Rhein wine, . . . and then he began to like it very much."
A second and increasingly acute problem for architects is that, when they take on
the task of designing whole complexes or areas instead of single buildings, theirprofessional training does not provide them with clear design criteria. In city
planning, for example, the boundary between the design of physical structures
and the design of social systems dissolves almost completely. Since there is little
in the knowledge base or portfolio

Page 152of techniques of architecture that qualifies the professional to plan such social
systems, the approach to the design tends to be highly idiosyncratic, reflecting
little that can be described as professional consensus, and even less that can be
described as empirically based analytic technique.
In the medical profession the stresses take slightly different forms. The firstarises from the resource allocation problem balancing the cost of medical careagainst its quality. Traditionally patients got such care as they could afford, or as
the doctor could afford to provide for them one can look at it either way. Todaywith indirect channels of payment for most medical services, budget constraints
are harder to define and monitor, and ethical choices have to be made explicitlythat formerly were made tacitly.
The second stress in the design of medical care and treatments derives from theadvance of medical technology, which gives the physician a degree of control
over life and death that is vastly greater than in the past. And so the traditionalview, which opts unconditionally for life, no longer remains unquestioned. Even
harder questions arise with new technical means for modifying genetic processes
and for manipulating the mind. In the traditional professional-client relation, theclient's needs and wants are given. The environment (including the functioningof the body) is to be adapted to the client's goals, not the goals to theenvironment. Yet much utopian thought has conceived of change in both
directions. Society was to be made more fit for human habitation, but the humaninhabitants were also to be modified to make them more fit for society. Today weare deeply conflicted about how far we should go in "improving" human beings
involuntarily. The movie The Clockwork Orange states the conflict dramaticallyby asking whether we are justified in destroying the capability for willful action
even to prevent viciousness.
The case of the engineer presents yet another aspect of the problems that growing
technical power and growing awareness of remote consequences bring. Most
engineering is done within the context of business and governmentalorganizations. In this environment there is continuing potential for conflict
between the decision criteria defined by the profession and those enforced by theorganization. In the hypothetical business firms of the pure theory ofcompetition, discussed in chapter 2, organizational criteria would prevail. In the
more complex world in which we

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actually live, the professional engineers possess substantial discretion to give
professional considerations priority over the goals of the organization. If theychoose to exercise that discretion, they must decide who the client is. In
particular they must decide which of the positive and negative externalities towhich the artifacts they are designing will give rise should be incorporated in thedesign criteria.
Society As the Client
It may seem obvious that all ambiguities should be resolved by identifying theclient with the whole society. That would be a clear-cut solution in a world
without conflict of interest or uncertainty in professional judgment. But whenconflict and uncertainty are present, it is a solution that abdicates organizedsocial control over professionals and leaves it to them to define social goals andpriorities. If some measure of control is to be maintained, the institutions of thesociety must share with the professional the redefinition of the goals of design.
The client seeks to control professionals not only by defining their goals ofdesign but also by reacting to the plans they propose. It is well known thatphysicians' patients fail to take much of the medicine that is prescribed for them.
Society as client is no more docile than are medical patients. In any planning
whose implementation involves a pattern of human behavior, that behavior must
be motivated. Knowledge that "it is for your own good" seldom provides
adequate motivation.
The members of an organization or a society for whom plans are made are notpassive instruments, but are themselves designers who are seeking to use thesystem to further their own goals. Organization theory deals with thismotivational question by examining organizations in terms of the balance
between the inducements that are provided to members to perform theirorganizational roles and the contributions that the members thereby provide to
the achievement of organizational goals.9
A not dissimilar representation of the social planning process views it as a gamebetween the planners and those whose behavior they seek to influence. The
planners make their move (i.e., implement their design),
9.

The notion of organizational survival and equilibrium depending on the balance of inducements
and contributions is due to Chester I. Barnard, The Functions of the Executive (Cambridge: Harvard
University Press, 1938).

Page 154and those who are affected by it then alter their own behavior to achieve theirgoals in the changed environment. The gaming aspects of social planning areparticularly evident in the domain of economic stabilization policies, where theadaptive response of firms and consumers to monetary and fiscal policies maylargely neutralize or negate those policies. The claims of monetarists, andespecially of the "rational expectations" theorists, that government is helpless toinfluence employment levels by using the standard Keynesian tools of monetaryand fiscal policy and that attempts to reduce unemployment can only cause
inflation, are based on the assumption that public responses to these measures
will be strongly and rapidly adaptive.
Except for economics it is still relatively rare for social planning and policy
discussions to include in any systematic way the possible "gaming" responses toplans. For example, until quite recently it was common to design new urban
transit facilities without envisioning the possible relocations of population within
the urban area that would be produced by the new facilities themselves. Yet such
effects have been known and observed for half a century. Social planningtechniques need to be expanded to encompass them routinely.
Organizations in Social Design
In introducing the subject of social design, I used the Constitution of the UnitedStates as an example. Configuring organizations, whether business corporations,
governmental organizations, voluntary societies or others, is one of society's
most important design tasks. If we human beings were isolated monads small,hermetically sealed particles that had no mutual relations except occasionalelastic collisions we would not have to concern ourselves with the design oforganizations. But, contrary to libertarian rhetoric, we are not monads. Frombirth until death, our ability to reach our goals, even to survive, is tightly linked
to our social interactions with others in our society.
The rules imposed upon us by organizations the organizations that employ us andthe organizations that govern us restrict our liberties in a variety of ways. But
these same organizations provide us with opportunities for reaching goals andattaining freedoms that we could not evenPage 155

imagine reaching by individual effort. For example, almost everyone who will
read these lines has an income that is astronomical by comparison with the world
average. If we were to assign a single cause to our good fortune, we would haveto attribute it to being born in the right place at the right time: in a society that isable to maintain order (through public organizations), to produce efficiently(largely through business organizations), and to maintain the infrastructure
required for high production (again largely through public organizations). We
have even discovered, in our society and a modest number of others, how todesign organizations, business and governmental, that do not interfere
egregiously with our freedoms, including those of speech and thought.
This is not the place to enter into a long disquisition on organizational design,private and public, which has a large literature of its own.10 But one can hardly
pass by governments and business firms in complete silence in a chapter on thedesign of social structures. A society's organizations are matters not only of
specialized professional concern but of broad public concern.
Today, organizations, and especially governmental organizations, have an
exceedingly bad press in our society. The terms "politician" and "bureaucrat" arenot used as descriptors but as pejoratives. While the events in Oklahoma City
surely did not evoke public approval, the general horrified reaction was not to theanti-governmental attitudes that the bombing expressed but to the killings. Thereis more than a little anarchism (usually phrased as libertarianism) in the current
American credo (and for that matter, in our credo since the time of the Founding
Fathers).
Organizational design, then, is a matter for urgent attention in any curriculum onsocial design. Organizations are exceedingly complex systems that share many
properties with other complex systems for example, their typically hierarchicalstructure. Questions of organizational design
10.

My views on some of these matters have been expounded at length in H. A. Simon, Administrative
Behavior, 3rd ed., (New York, NY: The Free Press, 1976); H. A. Simon, V. A. Thompson and D. W.
Smithburg, Public Administration (New Brunswick, NJ: Transaction Publishers, 1991); and J. G.
March and H. A. Simon, Organizations, 2nd ed., (Cambridge, MA: Blackwell, 1993). On the natureof business organizations, and especially the role of organizational identification in maintaining
them, see chapter 2 of the present volume.

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will reappear, from time to time, as part of the discussion of complex systems inchapters 7 and 8, below, and especially in connection with the use of hierarchyand "near decomposability" as a basis for specialization.
Time and Space Horizons for Design
Each of us sits in a long dark hall within a circle of light cast by a small lamp.
The lamplight penetrates a few feet up and down the hall, then rapidly attenuates,
diluted by the vast darkness of future and past that surrounds it.
We are curious about that darkness. We consult soothsayers and forecasters of
the economy and the weather, but we also search backward for our "roots." Someyears ago I conducted such a search in the Rheinland villages near Mainz where
my paternal ancestors had lived. I found records of grandparents readily enough,and even of great-grandparents and beyond. But before I had gone far scarcely
back to the 18th century I came to the edge of the circle of light. Darkness closed
in again in the little towns of Ebersheim, Woerstadt, and Partenheim, and I could
see no farther back.
History, archaeology, geology, and astronomy provide us with narrow beams thatpenetrate immense distances down the hallway of the past but illuminate it only
fit fully a statesman or philosopher here, a battle there, some hominoid bones
buried with pieces of chipped stone, fossils embedded in ancient rock, rumors ofa great explosion. We read about the past with immense interest. A few spots
caught by the beams take on a vividness and immediacy that capture, for a
moment, our attention and our hearts some Greek warriors camped before Troy,a man on a cross, the painted figure of a deer glimpsed by flickering torchlighton the wall of a limestone cave. But mostly the figures are shadowy, and ourattention shifts back to the present.
The light dims even more rapidly in the opposite direction, toward the future.
Although we are titillated by Sunday Supplement descriptions of a cooling Sun,
it is our own mortality, just a few years away, and not the Earth's, with which weare preoccupied. We can empathize with parents and grandparents whom wehave known, or of whom we have had first-hand accounts, and in the opposite
direction with children and grandchil-Page 157

dren. But beyond that circle our concern is more curious and intellectual thanemotional. We even find it difficult to define which distant events are thetriumphs and which the catastrophes, who the heroes and who the villains.
Discounting the FutureThus the events and prospective events that enter into our value systems are all
dated, and the importance we attach to them generally drops off sharply with
their distance in time. For the creatures of bounded rationality that we are, this is
fortunate. If our decisions depended equally upon their remote and theirproximate consequences, we could never act but would be forever lost inthought. By applying a heavy discount factor to events, attenuating them with
their remoteness in time and space, we reduce our problems of choice to a sizecommensurate with our limited computing capabilities. We guarantee that, when
we integrate outcomes over the future and the world, the integral will converge.
Socio biologists, in their analyses of egoism and altruism, undertake to explain
how the forces of evolution would necessarily produce organisms more
protective of their offspring and their kin than of unrelated creatures. Thisevolutionary account does not explain, however, why the concern tends to be so
myopic with respect to the future. At least one part of the explanation is that weare unable to think coherently about the remote future, and particularly about the
distant consequences of our actions. Our myopia is not adaptive, but
symptomatic of the limits of our adaptability. It is one of the constraints on
adaptation belonging to the inner environment.
The economist expresses this discounting of the future by a rate of interest. To
find the present value of a future dollar, he applies, backwards, a compound
discount rate that shrinks the dollar by a fixed percentage for each step from thepresent. Even a moderate rate of interest can make the dollars of the next century
look quite inconsequential for our present decisions. There is a vast literatureseeking to explain, none too convincingly, what determines the time rate ofdiscount used by savers. (In modern times it has hovered remarkably steadily
around 3 percent per annum, after appropriate adjustment for risk and inflation.)
There is also a considerable literature seeking to determine what the social rate ofinterest

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should be what the rate of exchange should be between the welfare of thisgeneration and the welfare of its descendants.
The rate of interest should not be confused with another factor that discounts theimportance of the future with respect to the present. Even if we are aware ofcertain unfavorable events that will occur in the distant future, there may benothing to be done about them today. If we knew that the wheat harvest wasgoing to fail in the year 2020, we would be ill-advised to store wheat now. Our
unconcern with a distant future is not merely a failure of empathy but arecognition that (1) we shall probably not be able to foresee and calculate theconsequences of our actions for more than short distances into the future and (2)those consequences will in any case be diffuse rather than specific.
The important decisions we make about the future are primarily the decisions
about spending and saving about how we shall allocate our production between
present and future satisfactions. And in saving, we count flexibility among theimportant attributes of the objects of our investment, because flexibility insuresthe value of those investments against the events that will surely occur but which
we cannot predict. It will (or should) bias our investments in the direction of
structures that can be shifted from one use to another, and to knowledge that isfundamental enough not soon to be outmoded knowledge that may itself provide
a basis for continuing adaptation to the changing environment.
The Change in Time Perspective
One of the noteworthy characteristics of our century is the shift that appears to be
taking place, especially in the industrialized world, in our time perspectives. For
example, embedded in the energy-environment problem that confronts us today,
we can see three almost independent aspects. The first is our immediate
dependence on petroleum, which we must reduce to protect ourselves frompolitical blackmail and to achieve a balance of international payments. The
second is the prospect of the exhaustion of oil and gas supplies, a problem thatmust be solved within about a generation, mostly by the use of coal and nuclear
energy. The third is the joint problem of the exhaustion of fossil fuels and theimpact of their combustion on the climate. The time scale of this third problem isa century or so.

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What is remarkable in our age, and relatively novel I believe, is the amount ofattention we pay to the third problem. Perhaps it is just that we have all three
confused in our minds and have not sorted them out to the point where we canthink about the more pressing ones without concern for the other. But I do notthink that is the reason. I believe there has been a genuine downward shift in thesocial interest rate we apply to discount events that are remote in time and space.
There are some obvious reasons for our new concern with matters that are remotein time and space. Among these are the relatively new facts of instantaneous
worldwide communication and rapid air transportation. Consequent on these is
the continually increasing economic and military interdependence of all thenations. More subtle than either of these causes is the progress of humanknowledge, especially of science. I have already commented on the way in whicharchaeology, geology, anthropology, and cosmology have lengthened ourperspectives. But in addition new laboratory technologies have vastly increased
our ability to detect and assess small and indirect effects of our actions. Oscar
Wilde once claimed that there were no fogs on the River Thames until Turner, by
painting them, revealed them to the residents of London. In the same way ouratmosphere contained no noxious substances in quantities of a few parts per
million until chromatography and other sensitive analytic techniques showed
their presence and measured them. DDT was an entirely beneficent insecticide
until we detected its presence in falcons' eggs and in fish. If eating the apple
revealed to us the nature of good and evil, modern analytic tools have taught us
how to detect good and evil in minute amounts and at immense distances in timeand space.
It may be objected that there has been no such lengthening of social time
perspectives as I have claimed. What perspective can be longer than the eternityof life after death that is so central to Christian thought, or longer than therepeated reincarnations of Eastern religions? But the attitudes toward the futureengendered by those beliefs are very different from the ones I have been
discussing. The future with which the Christian is concerned is his own future in
the light of his current conduct. There is nothing in the belief in an afterlife or areincarnation that calls attention to the future consequences for this world of
one's present actions. NorPage 160

do I find in those religious beliefs anything resembling the contemporary concern
for the fragility of the environment on which human life depends or for the
power of human actions to make that environment more or less habitable in the
future. It does appear therefore that there has been a genuine shift in ourorientation to time and a significant lengthening in time perspectives.
Defining Progress
As the web of cause and effect is woven tighter, we put severe loads upon ourplanning and decision-making procedures to deal with these remote effects.
There is a continuing race between the part of our new science and knowledge
that enables us to see more distant views and the part that enables us to deal withwhat we see. And if we live in a time that is sometimes pessimistic about
technology, it is because we have learned to look farther than our arms can reach.
Defining what is meant by progress in human societies is not easy. Increasing
success in meeting basic human needs for food, shelter, and health is one kind of
definition that most people would agree upon. Another would be an averageincrease in human happiness. With the advance of productive technology, we canclaim that there has been major progress by the first criterion; but what has been
said in chapter 2 about changing aspiration levels would lead us to doubt whether
progress is possible if we use the second criterion, human happiness, to measure
it. There is no reason to suppose that a modern industrial society is more
conducive to human happiness than the simpler, if more austere, societies thatpreceded it. On the other hand, there seems to be little empirical basis for thenostalgia that is sometimes expressed for an imagined (and imaginary) happier ormore humane past.
A third way of measuring progress is in terms of intentions rather than out comeswhat might be called moral progress. Moral progress has always been associatedwith the capacity to respond to universal values to grant equal weight to theneeds and claims of all mankind, present and future. It can be argued that thegrowth of knowledge of the kinds I have been describing represents such moral
progress.
But we should not be hasty in our evaluation of the consequences of lengthening
perspectives in space or time. The present century is not lack-Page 161

sing in horrible examples of man's inhumanity to man. We must be alert also to
the possibility that rationality applied to a broader domain will simply be a morecalculatedly rational selfishness than the impulsive selfishness of the past.
The Management of Attention
From a pragmatic standpoint we are concerned with the future because securing
a satisfactory future may require actions in the present. Any interest in the futurethat goes beyond this call for present action has to be charged to pure curiosity. It
belongs to our recreational rather than our working day. Thus our present
concern for the short-run energy problem is quite different from our concern forthe long-run problem or even the middle-run problem. The actions we have totake today, if we are to improve the short-run situation, are largely actions thatwill reduce our use of energy there are only modest prospects of a substantial
short-run increase in supply. The actions we have to take with respect to the
middle-run problem are largely actions on a large scale toward the development
and exploitation of some mix of technologies for the conversion of coal, miningof oil sands and shales, and safe nuclear fission or fusion. The principal actions
we can take now with respect to the long-range energy problem are primarily
knowledge-acquiring actions research programs to develop nuclear fusion andsolar technologies and to gain a deeper understanding of the environmental
consequences of all the alternatives.
The energy problem is rather typical in this respect of large-scale design
problems. In addition to the things we can do to produce immediate
consequences, we must anticipate the time lags involved in developing newcapital plant and the even greater time lags involved in developing the body oftechnology and other knowledge that we will need in the more distant future.
Attention of the decision-making bodies has to be allocated correspondingly.
It is a commonplace organizational phenomenon that attending to the needs ofthe moment putting out fire stakes precedence over attending to the needs for
new capital investment or new knowledge. The more crowded the total agenda
and the more frequently emergencies arise, the more likely it is that the middlerange and long range decisions will be neglected. In formal organizations a
remedy is often sought for thisPage 162

condition by creating planning groups that are insulated in one way or anotherfrom the momentary pressures upon the organization. Planning units face two
hazards. On the one hand, and especially if they are competently staffed, they
may be consulted more and more frequently for help on immediate problems
until they are sucked into the operating organization and can no longer performtheir planning functions. If they are sufficiently well sealed off from the rest of
the organization to prevent this from happening, then they may find the reverse
channel blocked they may be unable to influence decisions in the operatingorganization. There is no simple or automatic way to remove these difficulties
once and for all. They require repeated attention from the organization's
leadership.
Designing Without Final Goals
To speak of planning without goals may strike one as a contradiction in terms. 11
It seems ''obvious" that the very concept of rationality implies goals at whichthought and action are aimed. How can we evaluate a design unless we havewell-defined criteria against which to judge it, and how can the design processitself proceed without such criteria to guide it?
Some answer has already been given to these questions in chapter 4, in thediscussion of discovery processes. We saw there that search guided by only themost general heuristics of "interestingness" or novelty is a fully realizable
activity. This kind of search, which provides the mechanism for scientificdiscovery, may also provide the most suitable model of the social design process.
It is generally recognized that in order to acquire new tastes in music, a goodprescription is to hear more music; in painting, to look at paintings; in wine, todrink good wines. Exposure to new experiences is almost certain to change thecriteria of choice, and most human beings deliberately seek out such experiences.
A paradoxical, but perhaps realistic, view of design goals is that their function is
to motivate activity which in turn will generate new goals. For example, whenabout fifty years ago an extensive renewal program was
11.

This section owes much to James G. March, who has thought deeply on these lines. See his"Bounded Rationality, Ambiguity, and the Engineering of Choice," Bell Journal of Economics
9(1978):587 608.

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begun in the city of Pittsburgh, a principal goal of the program was to rebuild thecenter of the city, the so-called Golden Triangle. Architects have had much tosay, favorable and unfavorable, about the esthetic qualities of the plans that werecarried out. But such evaluations are largely beside the point. The mainconsequence of the initial step of redevelopment was to demonstrate thepossibility of creating an attractive and functional central city on this site, ademonstration that was followed by many subsequent construction activities thathave changed the whole face of the city and the attitudes of its inhabitants.
It is also beside the point to ask whether the later stages of the development wereconsistent with the initial one whether the original designs were realized. Eachstep of implementation created a new situation; and the new situation provided a
starting point for fresh design activity.
Making complex designs that are implemented over a long period of time andcontinually modified in the course of implementation has much in common withpainting in oil. In oil painting every new spot of pigment laid on the canvas
creates some kind of pattern that provides a continuing source of new ideas to thepainter. The painting process is a process of cyclical interaction between painterand canvas in which current goals lead to new applications of paint, while thegradually changing pattern suggests new goals.
The Starting Point
The idea of final goals is inconsistent with our limited ability to foretell or
determine the future. The real result of our actions is to establish initialconditions for the next succeeding stage of action. What we call "final" goals arein fact criteria for choosing the initial conditions that we will leave to our
successors.
How do we want to leave the world for the next generation? What are goodinitial conditions for them? One desideratum would be a world offering as many
alternatives as possible to future decision makers, avoiding irreversible
commitments that they cannot undo. It is the aura of irreversibility hanging about
so many of the decisions of nuclear energy deployment that makes thesedecisions so difficult.
A second desideratum is to leave the next generation of decision makers with abetter body of knowledge and a greater capacity for experience.

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The aim here is to enable them not just to evaluate alternatives better but
especially to experience the world in more and richer ways.
Becker and Stigler have argued that considerations of the sort I have been
advancing can be accommodated without giving up the idea of fixed goals. 12 Allthat is required, they say, is that the utilities to be obtained from actions be
defined in sufficiently abstract form. In their scheme the utility yielded by anhour's listening to music increases with one's capacity for musical enjoyment,
and this capacity is a kind of capital that can be increased by a prior investment
in musical listening. While I find their way of putting the matter a trifle
humorless, perhaps it makes the idea of rational behavior without goals lessmysterious. If we conceive human beings as having some kind of alterable
capacity for enjoyment and appreciation of life, then surely it is a reasonable goalfor social decision to invest in that capacity for future enjoyment.
Designing As Valued Activity
Closely related to the notion that new goals may emerge from creating designs is
the idea that one goal of planning may be the design activity itself. The act ofenvisioning possibilities and elaborating them is itself a pleasurable and valuable
experience. Just as realized plans may be a source of new experiences, so newprospects are opened up at each step in the process of design. Designing is a kindof mental window shopping. Purchases do not have to be made to get pleasurefrom it.
One of the charges sometimes laid against modern science and technology is that
if we know how to do something, we cannot resist doing it. While one can thinkof counterexamples, the claim has some measure of truth. One can envisage afuture, however, in which our main interest in both science and design will lie inwhat they teach us about the world and not in what they allow us to do to theworld. Design like science is a tool for understanding as well as for acting.
12.

G. J. Stigler and G. S. Becker, "De Gustibus non est Disputandum," American Economic Review,
67(1977):76 90.

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Social Planning and Evolution
Social planning without fixed goals has much in common with the processes of
biological evolution. Social planning, no less than evolution, is myopic. Looking
a short distance ahead, it tries to generate a future that is a little better (read"fitter") than the present. In so doing, it creates a new situation in which theprocess is then repeated. In the theory of evolution there are no theorems thatextract a long-run direction of development from this myopic hill climbing. In
fact evolutionary biologists are extremely wary of postulating such a direction orof introducing any notion of "progress." By definition the fit are those who
survive and multiply.
Whether there is a long-run direction in evolution, and whether that direction is
to be considered progress are of course two different questions. We might answer
the former affirmatively but the latter negatively. Let me venture a speculation
about the direction of social and biological evolution, which I will develop
further in the next two chapters. My speculation is emphatically not a claim
about progress.
From a reading of evolutionary history whether biological or social one might
conjecture that there has been a long-run trend toward variety and complexity.
There are more than a hundred kinds of atoms, thousands of kinds of inorganic
molecules, hundreds of thousands of organic molecules, and millions of speciesof living organisms. Mankind has elaborated several thousand distinct languages,
and modern industrial societies count their specialized occupations in the tens of
thousands.
I shall emphasize in the following chapters that forms can proliferate in this way
because the more complex arise out of a combinatoric play upon the simpler. The
larger and richer the collection of building blocks that is available forconstruction, the more elaborate are the structures that can be generated.
If there is such a trend toward variety, then evolution is not to be understood as aseries of tournaments for the occupation of a fixed set of environmental niches,
each tournament won by the organism that is fittest for that niche. Instead
evolution brings about a proliferation of niches. The environments to which most
biological organisms adapt are formed mainly of other organisms, and theenvironments to which human beings

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adapt, mainly of other human beings. Each new bird or mammal provides a nichefor one or more new kind of flea.
Vannevar Bush wrote of science as an "endless frontier." It can be endless, as
can be the process of design and the evolution of human society, because there isno limit on diversity in the world. By combinatorics on a few primitive elements,
unbounded variety can be created.
The Curriculum for Social Design
Our examination of the social planning process here suggests some extension of
the curriculum for design that was proposed in the last chapter. Topic 7, therepresentation of design problems, must be expanded to incorporate the skills of
constructing organizations as frameworks for problem representation, building
representations around limiting factors, and representing non-numericalproblems. Our discussion also suggests at least six new topics for the curriculum:
1. Bounded rationality. The meaning of rationality in situations where the complexity of theenvironment is immensely greater than the computational powers of the adaptive system.
2. Data for planning. Methods of forecasting, the use of prediction and feedback in control.
3. Identifying the client. Professional-client relations, society as the client, the client asplayer in a game.
4. Organizations in social design. Not only is social design carried out mainly by people
working in organizations, but an important goal of the design is to fashion and change socialorganization in general and individual organizations in particular.
5. Time and space horizons. The discounting of time, defining progress, managing attention.
6. Designing without final goals. Designing for future flexibility, design activity as goal,
designing an evolving system.
With the exception of control theory and game theory, which are of central importance totopics 2 and 3, the design tools relevant to these additional topics are in general less formal
than those we described in the previous chapter. But whether we have the formal tools weneed or not, the topics are too crucial to the social design process to permit them to beignored or omitted from the curriculum.

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Our age is one in which people are not reluctant to express their pessimism andanxieties. It is true that humanity is faced with many problems. It always has
been but perhaps not always with such keen awareness of them as we have today.
We might be more optimistic if we recognized that we do not have to solve all ofthese problems. Our essential task a big enough one to be sure is simply to keep
open the options for the future or perhaps even to broaden them a bit by creating
new variety and new niches. Our grandchildren cannot ask more of us than that
we offer to them the same chance for adventure, for the pursuit of new andinteresting designs, that we have had.

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7
Alternative Views of Complexity
The preceding chapters of this book have discussed several kinds of artificial
systems. The examples we have examined in particular, economic systems, thebusiness firm, the human mind, sophisticated engineering designs, and social
plans range from the moderately to the exceedingly complex (not necessarily inthe order in which I have just listed them). These final two chapters address the
topic of complexity more generally, to see what light it casts on the structure andoperation of these and other large systems that are prominent in our world today.
Conceptions of Complexity
This century has seen recurrent bursts of interest in complexity and complex
systems. An early eruption, after World War I, gave birth to the term "holism,"and to interest in "Gestalts" and "creative evolution." In a second major eruption,
after World War II, the favorite terms were "information," "feedback,''"cybernetics," and "general systems." In the current eruption, complexity is often
associated with "chaos," "adaptive systems," "genetic algorithms," and "cellular
automata."
While sharing a concern with complexity, the three eruptions selected different
aspects of the complex for special attention. The post-WWII interest incomplexity, focusing on the claim that the whole transcends the sum of the parts,
was strongly anti-reductionist in flavor. The post-WWII outburst was rather
neutral on the issue of reductionism, focusing on the roles of feedback andhomeostasis (self-stabilization) in maintaining complex systems. The current
interest in complexity focuses mainly onPage 170mechanisms that create and sustain complexity and on analytic tools fordescribing and analyzing it.
Holism and Reductionism"Holism" is a modern name for a very old idea. In the words of its author, theSouth African statesman and philosopher, J. C. Smuts:[Holism] regards natural objects as wholes. . . . It looks upon nature as consisting of discrete,concrete bodies and things . . . [which] are not entirely resolvable into parts; and . . . which are morethen the sums of their parts, and the mechanical putting together of their parts will not produce themor account for their characters and behavior.1

Holism can be given weaker or stronger interpretations. Applied to living
systems, the strong claim that "the putting together of their parts will not produce
them or account for their characters and behaviors" implies a vitalism that iswholly antithetical to modern molecular biology. Applied to minds in particular,
it is used to support both the claim that machines cannot think and the claim thatthinking involves more than the arrangement and behavior of neurons. Applied
to complex systems in general, it postulates new system properties and relations
among subsystems that had no place in the system components; hence it calls for
emergence, a "creative" principle. Mechanistic explanations of emergence arerejected.
In a weaker interpretation, emergence simply means that the parts of a complex
system have mutual relations that do not exist for the parts in isolation. Thus,
there can be gravitational attractions among bodies only when two or morebodies interact with each other. We can learn something about the (relative)
gravitational accelerations of binary stars, but not of isolated stars.
In the same way, if we study the structures only of individual proteins, nothingpresages the way in which one protein molecule, serving as an enzyme, canprovide a template into which two other molecules can insert themselves and be
held while they undergo a reaction linking them. The template, a real enoughphysical property of the enzyme, has no function until it is placed in anenvironment of other molecules of a certain kind.
1. J. C. Smuts, "Holism," Encyclopaedia Britannica, 14th ed., vol. 11 (1929), p. 640.

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Even though the template's function is "emergent;" having no meaning for theisolated enzyme molecule, the binding process, and the forces employed in it,
can be given a wholly reductionist explanation in terms of the known physicochemical properties of the molecules that participate in it. Consequently, thisweak form of emergence poses no problems for even the most ardent
reductionist.
"Weak emergence" shows up in a variety of ways. In describing a complex
system we often find it convenient to introduce new theoretical terms, likeinertial mass in mechanics, or voltage in the theory of circuits, for quantities thatare not directly observable but are defined by relations among the observables. 2
We can often use such terms to avoid reference to details of the component
subsystems, referring only to their aggregate properties.
Ohm, for example, established his law of electrical resistance by constructing acircuit containing a battery that drove current through a wire, and an ammeterthat measured the magnetic force induced by the current. By changing the lengthof the wire, he altered the current. The equation relating the length of the wire
(resistance) to the force registered by the ammeter (current) contained two
constants, which were independent of the length of the wire but changed if hereplaced the battery by another. These constants were labeled the voltage andinternal resistance of the battery, which was otherwise unanalyzed and treated asa "black box." Voltage and internal resistance are not measured directly but aretheoretical terms, inferred from the measured resistance and current with the aidof Ohm's Law.
Whereas the details of components can often be ignored while studying theirinteractions in the whole system, the short-run behavior of the individual
subsystems can often be described in detail while ignoring the (slower)interactions among subsystems. In economics, we often study the interaction ofclosely related markets for example, the markets for iron ore, pig iron, sheet steel
and steel products under the assumption that all other supply and demand
relations remain constant. In the next chapter, we will discuss at length this near
independence of hierarchical2.

H. A. Simon, "The Axiomatization of Physical Theories," Philosophy of Science, 37(1970), 16 26.

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systems from the detail of their component subsystems, as well as the short-run
independence of the subsystems from the slower movements of the total system.
By adopting this weak interpretation of emergence, we can adhere (and I will
adhere) to reductionism in principle even though it is not easy (often not evencomputationally feasible) to infer rigorously the properties of the whole fromknowledge of the properties of the parts. In this pragmatic way, we can build
nearly independent theories for each successive level of complexity, but at thesame time, build bridging theories that show how each higher level can be
accounted for in terms of the elements and relations of the next level below.
This is, of course, the usual conception of the sciences as building upward fromelementary particles, through atoms and molecules to cells, organs andorganisms. The actual history, however, has unfolded, as often as not, in the
opposite direction from top down. We have already observed, in chapter 1, how
we commonly hang our scientific theories from skyhooks.
Cybernetics and General Systems Theory
The period during and just after World War II saw the emergence of what
Norbert Wiener dubbed "cybernetics": a combination of servomechanism theory(feedback control systems), information theory, and modern stored-program
computers, all of which afford bold new insights into complexity. Information
theory explains organized complexity in terms of the reduction of entropy(disorder) that is achieved when systems (organisms, for example) absorb energy
from external sources and convert it to pattern or structure. In information
theory, energy, information, and pattern all correspond to negative entropy.
Feedback control shows how a system can work toward goals and adapt to achanging environment,3 thereby removing the mystery from teleology. What isrequired is ability to recognize the goal, to detect differences between the current
situation and the goal, and actions that can reduce such differences: precisely thecapabilities embodied in a system like the General Problem Solver. Soon thisinsight was being applied to3.

A. Rosenblueth, N. Wiener and J. Bigelow, "Behavior, Purpose and Teleology," Philosophy of
Science, 10(1943), 18 24.

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constructing small robots that could maneuver around a room autonomously. 4 As
computers became available, systems could be built at levels of complexity that
had never before been contemplated; and by virtue of their capability forinterpreting and executing their own internally stored programs, computers
initiated the study of artificial intelligence.
These developments encouraged both the study of complex systems, especially
adaptive goal-oriented systems, "as wholes," and simultaneously, the reductive
explanation of system properties in terms of mechanisms. Holism was brought
into confrontation with reductionism in a way that had never been possible
before, and that confrontation continues today in philosophical discussion of
artificial systems.
During these postwar years, a number of proposals were advanced for thedevelopment of "general systems theory," that, abstracting from the special
properties of physical, biological, or social systems, would apply to all of them. 5
We might well feel that, while the goal is laudable, systems of these diverse
kinds could hardly be expected to have any nontrivial properties in common.
Metaphor and analogy can be helpful or they can be misleading. All depends onwhether the similarities the metaphor captures are significant or superficial.
If a general systems theory is too ambitious a goal, it might still not be vain tosearch for common properties among broad classes of complex systems. The
ideas that go by the name of cybernetics constitute, if not a theory, at least a
point of view that has proved fruitful over a wide range of applications.6 It has
been highly useful to look at the behavior of adaptive systems in terms of
feedback and homeostasis and to apply to these concepts the theory of selective
information.7 The concepts of feedback
4.

W. Grey Walter, "An Imitation of Life," Scientific American, 182 (5) (1950):42.

5.

See especially the yearbooks of the Society for General Systems Research. Prominent exponents ofgeneral systems theory were L. von Bertalanffy, K. Boulding, R. W. Gerard and, still active in thisendeavor, J. G. Miller.
6.

N. Wiener, Cybernetics (New York: Wiley, 1948). For an imaginative forerunner, see A. J. Lotka,
Elements of Mathematical Biology (New York: Dover Publications, 1951), first published in 1924 as
Elements of Physical Biology.
7.

C. Shannon and W. Weaver, The Mathematical Theory of Communication (Urbana: University of
Illinois Press, 1949); W. R. Ashby, Design for a Brain (New York: Wiley, 1952).

Page 174and information provide a frame of reference for viewing a wide range ofsituations, just as do the ideas of evolution, of relativism, of axiomatic method,and of operationalism.
The principal contribution of this second wave of inquiry into complexity Jay
precisely in the more specific concepts it brought to attention rather than in the
broad idea of a general systems theory. This view is illustrated in the next
chapter, which focuses on the properties of those particular complex systems thatare hierarchical in structure, and draws out the consequences for system behaviorof the strong assumption of hierarchy (or near-decomposability, as I shall call it).
Current Interest in Complexity
The current, third, burst of interest in complexity shares many of thecharacteristics of the second. Much of the motivation for it is the growing need tounderstand and cope with some of the world's large-scale systems theenvironment, for one, the world-wide society that our species has created, foranother, and organisms, for a third. But this motivation could not, by itself, tie
attention to complexity for very long if novel ways of thinking about it were notalso provided. Going beyond the tools and concepts that appeared in the second
wave, other new ideas have emerged, together with relevant mathematics andcomputational algorithms. The ideas have such labels as "catastrophe," "chaos,""genetic algorithms;" and ''cellular automata."
As always, the labels have some tendency to assume a life of their own. The
foreboding tone of "catastrophe" and "chaos" says something about the age ofanxiety in which these concepts were named. Their value as concepts, however,depends not on the rhetoric they evoke, but on their power to produce concreteanswers to questions of complexity. For the particular concepts listed above,
much of the verdict is not yet in. I want to comment briefly on each of them, forthey are both alternatives and complements to the approach to hierarchicalcomplexity that I will develop in the next chapter.

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Catastrophe Theory
Catastrophe theory appeared on the scene around 1968,8 made an audible splash,and nearly faded from public sight within a few years. It is a solid body ofmathematics dealing with the classification of nonlinear dynamic systems
according to their modes of behavior. Catastrophic events occur in a special kindof system. Such a system can assume two (or more) distinct steady states (static
equilibria, for example, or periodic cycles); but when the system is in one ofthese states, a moderate change in a system parameter may cause it to shiftsuddenly to the other or into an unstable state in which variables increase without
limit. The mathematician R. Thom constructed a topological classification of
two-variable and three-variable systems according to the kinds of catastrophes
they could or couldn't experience.
It is not hard to think of natural systems that exhibit behavior of this kind stablebehavior followed by a sudden shift to disequilibrium or to another, quite
different, equilibrium. A commonly cited example is the threatened dog thateither suddenly moves to the attack or panics and flees. More complex examples
have been studied: for instance, a budworm population infesting a spruce forest.
The rapidly reproducing budworms quickly reach an equilibrium of maximum
density; but the slow continuing growth of the spruce forest gradually alters thelimit on the budworm population until, when a critical forest density is exceeded,
the population explodes.9 One can conjure up models of human revolutions
embodying similar mechanisms.
In the circumstances that create it, the catastrophe mechanism is effective and themetaphor evocative, but in practice, only a limited number of situations havebeen found where it leads to any further analysis. Most of the initial applications
that struck public fancy (like the attacking/fleeing dog) were after-the-fact
explanations of phenomenon that were8.
9.

See R. Thom, An Outline of a General Theory of Models (Reading, MA: Benjamin, 1975).

For an account of the spruce/budworm model, see T. F. H. Allen and T. B. Starr, Ecology:
Perspectives for Ecological Complexity (Chicago, IL: University of Chicago Press, 1982), andreferences cited there. In the next chapter we will see that this same example can be described as anearly decomposable system.

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already familiar. For this reason, catastrophe theory is much less prominent in
the public eye and in the literature of complexity today than it was twenty-fiveyears ago.
Complexity and Chaos
The theory of chaos also represents solid mathematics, which in this case has along history reaching back to Poincaré.10 Chaotic systems are deterministic
dynamic systems that, if their initial conditions are disturbed eveninfinitesimally, may alter their paths radically. Thus, although they are
deterministic, their detailed behavior over time is unpredictable, for small
perturbations cause large changes in path. Chaotic systems were sufficiently
intractable to mathematical treatment that, although the subject was kept alive by
a few French mathematicians working in the tradition of Poincaré, only modest
progress was made with them until well beyond the middle of this century. A
major source of new progress has been the ability to use computers to display
and explore their chaotic behavior.
Gradually, researchers in a number of sciences began to suspect that importantphenomena they wished to understand were, in this technical sense, chaotic. Oneof the first was the meteorologist E. N. Lorenz, who started to explore in theearly 1960s the possibility that weather was a chaotic phenomenon the possibility
that the butterfly in Singapore, by flapping its wings, could cause a thunderstorm
in New York. Soon, fluid turbulence in general was being discussed in terms ofchaos; and the possible inculpation of chaos in the complex behavior of a wide
range of physical and biological systems was being studied. Solid experimental
evidence that specific physical systems do, in fact, behave chaotically began toappear in the late 1970s.11
The growth in attention to chaos must be viewed against the background of ourgeneral understanding of dynamic systems. For a long time we have had a quite
general theory of systems of linear differential equa10.
11.

H. Poincaré, Les Methodes Nouvelle de la Méchanique Céleste. (Paris: Gauthier-Villars, 1892).

An excellent selection of the literature of chaos, both mathematical and experimental, up to the
middle 1980s can be found in P. Cvitanovic * (ed.), Universality in Chaos (Bristol: Adam Hilger, 1986).

Page 177

tions and their solution in closed form. With systems of nonlinear equations,matters were less satisfactory. Under particular simple boundary conditions,solutions were known for a number of important systems of nonlinear partial
differential equations that capture the laws of various kinds of wave motion. But
beyond these special cases, knowledge was limited to methods for analyzing
local behavior qualitatively its stability or instability in order to divide the spaceof achievable states into discrete regions. In each such region, specific kinds of
behavior (e.g., movement to equilibrium, escape from unstable equilibrium,
steady-state motion in limit cycles) would occur.12
This was the bread-and-butter content of the standard textbook treatments ofnonlinear analysis, and beyond these qualitative generalizations, complex
nonlinear systems had to be studied mainly by numerical simulation withcomputers. Most of the large computers and super-computers of the past half
century have been kept busy simulating numerically the behavior of the systemsof partial differential equations that describe the dynamics of airplanes, atomic
piles, the atmosphere, and turbulent systems generally. As chaotic systems were
not typically discussed in the textbooks, the then-current theory of nonlinear
systems provided little help in treating such phenomena as turbulence except at
an aggregate and very approximate level.
Under these circumstances, new computer-generated discoveries about chaos inthe late 1970s and early 1980s created enormous interest and excitement in avariety of fields where phenomena were already suspected of being chaotic, andhence could perhaps be understood better with the new theory. Numericalcomputations on simple nonlinear systems revealed unsuspected invariants("universal numbers") that predicted, for broad classes of such systems, at what
point they would change from orderly to chaotic behavior.13 Until high-speed
computers were available to reveal them, such regularities were invisible.
12.

A standard source is A. A. Andronov, E. A. Leontovich, I. I. Gordon and A. G. Maier, Qualitative
Theory of Second-Order Dynamic Systems (Wiley, NY: 1973).
13.

M. J. Feigenbaum, "Universal Behavior in Nonlinear Systems;" Los Alamos Science, 1 (1980):4 27.
This and other "classic" papers on chaos from the 1970s and 1980s are reprinted in P. Cvitanovic *, ed.,
op. cit.

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Deep understanding has now been achieved of many aspects of chaos, but to saythat we "understand" does not imply that we can predict. Chaos led to therecognition of a new, generalized, notion of equilibrium the so-called "strange
attractor." In classical nonlinear theory a system might come to a stableequilibrium, or it might oscillate permanently in a limit cycle, like the orbit of aplanet. A chaotic system, however, might also enter a region of its state space,
the strange attractor, in which it would remain permanently.
Within the strange attractor, motion would not cease, nor would it be predictable,
but although deterministic, would appear to be random. That is, slightly different
directions of entrance into the strange attractor, or slight perturbations when in it,
would lead the system into quite different paths. A billiard ball aimed exactly at a
45° angle across a square "ideal" billiard table, will reflect off successive sides
and, returning to the starting point, repeat its rectangular path indefinitely. But if
you decrease or increase the 45° angle by an epsilon, the ball will never return tothe starting point but will pursue a path that will eventually take it as close as you
please to any spot on the table. The table's entire surface has become the strange
attractor for the chaotic behavior and almost equal but different initial angles willproduce continually diverging paths.
The theory of chaos has perhaps not maintained the hectic pace of development it
experienced from the early 1960s to the late 1980s, but during this period it
established itself as an essential conceptual framework and mathematical tool forthe study of a class of systems that have major real-world importance in a
number of scientific domains. The mechanisms of chaos are more general, butalso of wider application, than those of catastrophe theory. Hence, we can expect
chaos to continue to play a larger role than catastrophe in the continuing study ofcomplex systems.
Rationality in a Catastrophic or Chaotic World
What implications do catastrophe and chaos have for the systems economies, thehuman mind, and designed complex systems that we have been discussing in theprevious six chapters? Although there have been some attempts to discover chaosin economic time series, the results thus far have been inconclusive. I am awareof no clear demonstration of chaosPage 179

in the brain, but there is increasing evidence that chaos plays a role, although stilla rather unclear one, in the functioning of the normal and defective heart.
Designers frequently construct systems (e.g., airplanes and ships) that produce,and cope successfully with, turbulence and perhaps other kinds of chaos.
On the basis of the evidence, we should suppose neither that all of the complex
systems we encounter in the world are chaotic, nor that few of them are.
Moreover, as the airplane example shows, the ominous term "chaotic" should not
be read as "unmanageable." Turbulence is frequently present in hydraulic andaerodynamic situations and artifacts. In such situations, although the future is notpredictable in any detail, it is manageable as an aggregate phenomenon. And thepaths of tornadoes and hurricanes are notoriously unstable but stable enough inthe short run that we can usually be warned and reach shelter before they hit us.
Since Newton, astronomers have been able to compute the motion of a system of
two bodies that exercise mutual gravitational attraction on each other. With threeor more bodies, they never obtained more than approximations to the motion,and indeed, there is now good reason to believe that, in general, gravitational
systems of three or more bodies, including the solar system, are chaotic. But wehave no reason to anticipate untoward consequences from that chaos its presence
simply implies that astronomers will be frustrated in their attempts to predict theexact positions of the planets in the rather long run a perplexity as frustrating as,
but perhaps less damaging than, the difficulties meteorologists experience inpredicting the weather.
Finally, there has been substantial progress in devising feedback devices that"tame" chaos by restricting chaotic systems, moving within their strange
attractors, to small neighborhoods having desired properties, so that the chaos
becomes merely tolerable noise. Such devices provide an example, consonantwith the discussion in earlier chapters, of the substitution of control for
prediction.
Complexity and Evolution
Much current research on complex systems focuses upon the emergence ofcomplexity, that is, system evolution. Two computational approaches

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to evolution that have attracted particular attention are the genetic algorithms
first explored by Holland14 and computer algorithms for cellular automata thatsimulate the multiplication and competition of organisms, playing the so-called"game of life."
Genetic Algorithms

From an evolutionary standpoint, an organism can be represented by a list or
vector of features (its genes). Evolution evaluates this vector in terms of fitness
for survival. From generation to generation, the frequency distribution of features
and their combinations over the members of a species change through sexual
reproduction, crossover, inversion, and mutation. Natural selection causesfeatures and combinations of features contributing to high fitness to multiply
more rapidly than, and ultimately to replace, features and combinations
conducive to low fitness.
By programming this abstraction on a modern computer we can build acomputational model of the process of evolution. The simulation, in turn, can beused to study the relative rates at which fitness will grow under different
assumptions about the model, including assumptions about rates of mutation andcrossover. In the next chapter, we will consider the special case of evolution inhierarchical systems, which appears to be the kind of system that predominates in
the natural world.
Cellular Automata and the Game of LifeThe computer is used not only to estimate the statistics of evolution but to carryout simulations, at an abstract level, of evolutionary processes. This researchgoes back, in fact, to the second eruption of interest in complexity, after WorldWar II, when John von Neumann, building on some ideas of Stanislaw Ulam,defined abstractly (but did not implement) a system that was capable ofreproducing itself. The idea was kept alive by Arthur Burks and others, but it was
not until well into the current period of activity that Christopher Langton created
a computer program that simulated a self-reproducing cellular automaton.15
Computer programs can create
14.

J. H. Holland, Adaptation in Natural and Artificial Systems (Ann Arbor: University of Michigan
Press, 1975).
15.

A. W. Burks (ed.), Essays on Cellular Automata (Champaign-Urbana: University of Illinois Press,
1970); C. G. Langton (ed.), Artificial Life. Santa Fe Insti(footnote continued on next page)Page 181

symbolic objects of various kinds and apply rules for their replication or
destruction as a function of their environments (which include other nearby
objects). With appropriate selection of the system parameters, such simulations
can provide vivid demonstrations of evolving self-reproducing systems. This lineof exploration is still at a very early stage of development, is largely dependent
on computer simulation, and lacks any large body of formal theory. It will besome time before we can assess its potential, but it has already presented us witha fundamental and exciting result: self-reproducing systems are a reality.
Conclusion
Complexity is more and more acknowledged to be a key characteristic of theworld we live in and of the systems that cohabit our world. It is not new forscience to attempt to understand complex systems: astronomers have been at it
for millennia, and biologists, economists, psychologists, and others joined themsome generations ago. What is new about the present activity is not the study ofparticular complex systems but the study of the phenomenon of complexity in its
own right.
If, as appears to be the case, complexity (like systems science) is too general a
subject to have much content, then particular classes of complex systems
possessing strong properties that provide a fulcrum for theorizing andgeneralizing can serve as the foci of attention. More and more, this appears to bejust what is happening, with chaos, genetic algorithms, cellular automata,catastrophe, and hierarchical systems serving as some of the currently visible
focal points. In the next chapter we will examine the last-named of these moreclosely.
(footnote continued from previous page)tute Studies in the Sciences of Complexity, Proceedings, vol. 6 (Redwood City, CA: AddisonWesley; 1989); C. G. Langton, C. Taylor, J. D. Farmer and S. Rassmussen (eds.), Artificial Life II.
Santa Fe Institute Studies in the Sciences of Complexity, Proceedings, vol. 10 (Redwood City, CA:
Addison-Wesley, 1992).

Page 182

Page 183

8The Architecture of Complexity: Hierarchic Systems
In this chapter I should like to report on some things we have been learning about
particular kinds of complex systems encountered in various sciences. The
developments I shall discuss arose in the context of specific phenomena, but thetheoretical formulations themselves make little reference to details of structure.
Instead they refer primarily to the complexity of the systems under view without
specifying the exact content of that complexity. Because of their abstractness, thetheories may have relevance application would be too strong a term to manykinds of complex systems observed in the social, biological, and physicalsciences.
In recounting these developments, I shall avoid technical detail, which cangenerally be found elsewhere. I shall describe each theory in the particular
context in which it arose. Then I shall cite some examples of complex systems,
from areas of science other than the initial application, to which the theoreticalframework appears relevant. In doing so, I shall make reference to areas ofknowledge where I am not expert perhaps not even literate. The reader will havelittle difficulty, I am sure, in distinguishing instances based on idle fancy or sheer
ignorance from instances that cast some light on the ways in which complexity
exhibits itself wherever it is found in nature.
I shall not undertake a formal definition of "complex systems."1 Roughly, by acomplex system I mean one made up of a large number ofThis chapter is a revision of a paper with the same title, reprinted with permission from Proceedingsof the American Philosophical Society, 106(December 1962):467 482.
1.

W. Weaver, in "Science and Complexity," American Scientist, 36(1948):536, has distinguished two
kinds of complexity, disorganized and organized. We shall be concerned primarily with organizedcomplexity.

Page 184

parts that have many interactions. As we saw in the last chapter, in such systems
the whole is more than the sum of the parts in the weak but important pragmatic
sense that, given the properties of the parts and the laws of their interaction, it is
not a trivial matter to infer the properties of the whole.2
The four sections that follow discuss four aspects of complexity. The first offerssome comments on the frequency with which complexity takes the form of
hierarchy the complex system being composed of subsystems that in turn havetheir own subsystems, and so on. The second section theorizes about the relation
between the structure of a complex system and the time required for it to emerge
through evolutionary processes; specifically it argues that hierarchic systems will
evolve far more quickly than non-hierarchic systems of comparable size. The
third section explores the dynamic properties of hierarchically organized systems
and shows how they can be decomposed into subsystems in order to analyze their
behavior. The fourth section examines the relation between complex systems andtheir descriptions.
Thus my central theme is that complexity frequently takes the form of hierarchy
and that hierarchic systems have some common properties independent of their
specific content. Hierarchy, I shall argue, is one of the central structural schemesthat the architect of complexity uses.
Hierarchic Systems
By a hierarchic system, or hierarchy, I mean a system that is composed ofinterrelated subsystems, each of the latter being in turn hierarchic in structure
until we reach some lowest level of elementary subsystem. In most systems innature it is somewhat arbitrary as to where we leave off
2.

See also John R. Platt, "Properties of Large Molecules that Go beyond the Properties of Their
Chemical Sub-groups," Journal of Theoretical Biology, 1(1961):342 358. Since the reductionismholism issue is a major cause de guerre between scientists and humanists, perhaps we might evenhope that peace could be negotiated between the two cultures along the lines of the compromise justsuggested. As I go along, I shall have a little to say about complexity in the arts as well as in thenatural sciences. I must emphasize the pragmatism of my holism to distinguish it sharply from theposition taken by W. M. Elsasser in The Physical Foundation of Biology (New York: Pergamon
Press, 1958).

Page 185

the partitioning and what subsystems we take as elementary. Physics makes
much use of the concept of ''elementary particle," although particles have a
disconcerting tendency not to remain elementary very long. Only a couple ofgenerations ago the atoms themselves were elementary particles; today to the
nuclear physicist they are complex systems. For certain purposes of astronomy
whole stars, or even galaxies, can be regarded as elementary subsystems. In one
kind of biological research a cell may be treated as an elementary subsystem; inanother, a protein molecule; in still another, an amino acid residue.
Just why a scientist has a right to treat as elementary a subsystem that is in fact
exceedingly complex is one of the questions we shall take up. For the moment
we shall accept the fact that scientists do this all the time and that, if they arecareful scientists, they usually get away with it.
Etymologically the word "hierarchy" has had a narrower meaning than I am
giving it here. The term has generally been used to refer to a complex system inwhich each of the subsystems is subordinated by an authority relation to thesystem it belongs to. More exactly, in a hierarchic formal organization eachsystem consists of a "boss" and a set of subordinate subsystems. Each of thesubsystems has a "boss" who is the immediate subordinate of the boss of thesystem. We shall want to consider systems in which the relations among
subsystems are more complex than in the formal organizational hierarchy justdescribed. We shall want to include systems in which there is no relation ofsubordination among subsystems. (In fact even in human organizations theformal hierarchy exists only on paper; the real flesh-and-blood organization has
many inter part relations other than the lines of formal authority.) For lack of abetter term I shall use "hierarchy" in the broader sense introduced in the previous
paragraphs to refer to all complex systems analyzable into successive sets ofsubsystems and speak of "formal hierarchy" when I want to refer to the more
specialized concept.3
3.

The mathematical term "partitioning" will not do for what I call here a hierarchy; for the set ofsubsystems and the successive subsets in each of these define the partitioning, independent of any
systems of relations among the subsets. By "hierarchy" I mean the partitioning in conjunction withthe relations that hold among its parts.

Page 186

Social Systems
I have already given an example of one kind of hierarchy that is frequently
encountered in the social sciences a formal organization. Business firms,
governments, and universities all have a clearly visible parts-within-parts
structure. But formal organizations are not the only, or even the most common,
kind of social hierarchy. Almost all societies have elementary units calledfamilies, which may be grouped into villages or tribes, and these into larger
groupings, and so on. If we make a chart of social interactions, of who talks towhom, the clusters of dense interaction in the chart will identify a rather welldefined hierarchic structure. The groupings in this structure may be defined
operationally by some measure of frequency of interaction in this socio metricmatrix.
Biological and Physical Systems
The hierarchical structure of biological systems is a familiar fact. Taking the cellas the building block, we find cells organized into tissues, tissues into organs,organs into systems. Within the cell are well-defined subsystems for example,
nucleus, cell membrane, microsomes, and mitochondria.
The hierarchic structure of many physical systems is equally clear-cut. I havealready mentioned the two main series. At the microscopic level we haveelementary particles, atoms, molecules, and macromolecules. At the macroscopic
level we have satellite systems, planetary systems, galaxies. Matter is distributed
throughout space in a strikingly non uniform fashion. The most nearly random
distributions we find, gases, are not random distributions of elementary particles
but random distributions of complex systems, that is, molecules.
A considerable range of structural types is subsumed under the term "hierarchy"
as I have defined it. By this definition a diamond is hierarchic, for it is a crystal
structure of carbon atoms that can be further decomposed into protons, neutrons,
and electrons. However, it is a very "flat" hierarchy, in which the number of firstorder subsystems belonging to the crystal can be indefinitely large. A volume ofmolecular gas is a flat hierarchy in the same sense. In ordinary usage we tend toreserve the word "hierarchy'' for a system that is divided into a small or moderate
number of subsystems, each of which may be further subdivided. Hence we doPage 187

not ordinarily think of or refer to a diamond or a gas as a hierarchic structure.
Similarly a linear polymer is simply a chain, which may be very long, ofidentical subparts, the monomers. At the molecular level it is a very flat
hierarchy.
In discussing formal organizations, the number of subordinates who report
directly to a single boss is called his span of control. I shall speak analogously of
the span of a system, by which I shall mean the number of subsystems into
which it is partitioned. Thus a hierarchic system is flat at a given level if it has awide span at that level. A diamond has a wide span at the crystal level but not at
the next level down, the atomic level.
In most of our theory construction in the following sections we shall focus ourattention on hierarchies of moderate span, but from time to time I shall commenton the extent to which the theories might or might not be expected to apply tovery flat hierarchies.
There is one important difference between the physical and biologicalhierarchies, on the one hand, and social hierarchies, on the other. Most physicaland biological hierarchies are described in spatial terms. We detect the organelles
in a cell in the way we detect the raisins in a cake they are "visibly"
differentiated substructures localized spatially in the larger structure. On theother hand, we propose to identify social hierarchies not by observing who lives
close to whom but by observing who interacts with whom. These two points ofview can be reconciled by defining hierarchy in terms of intensity of interaction,but observing that in most biological and physical systems relatively intenseinteraction implies relative spatial propinquity. One of the interesting
characteristics of nerve cells and telephone wires is that they permit very specificstrong interactions at great distances. To the extent that interactions arechanneled through specialized communications and transportation systems,
spatial propinquity becomes less determinative of structure.
Symbolic Systems
One very important class of systems has been omitted from my examples thus
far: systems of human symbolic production. A book is a hierarchy in the sense inwhich I am using that term. It is generally divided into chapters, the chapters into
sections, the sections into paragraphs, the paragraphs into sentences, thesentences into clauses and phrases, thePage 188

clauses and phrases into words. We may take the words as our elementary units,
or further subdivide them, as the linguist often does, into smaller units. If thebook is narrative in character, it may divide into "episodes" instead of sections,but divisions there will be.
The hierarchic structure of music, based on such units as movements, parts,
themes, phrases, is well known. The hierarchic structure of products of thepictorial arts is more difficult to characterize, but I shall have something to say
about it later.
The Evolution of Complex Systems
Let me introduce the topic of evolution with a parable. There once were two
watchmakers, named Hora and Tempus, who manufactured very fine watches.
Both of them were highly regarded, and the phones in their workshops rang
frequently new customers were constantly calling them. However, Hora
prospered, while Tempus became poorer and poorer and finally lost his shop.
What was the reason?
The watches the men made consisted of about 1,000 parts each. Tempus had soconstructed his that if he had one partly assembled and had to put it down toanswer the phone, say it immediately fell to pieces and had to be reassembled
from the elements. The better the customers liked his watches, the more theyphoned him and the more difficult it became for him to find enoughuninterrupted time to finish a watch.
The watches that Hora made were no less complex than those of Tempus. But hehad designed them so that he could put together subassemblies of about tenelements each. Ten of these subassemblies, again, could be put together into alarger subassembly; and a system of ten of the latter subassemblies constituted
the whole watch. Hence, when Hora had to put down a partly assembled watch toanswer the phone, he lost only a small part of his work, and he assembled his
watches in only a fraction of the man-hours it took Tempus.
It is rather easy to make a quantitative analysis of the relative difficulty of thetasks of Tempus and Hora: suppose the probability that an interruption will
occur, while a part is being added to an incomplete assembly, is p. Then theprobability that Tempus can complete a watch he has started without interruptionis (1 - p)1000a very small number unless p

Page 189

is 0.001 or less. Each interruption will cost on the average the time to assemble
1/p parts (the expected number assembled before interruption). On the other
hand, Hora has to complete 111 subassemblies of ten parts each. The probability
that he will not be interrupted while completing any one of these is (1 - p)10, andeach interruption will cost only about the time required to assemble five parts.4
Now if p is about 0.01that is, there is one chance in a hundred that either
watchmaker will be interrupted while adding any one part to an assembly then astraightforward calculation shows that it will take Tempus on the average aboutfour thousand times as long to assemble a watch as Hora.
We arrive at the estimate as follows:
1. Hora must make 111 times as many complete assemblies per watch as
Tempus; but
2. Tempus will lose on the average 20 times as much work for each interrupted
assembly as Hora (100 parts, on the average, as against 5); and3. Tempus will complete an assembly only 44 times per million attempts (0.991000= 44 × 10-6), while Hora will complete nine out of ten (0.99 10 = 9 × 10-1). Hence
Tempus will have to make 20,000 as many attempts per completed assembly as
Hora. (9 × 10-1)/(44 × 10-6) = 2 × 104. Multiplying these three ratios, we get
4.

The speculations on speed of evolution were first suggested by H. Jacobson's application of
information theory to estimating the time required for biological evolution. See his paper"Information, Reproduction, and the Origin of Life," in American Scientist, 43(January 1955):119
127. From thermodynamic considerations it is possible to estimate the amount of increase in entropy
that occurs when a complex system decomposes into its elements. (See for example, R. B. Setlow
and E. C. Pollard, Molecular Biophysics (Reading, Mass.: Addison-Wesley, 1962), pp. 63 65, andreferences cited there.) But entropy is the logarithm of a probability; hence information, the negativeof entropy, can be interpreted as the logarithm of the reciprocal of the probability the"improbability," so to speak. The essential idea in Jacobson's model is that the expected timerequired for the system to reach a particular state is inversely proportional to the probability of thestate hence it increases exponentially with the amount of information (negentropy) of the state.
Following this line of argument, but not introducing the notion of levels and stable subassemblies,
Jacobson arrived at estimates of the time required for evolution so large as to make the event rather
improbable. Our analysis, carried through in the same way, but with attention to the stable intermediate
forms, produces very much smaller estimates.

Page 190

1/111 × 100/5 × 0.9910/0.991000= 1/111 × 20 × 20,000 ~ 4,000.
Biological Evolution
What lessons can we draw from our parable for biological evolution? Let us
interpret a partially completed subassembly of k elementary parts as thecoexistence of k parts in a small volume ignoring their relative orientations. The
model assumes that parts are entering the volume at a constant rate but that there
is a constant probability, p, that the part will be dispersed before another isadded, unless the assembly reaches a stable state. These assumptions are notparticularly realistic. They undoubtedly underestimate the decrease in probabilityof achieving the assembly with increase in the size of the assembly. Hence theassumptions understate probably by a large factor the relative advantage of ahierarchic structure.
Although we cannot therefore take the numerical estimate seriously, the lesson
for biological evolution is quite clear and direct. The time required for theevolution of a complex form from simple elements depends critically on thenumbers and distribution of potential intermediate stable forms. In particular, if
there exists a hierarchy of potential stable "subassemblies," with about the samespan, s, at each level of the hierarchy, then the time required for a subassembly
can be expected to be about the same at each level that is, proportional to 1/(1 p)s. The time required for the assembly of a system of n elements will be
proportional to log n, that is, to the number of levels in the system. One would
say with more illustrative than literal intent that the time required for theevolution of multi-celled organisms from single-celled organisms might be of thesame order of magnitude as the time required for the evolution of single-celledorganisms from macromolecules. The same argument could be applied to theevolution of proteins from amino acids, of molecules from atoms, of atoms fromelementary particles.
s

A whole host of objections to this oversimplified scheme will occur, I am sure, toevery working biologist, chemist, and physicist. Before turning to matters I know
more about, I shall lay at rest four of these problems, leaving the remainder to theattention of the specialists.

Page 191

First, in spite of the overtones of the watchmaker parable, the theory assumes noteleological mechanism. The complex forms can arise from the simple ones bypurely random processes. (I shall propose another model in a moment that shows
this clearly.) Direction is provided to the scheme by the stability of the complex
forms, once these come into existence. But this is nothing more than survival of
the fittest that is, of the stable.
Second, not all large systems appear hierarchical. For example, most polymers
such as nylon are simply linear chains of large numbers of identical components,
the monomers. However, for present purposes we can simply regard such astructure as a hierarchy with a span of one the limiting case; for a chain of any
length represents a state of relative equilibrium.5
Third, the evolutionary process does not violate the second law ofthermodynamics. The evolution of complex systems from simple elements
implies nothing, one way or the other, about the change in entropy of the entire
system. If the process absorbs free energy, the complex system will have asmaller entropy than the elements; if it releases free energy, the opposite will betrue. The former alternative is the one that holds for most biological systems, andthe net inflow of free energy has to be supplied from the sun or some othersource if the second law of thermodynamics is not to be violated. For theevolutionary process we are describing, the equilibria of the intermediate statesneed have only local and not global stability, and they may be stable only in thesteady state that is, as long as there is an external source of free energy that may
be drawn upon.6
5.

There is a well-developed theory of polymer size, based on models of random assembly: See, forexample, P. J. Flory, Principles of Polymer Chemistry (Ithaca: Cornell University Press, 1953),
chapter 8. Since all subassemblies in the polymerization theory are stable, limitation of molecular
growth depends on "poisoning" of terminal groups by impurities or formation of cycles rather thanupon disruption of partially formed chains.
6.

This point has been made many times before, but it cannot be emphasized too strongly. For further
discussion, see Setlow and Pollard, Molecular Biophysics, pp. 49 64; E. Schrödinger, What Is Life?
(Cambridge: Cambridge University Press, 1945); and H. Linschitz, "The Information Content of a
Bacterial Cell," in H. Quastler (ed.), Information Theory in Biology (Urbana: University of Illinois
Press, 1953), pp. 251 262.

Page 192

Because organisms are not energetically closed systems, there is no way todeduce the direction, much less the rate, of evolution from classicalthermodynamic considerations. All estimates indicate that the amount of entropy,
measured in physical units, involved in the formation of a one-celled biologicalorganism is trivially small about -10-11cal/degree.7 The "improbability" ofevolution has nothing to do with this quantity of entropy, which is produced byevery bacterial cell every generation. The irrelevance of quantity of information,in this sense, to speed of evolution can also be seen from the fact that exactly as
much information is required to "copy" a cell through the reproductive process as
to produce the first cell through evolution.
The fact of the existence of stable intermediate forms exercises a powerful effecton the evolution of complex forms that may be likened to the dramatic effect of
catalysts upon reaction rates and steady-state distribution of reaction products inopen systems.8 In neither case does the entropy change provide us with a guide tosystem behavior.
Evolution of Multi-Cellular Organisms
We must consider a fourth objection to the watchmaker metaphor. Howeverconvincing a model the metaphor may provide for the evolution of atomic andmolecular systems, and even uni-cellular organisms, it does not appear to fit thehistory of multi-cellular organisms. The metaphor assumes that complex systems
are formed by combining sets of simpler systems, but this is not the way inwhich multi-cellular organisms have evolved. Although bacteria may, in fact,
have been produced by a merging of mitochondria with the cells they inhabited,
multi-cellular organisms have evolved through multiplication and specializationof the cells of a single system, rather than through the merging of previously
independent subsystems.
Lest we dismiss the metaphor too quickly, however, we should observe thatsystems that evolve through specialization acquire the same kind7.

See Linschitz, "The Information Content." This quantity, 10-11 cal/degree, corresponds to about
1013 bits of information.
8.

See H. Kacser, "Some Physico-chemical Aspects of Biological Organization," appendix, pp. 191 249,
in C. H. Waddington, The Strategy of the Genes (London: George Allen and Unwin, 1957).

Page 193of boxes-within-boxes structure (e.g., a digestive system consisting of mouth,larynx, esophagus, stomach, small and large intestines, colon; or a circulatory
system consisting of a heart, arteries, veins, and capillaries) as is acquired bysystems that evolve by assembly of simpler systems. The next main section ofthis chapter deals with nearly decomposable systems. It proposes that it is notassembly from components, per se, but hierarchic structure produced either byassembly or specialization, that provides the potential for rapid evolution.
The claim is that the potential for rapid evolution exists in any complex systemthat consists of a set of stable subsystems, each operating nearly independently ofthe detailed processes going on within the other subsystems, hence influenced
mainly by the net inputs and outputs of the other subsystems. If the neardecomposability condition is met, the efficiency of one component (hence its
contribution to the organism's fitness) does not depend on the detailed structureof other components.
Before examining this claim in detail, however, I should like to discuss briefly
some non-biological applications of the watchmaker metaphor to illustrate theimportant advantages that hierarchic systems enjoy in other circumstances.
Problem Solving As Natural Selection
Hierarchy, as well as processes akin to natural selection, appear in humanproblem solving, a domain that has no obvious connection with biologicalevolution. Consider, for example, the task of discovering the proof for a difficult
theorem. The process can be and often has been described as a search through amaze. Starting with the axioms and previously proved theorems, varioustransformations allowed by the rules of the mathematical systems are attempted,
to obtain new expressions. These are modified in turn until, with persistence andgood fortune, a sequence or path of transformations is discovered that leads to
the goal.
The process ordinarily involves much trial and error. Various paths are tried;some are abandoned, others are pushed further. Before a solution is found, many
paths of the maze may be explored. The more difficult and novel the problem,
the greater is likely to be the amount of trial and error required to find a solution.
At the same time the trial and error is not completely random or blind; it is in fact
rather highly selective. The

Page 194

new expressions that are obtained by transforming given ones are examined to
see whether they represent progress toward the goal. Indications of progress spurfurther search in the same direction; lack of progress signals the abandonment ofa line of search. Problem solving requires selective trial and error.9
A little reflection reveals that cues signaling progress play the same role in the
problem-solving process that stable intermediate forms play in the biologicalevolutionary process. In fact we can take over the watchmaker parable and apply
it also to problem solving. In problem solving, a partial result that represents
recognizable progress toward the goal plays the role of stable subassembly.
Suppose that the task is to open a safe whose lock has 10 dials, each with 100
possible settings, numbered from 0 to 99. How long will it take to open the safe
by a blind trial-and-error search for the correct setting? Since there are 10010
possible settings, we may expect to examine about one half of these, on the
average, before finding the correct one that is, 50 billion billion settings.
Suppose, however, that the safe is defective, so that a click can be heard when
any one dial is turned to the correct setting. Now each dial can be adjusted
independently and does not need to be touched again while the others are being
set. The total number of settings that have to be tried is only 10 × 50, or 500. The
task of opening the safe has been altered, by the cues the clicks provide, from apractically impossible one to a trivial one.10
9.

See A. Newell, J. C. Shaw, and H. A. Simon, "Empirical Explorations of the Logic Theory
Machine;" Proceedings of the 1957 Western Joint Computer Conference, February 1957 (New York:
Institute of Radio Engineers); "Chess-Playing Programs and the Problem of Complexity," IBM
Journal of Research and Development, 2(October 1958):320 335; and for a similar view of problem
solving, W. R. Ashby, "Design for an Intelligence Amplifier," pp. 215 233 in C. E. Shannon and J.
McCarthy, Automata Studies (Princeton: Princeton University Press, 1956).
10.

The clicking safe example was supplied by D. P. Simon. Ashby, "Design for an Intelligence
Amplifier," p. 230, has called the selectivity involved in situations of this kind "selection bycomponents." The even greater reduction in time produced by hierarchization in the clicking safe
example, as compared with the watch makers metaphor, is due to the fact that a random search for thecorrect combination is involved in the former case, while in the latter the parts come together in the rightorder. It is not clear which of these metaphors provides the better model for biological evolution, but wemay be sure that the watchmaker's(footnote continued on next page)Page 195

A considerable amount has been learned in the past thirty years about the natureof the mazes that represent common human problem-solving tasks proving
theorems, solving puzzles, playing chess, making investments, balancing
assembly lines, to mention a few. All that we have learned about these mazes
points to the same conclusion: that human problem solving, from the mostblundering to the most insightful, involves nothing more than varying mixturesof trial and error and selectivity. The selectivity derives from various rules of
thumb, or heuristics, that suggest which paths should be tried first and whichleads are promising. We do not need to postulate processes more sophisticatedthan those involved in organic evolution to explain how enormous problem
mazes are cut down to quite reasonable size (see also chapters 3 and 4).11
The Sources of Selectivity
When we examine the sources from which the problem-solving system, or the
evolving system, as the case may be, derives its selectivity, we discover that
selectivity can always be equated with some kind of feedback of informationfrom the environment.
Let us consider the case of problem solving first. There are two basic kinds of
selectivity. One we have already noted: various paths are tried out, theconsequences of following them are noted, and this information is used to guide
further search. In the same way in organic evolution various complexes comeinto being, at least evanescently, and those that are stable provide new building
blocks for further construction. It is this information about stable configurations,and not free energy or negentropy from the sun, that guides the process ofevolution and provides the selectivity that is essential to account for its rapidity.
The second source of selectivity in problem solving is previous experience. We
see this particularly clearly when the problem to be solved is(footnote continued from previous page)
metaphor gives an exceedingly conservative estimate of the savings due to hierarchization. The safe
may give an excessively high estimate because it assumes all possible arrangements of the elements
to be equally probable. For an application of a variant of the watchmaker and the clicking safe
arguments to structure at the molecular level, see J. D. Watson, Molecular Biology of the Gene, 3rded. (Menlo Park, CA: W. A. Benjamin, 1976), pp. 107 108.
11.

A. Newell and H. A. Simon, "Computer Simulation of Human Thinking," Science, 134(December 22,1961):2011 2017.

Page 196

similar to one that has been solved before. Then, by simply trying again the paths
that led to the earlier solution, or their analogues, trial-and-error search is greatly
reduced or altogether eliminated.
What corresponds to this latter kind of information in organic evolution? The
closest analogue is reproduction. Once we reach the level of self-reproducing
systems, a complex system, when it has once been achieved, can be multiplied
indefinitely. Reproduction in fact allows the inheritance of acquired
characteristics, but at the level of genetic material, of course; that is, only
characteristics acquired by the genes can be inherited. We shall return to the
topic of reproduction in the final section of this essay.
On Empires and Empire Building
We have not exhausted the categories of complex systems to which thewatchmaker argument can reasonably be applied. Philip assembled his
Macedonian empire and gave it to his son, to be later combined with the Persian
subassembly and others into Alexander's greater system. On Alexander's death
his empire did not crumble to dust but fragmented into some of the major
subsystems that had composed it.
The watchmaker argument implies that if one would be Alexander, one should beborn into a world where large stable political systems already exist. Where thiscondition was not fulfilled, as on the Scythian and Indian frontiers, Alexander
found empire building a slippery business. So too, T. E. Lawrence's organizingof the Arabian revolt against the Turks was limited by the character of his largest
stable building blocks, the separate, suspicious desert tribes.
The profession of history places a greater value upon the validated particular fact
than upon tendentious generalization. I shall not elaborate upon my fancy
therefore but shall leave it to historians to decide whether anything can belearned for the interpretation of history from an abstract theory of hierarchic
complex systems.
Conclusion: The Evolutionary Explanation of Hierarchy
We have shown thus far that complex systems will evolve from simple systems
much more rapidly if there are stable intermediate forms than if there are not.
The resulting complex forms in the former case will bePage 197

hierarchic. We have only to turn the argument around to explain the observed
predominance of hierarchies among the complex systems nature presents to us.
Among possible complex forms, hierarchies are the ones that have the time to
evolve. The hypothesis that complexity will be hierarchic makes no distinction
among very flat hierarchies, like crystals and tissues and polymers, and theintermediate forms. Indeed in the complex systems we encounter in nature
examples of both forms are prominent. A more complete theory than the one wehave developed here would presumably have something to say about thedeterminants of width of span in these systems.
Nearly Decomposable Systems
In hierarchic systems we can distinguish between the interactions among
subsystems, on the one hand, and the interactions within subsystems that is,among the parts of those subsystems on the other. The interactions at thedifferent levels may be, and often will be, of different orders of magnitude. In aformal organization there will generally be more interaction, on the average,
between two employees who are members of the same department than between
two employees from different departments. In organic substances intermolecularforces will generally be weaker than molecular forces, and molecular forces
weaker than nuclear forces.
In a rare gas the intermolecular forces will be negligible compared to thosebinding the molecules we can treat the individual particles for many purposes as
if they were independent of each other. We can describe such a system as
decomposable into the subsystems comprised of the individual particles. As thegas becomes denser, molecular interactions become more significant. But oversome range we can treat the decomposable case as a limit and as a first
approximation. We can use a theory of perfect gases, for example, to describe
approximately the behavior of actual gases if they are not too dense. As a second
approximation we may move to a theory of nearly decomposable systems, inwhich the interactions among the subsystems are weak but not negligible.
At least some kinds of hierarchic systems can be approximated successfully as
nearly decomposable systems. The main theoretical findings fromPage 198the approach can be summed up in two propositions: (1) in a nearly
decomposable system the short-run behavior of each of the component
subsystems is approximately independent of the short-run behavior of the othercomponents; (2) in the long run the behavior of any one of the components
depends in only an aggregate way on the behavior of the other components.
Let me provide a very concrete simple example of a nearly decomposable
system.12 Consider a building whose outside walls provide perfect thermal
insulation from the environment. We shall take these walls as the boundary of
our system. The building is divided into a large number of rooms, the walls
between them being good, but not perfect, insulators. The walls between rooms
are the boundaries of our major subsystems. Each room is divided by partitions
into a number of cubicles, but the partitions are poor insulators. A thermometer
hangs in each cubicle. Suppose that at the time of our first observation of thesystem there is a wide variation in temperature from cubicle to cubicle and fromroom to room the various cubicles within the building are in a state of thermal
disequilibrium. When we take new temperature readings several hours later,
what shall we find? There will be very little variation in temperature among the
cubicles within each single room, but there may still be large temperature
variations among rooms. When we take readings again several days later, wefind an almost uniform temperature throughout the building; the temperature
differences among rooms have virtually disappeared.
We can describe the process of equilibrium formally by setting up the usual
equations of heat flow. The equations can be represented by the matrix of theircoefficients, r , where r is the rate at which heat flows from the ith cubicle to thejth cubicle per degree difference in their temij

12.

ij

This discussion of near decomposability is based upon H. A. Simon and A. Ando, ''Aggregationof Variables in Dynamic Systems;" Econometrica, 29 (April 1961):111 138. The example is drawn
from the same source, pp. 117 118. For subsequent development and applications of the theory see
P. J. Courtois, Decomposability: Queueing and Computer System Applications (New York, NY:
Academic Press, 1977); Y. Iwasaki and H. A. Simon, "Causality and Model Abstraction;" Artificial
Intelligence, 67(1994):143 194; and D. F. Rogers and R. D. Plante, "Estimating Equilibrium
Probabilities for Band Diagonal Markov Chains Using Aggregation and Disaggregation
Techniques," Computers in Operations Research, 20(1993):857 877.

Page 199
A1
A1
A2

A3

100
100

A3
B1

A2

2

B2

B1

B2

C2

2

1

C3

2
100

1

1

100

2

1

100

1

C1

2

100

1

C1

2

100

C2

1

C3

1

2

100

2

100
100

Figure7
A hypothetical nearly decomposable system. In terms of the heat-exchange exampleof the text. A1, A2, and A3 may be interpreted as cubicles in one room, B1
and B2 as cubicles in a second room, and C1, C2, and C3 as cubicles in a third.
The matrix entries then are the heat diffusion coefficients between cubicles:
A1

B1

A2
A3

C1
C2

B2

C3

peratures. If cubicles i and j do not have a common wall, r will be zero. If cubicles i and jhave a common wall and are in the same room, r will be large. If cubicles i and j are
separated by the wall of a room, r will be nonzero but small. Hence, by grouping together
all the cubicles that are in the same room, we can arrange the matrix of coefficients so that
all its large elements lie inside a string of square sub matrices along the main diagonal. All
the elements outside these diagonal squares will be either zero or small (see figure 7). We
may take some small number, , as the upper bound of the extra diagonal elements. We shallcall a matrix having these properties a nearly decomposable matrix.
ij

ij

ij

Now it has been proved that a dynamic system that can be described by a nearly
decomposable matrix has the properties, stated earlier, of a nearly decomposable system. In
our simple example of heat flow this means that in the short run each room will reach anequilibrium temperature (an average of the initial temperatures of its offices) nearly
independently of the others and that each room will remain approximately in a state ofequilibrium over the longer period during which an over-all temperature equilibrium is being
established throughout the building.

Page 200

After the intra-room short-run equilibria have been reached, a single
thermometer in each room will be adequate to describe the dynamic behavior of
the entire system separate thermometers in each cubicle will be superfluous.
Near Decomposability of Social Systems
As a glance at figure 7 shows, near decomposability is a rather strong property
for a matrix to possess, and the matrices that have this property will describe
very special dynamic systems vanishingly few systems out of all those that arethinkable. How few they will be depends of course on how good anapproximation we insist upon. If we demand that epsilon be very small,correspondingly few dynamic systems will fit the definition. But we have alreadyseen that in the natural world nearly decomposable systems are far from rare. On
the contrary, systems in which each variable is linked with almost equal strengthwith almost all other parts of the system are far rarer and less typical.
In economic dynamics the main variables are the prices and quantities ofcommodities. It is empirically true that the price of any given commodity and therate at which it is exchanged depend to a significant extent only on the prices andquantities of a few other commodities, together with a few other aggregatemagnitudes, like the average price level or some over-all measure of economic
activity. The large linkage coefficients are associated in general with the main
flows of raw materials and semi-finished products within and between industries.
An input-output matrix of the economy, giving the magnitudes of these flows,
reveals the nearly decomposable structure of the system with one qualification.
There is a consumption subsystem of the economy that is linked strongly tovariables in most of the other subsystems. Hence we have to modify our notionsof decomposability slightly to accommodate the special role of the consumption
subsystem in our analysis of the dynamic behavior of the economy.
In the dynamics of social systems, where members of a system communicate
with and influence other members, near decomposability is generally very
prominent. This is most obvious in formal organizations, where the formal
authority relation connects each member of the organization with one immediate
superior and with a small number of subordinates.

Page 201

Of course many communications in organizations follow other channels than thelines of formal authority. But most of these channels lead from any particular
individual to a very limited number of his superiors, subordinates, and associates.
Hence departmental boundaries play very much the same role as the walls in ourheat example.
Physicochemical Systems
In the complex systems familiar in biological chemistry, a similar structure isclearly visible. Take the atomic nuclei in such a system as the elementary parts ofthe system, and construct a matrix of bond strengths between elements. There
will be matrix elements of quite different orders of magnitude. The largest will
generally correspond to the covalent bonds, the next to the ionic bonds, the thirdgroup to hydrogen bonds, still smaller linkages to van der Waals forces.13 If weselect an epsilon just a little smaller than the magnitude of a covalent bond, thesystem will decompose into sub systems the constituent molecules. The smaller
linkages will correspond to the intermolecular bonds.
It is well known that high-energy, high-frequency vibrations are associated withthe smaller physical subsystems and low-frequency vibrations with the larger
systems into which the subsystems are assembled. For example, the radiation
frequencies associated with molecular vibrations are much lower than those
associated with the vibrations of the planetary electrons of the atoms; the latter inturn are lower than those associated with nuclear processes.14 Molecular systems
are nearly decomposable systems, with the short-run dynamics relating to theinternal structures of
13.

For a survey of the several classes of molecular and intermolecular forces, and their dissociationenergies, see Setlow and Pollard, Molecular Biophysics, chapter 6. The energies of typical covalentbonds are of the order of 80 100 k cal/mole, of the hydrogen bonds, 10 k cal/mole. Ionic bonds
generally lie between these two levels; the bonds due to van der Waals forces are lower in energy.
14.

Typical wave numbers for vibrations associated with various systems (the wave number is thereciprocal of wave length, hence proportional to frequency):
Steel wire under tension10-10 to 10-9 cm-1
Molecular rotations100 to 102 cm-1
Molecular vibrations102 to 103 cm-1
Planetary electrons104 to 105 cm-1
Nuclear rotations109 to 1010 cm-1
Nuclear surface vibrations1011 to 1012 cm-1

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the subsystems and the long-run dynamics to the interactions of thesesubsystems.
A number of the important approximations employed in physics depend for theirvalidity on the near decomposability of the systems studied. The theory of thethermodynamics of irreversible processes, for example, requires the assumptionof macroscopic disequilibrium but microscopic equilibrium, exactly the situation
described in our heat-exchange example.15 Similarly computations in quantum
mechanics are often handled by treating weak interactions as producing
perturbations on a system of strong interactions.
Some Observations on Hierarchic Span
To understand why the span of hierarchies is sometimes very broad as in crystals
and sometimes narrow, we need to examine more detail of the interactions. In
general the critical consideration is the extent to which interaction between two(or a few) subsystems excludes interaction of these subsystems with the others.
Let us examine first some physical examples.
Consider a gas of identical molecules, each of which can form covalent bonds incertain ways with others. Let us suppose that we can associate with each atom a
specific number of bonds that it is capable of maintaining simultaneously. (Thisnumber is obviously related to the number we usually call its valence.) Now
suppose that two atoms join and that we can also associate with the combinationa specific number of external bonds it is capable of maintaining. If this number isthe same as the number associated with the individual atoms, the bonding
process can go on indefinitely the atoms can form crystals or polymers ofindefinite extent. If the number of bonds of which the composite is capable isless than the number associated with each of the parts, then the process of
agglomeration must come to a halt.
We need only mention some elementary examples. Ordinary gases show notendency to agglomerate, because the multiple bonding of atoms "uses up" theircapacity to interact. While each oxygen atom has a valence of two, the O2
molecules have a zero valence. Contrariwise, indefi15.

S. R. de Groot, Thermodynamics of Irreversible Processes (New York: Inter-science Publishers,
1951), pp. 11 12.

Page 203

nite chains of single-bonded carbon atoms can be built up, because a chain of any
number of such atoms, each with two side groups, has a valence of exactly two.
Now what happens if we have a system of elements that possess both strong andweak interaction capacities and whose strong bonds are exhaustible throughcombination? Subsystems will form, until all the capacity for strong interaction
is utilized in their construction. Then these subsystems will be linked by the
weaker second-order bonds into larger systems. For example, a water moleculehas essentially a valence of zero all the potential covalent bonds are fully
occupied by the interaction of hydrogen and oxygen molecules. But the geometryof the molecule creates an electric dipole that permits weak interaction between
the water and salts dissolved in it whence such phenomena as its electrolytic
conductivity.16
Similarly it has been observed that, although electrical forces are much stronger
than gravitational forces, the latter are far more important than the former forsystems on an astronomical scale. The explanation of course is that the electricalforces, being bipolar, are all "used up" in the linkages of the smaller subsystems
and that significant net balances of positive or negative charges are not generally
found in regions of macroscopic size.
In social as in physical systems there are generally limits on the simultaneousinteraction of large numbers of subsystems. In the social case these limits are
related to the fact that a human being is more nearly a serial than a parallel
information-processing system. He or she can carry on only one conversation at atime, and although this does not limit the size of the audience to which a masscommunication can be addressed, it does limit the number of people
simultaneously involved in most other forms of social interaction. Apart fromrequirements of direct interactions, most roles impose tasks and responsibilities
that are time consuming. One cannot, for example, enact the role of "friend" withlarge numbers of other people.
It is probably true that in social as in physical systems the higher-frequency
dynamics are associated with the subsystems and the lower16.

See, for example, L. Pauling, General Chemistry (San Francisco: W. H. Freeman, 2nd ed., 1953),
chapter 15.

Page 204

frequency dynamics with the larger systems. It is generally believed, forexample, that the relevant planning horizon of executives is longer, the higher
their location in the organizational hierarchy. It is probably also true that both theaverage duration of an interaction between executives and the average interval
between interactions are greater at higher than lower levels.
Summary: Near Decomposability
We have seen that hierarchies have the property of near decomposability. Intracomponent linkages are generally stronger than inter component linkages. This
fact has the effect of separating the high-frequency dynamics of a hierarchy
involving the internal structure of the components from the low-frequency
dynamics involving interaction among components. We shall turn next to some
important consequences of this separation for the description and comprehensionof complex systems.
Biological Evolution Revisited
Having examined the properties of nearly-decomposable systems, we can now
complete our discussion of the evolution of multi-cellular organisms throughspecialization of tissues and organs. An organ performs a specific set offunctions, each usually requiring continual interaction among its component
parts (a sequence of chemical reactions, say, each step employing a particularenzyme for its execution). It draws raw materials from other parts of theorganism and delivers products to other parts, but these input and output
processes depend only in an aggregate way on what is occurring within eachspecific organ. Like a business firm in an economic market, each organ can
perform its functions in blissful ignorance of the detail of activity in otherorgans, with which it is connected by the digestive, circulatory, and excretory
systems and other transport channels.
Expressing the matter slightly differently, changes within an organ affect theother parts of the organism mainly by changing the relation between thequantities of outputs they produce and the inputs they require (that is, theiroverall efficiency). Thus, biological organisms are nearly-decomposable: theinteractions within units at any level are rapid and intense in comparison with theinteractions between units at the same

Page 205level. Inventories of various substances, held in the circulatory system or in
special tissues, slow down and buffer effects of each unit on the others. In the
short run, single units (e.g., single organs) operate nearly independently of thedetail of operation of the other units.
Within the Darwinian framework of natural selection there is no way in which
the fitness (efficiency) of individual tissues or organs can be separately
evaluated; fitness is measured by the number of offspring of the entire organism.
Evolution is like a complex experiment, with fitness as the sole dependent
variable, and the structures of the individual genes as independent variables. The
goal of the process is to compare the contribution to total fitness of alternative
forms (alleles) of each gene and of combinations of these alternatives for sets ofgenes.
If, in fact, the fitness of a particular gene depended on which alleles of all theother genes it was combined with, the combinatorics, involving tens of thousandsof genes in complex organisms, would be staggering, and the problem ofmeasuring the contribution of a particular allele to fitness would beoverwhelming.17
With near-decomposability, we can assume that the relative efficiency of two
different designs for the same organ (e.g., two different gene sequences with thesame function) is approximately independent of which variants of other organs
are present in the organism. The total fitness is essentially an additive measure of
the nearly independent contributions of the individual organs. Essentially, we
obtain the advantages of the clicking safe: the "correct" setting of each dial (thegenes governing one organ's processes) can be determined independently of how
the other dials are currently set. The search is for effective sets of organs insteadof effective sets of individual genes.
Enough is known today about the architecture of the genome to be reasonably
certain that it has a hierarchical control structure mapping reasonably closely tothe hierarchy of processes in the organism.18 Of
17.

With only two alleles for each of N genes, 2N alternatives would have to be evaluated by
selection. This is equivalent, in the watchmaker metaphor, to assembling 2N parts without
interruption. For an organism with even a thousand genes, say, change by natural selection would be
extremely slow, even on a geological scale.
18.

E Jacob and J. Monod, "Genetic Regulatory Mechanisms in the Synthesis of Proteins," Molecular
Biology, 3(1961):318 56.

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course, this is a gross simplification of the total picture in any actual organism. In
addition to the genes that operate in particular organs (turned on and off bycontrol genes), there are also the genes that determine the more generalmetabolic processes that are found within all the cells. But these common intracellular processes are at the cell level of the hierarchy, below the level of tissues
and organs, and again the corresponding genes can be supposed to operate nearly
independently of those that control specialized processes in specific organs.19
The Description of Complexity
If you ask a person to draw a complex object such as a human face he will almost
always proceed in a hierarchic fashion.20 First he will outline the face. Then hewill add or insert features: eyes, nose, mouth, ears, hair. If asked to elaborate, hewill begin to develop details for each of the features pupils, eyelids, lashes forthe eyes, and so on until he reaches the limits of his anatomical knowledge. His
information about the object is arranged hierarchically in memory, like a topicaloutline.
When information is put in outline form, it is easy to include information about
the relations among the major parts and information about the internal relationsof parts in each of the sub outlines. Detailed information about the relations of
subparts belonging to different parts has no place in the outline and is likely to belost. The loss of such information and the preservation mainly of information
about hierarchic order is a salient characteristic that distinguishes the drawings ofa child or someone untrained in representation from the drawing of a trained
artist. (I am speaking of an artist who is striving for representation.)
19.

How hierarchical architectures of these kinds can be introduced into the genetic algorithms
discussed in chapter 7, in order to speed up their rates of learning or evolution, is discussed by John
H. Holland in Adaptation in Natural and Artificial Systems (Ann Arbor, MI: The University ofMichigan Press, 1975). See especially pp. 167 168 and 152 153.
20.

George A. Miller has collected protocols from subjects who were given the task of drawing faces andfinds that they behave in the manner described here (private communication). See also E. H. Gombrich,
Art and Illusion (New York: Pantheon Books, 1960), pp. 291 296.

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Near Decomposability and Comprehensibility
From our discussion of the dynamic properties of nearly decomposable systems,
we have seen that comparatively little information is lost by representing them as
hierarchies. Subparts belonging to different parts only interact in an aggregative
fashion the detail of their interaction can be ignored. In studying the interactionof two large molecules, generally we do not need to consider in detail theinteractions of nuclei of the atoms belonging to the one molecule with the nucleiof the atoms belonging to the other. In studying the interaction of two nations,
we do not need to study in detail the interactions of each citizen of the first witheach citizen of the second.
The fact then that many complex systems have a nearly decomposable,
hierarchic structure is a major facilitating factor enabling us to understand,describe, and even "see" such systems and their parts. Or perhaps the proposition
should be put the other way round. If there are important systems in the worldthat are complex without being hierarchic, they may to a considerable extent
escape our observation and understanding. Analysis of their behavior would
involve such detailed knowledge and calculation of the interactions of theirelementary parts that it would be beyond our capacities of memory or
computation.21
21.

I believe the fallacy in the central thesis of W. M. Elsasser's The Physical Foundation of Biology,
mentioned earlier, lies in his ignoring the simplification in description of complex systems thatderives from their hierarchic structure. Thus (p. 155):
If we now apply similar arguments to the coupling of enzymatic reactions with the substratum ofprotein molecules, we see that over a sufficient period of time, the information corresponding to thestructural details of these molecules will be communicated to the dynamics of the cell, to higher
levels of organization as it were, and may influence such dynamics. While this reasoning is only
qualitative, it lends credence to the assumption that in the living organism, unlike the inorganic
crystal, the effects of microscopic structure cannot be simply averaged out; as time goes on thisinfluence will pervade the behavior of the cell "at all levels."
But from our discussion of near decomposability it would appear that those aspects of microstructurethat control the slow developmental aspects of organismic dynamics can be separated out from the
aspects that control the more rapid cellular metabolic processes. For this reason we should notdespair of unraveling the web of causes. See also J. R. Platt's review of Elsasser's book in
Perspectives in Biology and Medicine, 2(1959):243 245.

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I shall not try to settle which is chicken and which is egg: whether we are able tounderstand the world because it is hierarchic or whether it appears hierarchic
because those aspects of it which are not elude our understanding andobservation. I have already given some reasons for supposing that the former is
at least half the truth that evolving complexity would tend to be hierarchic but it
may not be the whole truth.
Simple Descriptions of Complex Systems
One might suppose that the description of a complex system would itself be acomplex structure of symbols and indeed it may be just that. But there is noconservation law that requires that the description be as cumbersome as theobject described. A trivial example will show how a system can be describedeconomically. Suppose the system is a two-dimensional array like this:
A

B

M

N

R

S

H

I

C

D

O

P

T

U

J

K

M

N

A

B

H

I

R

S

O

P

C

D

J

K

T

U

R

S

H

I

A

B

M

N

T

U

J

K

C

D

O

P

H

I

R

S

M

N

A

B

J

K

T

U

O

P

C

D

Let us call the arraya, the arraym, the arrayr, and the arrayh. Letus call the arrayw, and the array x. Then the entire array is simply.
While the original structure consisted of 64 symbols, it requires only 35 to write
down its description:

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We achieve the abbreviation by making use of the redundancy in the original
structure. Since the pattern, for example, occurs four times in the total
pattern, it is economical to represent it by the single symbol, a.
If a complex structure is completely un redundant if no aspect of its structure canbe inferred from any other then it is its own simplest description. We can exhibit
it, but we cannot describe it by a simpler structure. The hierarchic structures wehave been discussing have a high degree of redundancy, hence can often be
described in economical terms. The redundancy takes a number of forms, ofwhich I shall mention three:1. Hierarchic systems are usually composed of only a few different kinds of subsystems invarious combinations and arrangements. A familiar example is the proteins, theirmultitudinous variety arising from arrangements of only twenty different amino acids.
Similarly the ninety-odd elements provide all the kinds of building blocks needed for an
infinite variety of molecules. Hence we can construct our description from a restrictedalphabet of elementary terms corresponding to the basic set of elementary subsystems fromwhich the complex system is generated.
2. Hierarchic systems are, as we have seen, often nearly decomposable. Hence only
aggregative properties of their parts enter into the description of the interactions of thoseparts. A generalization of the notion of near decomposability might be called the ''empty
world hypothesis" most things are only weakly connected with most other things; for atolerable description of reality only a tiny fraction of all possible interactions needs to betaken into account. By adopting a descriptive language that allows the absence of something
to go unmentioned, a nearly empty world can be described quite concisely. Mother Hubbard
did not have to check off the list of possible contents to say that her cupboard was bare.
3. By appropriate "recoding," the redundancy that is present but unobvious in the structure of
a complex system can often be made patent. The commonest recoding of descriptions ofdynamic systems consists in replacing a description of the time path with a description of adifferential law that generates that path. The simplicity resides in a constant relation between
the state of the system at any given time and the state of the system a short time later. Thus
the structure of the sequence 1 3 5 7 9 11 . . . is most simply expressed by observing thateach member is obtained by adding 2 to the previous one. But this is the sequence that
Galileo found to describe the velocity at the end of successive time intervals of a ball rolling
down an inclined plane.

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It is a familiar proposition that the task of science is to make use of the world's
redundancy to describe that world simply. I shall not pursue the general
methodological point here, but I shall instead take a closer look at two maintypes of description that seem to be available to us in seeking an understandingof complex systems. I shall call these state description and process description,
respectively.
State Descriptions and Process Descriptions"A circle is the locus of all points equidistant from a given point." "To constructa circle, rotate a compass with one arm fixed until the other arm has returned toits starting point." It is implicit in Euclid that if you carry out the process
specified in the second sentence, you will produce an object that satisfies thedefinition of the first. The first sentence is a state description of a circle; thesecond, a process description.
These two modes of apprehending structures are the warp and weft of ourexperience. Pictures, blueprints, most diagrams, and chemical structural formulas
are state descriptions. Recipes, differential equations, and equations for chemical
reactions are process descriptions. The former characterize the world as sensed;
they provide the criteria for identifying objects, often by modeling the objects
themselves. The latter characterize the world as acted upon; they provide themeans for producing or generating objects having the desired characteristics.
The distinction between the world as sensed and the world as acted upon defines
the basic condition for the survival of adaptive organisms. The organism must
develop correlations between goals in the sensed world and actions in the worldof process. When they are made conscious and verbalized, these correlations
correspond to what we usually call means-ends analysis. Given a desired state ofaffairs and an existing state of affairs, the task of an adaptive organism is to findthe difference between these two states and then to find the correlating processthat will erase the difference.22
Thus problem solving requires continual translation between the state andprocess descriptions of the same complex reality. Plato, in the Meno,
22.

See H. A. Simon and A. Newell, "Simulation of Human Thinking," in M. Greenberger (ed.),Management and the Computer of the Future (New York: Wiley, 1962), pp. 95 114, esp. pp. 110 ff.

Page 211

argued that all learning is remembering. He could not otherwise explain how we
can discover or recognize the answer to a problem unless we already know theanswer.23 Our dual relation to the world is the source and solution of the paradox.
We pose a problem by giving the state description of the solution. The task is to
discover a sequence of processes that will produce the goal state from an initialstate. Translation from the process description to the state description enables us
to recognize when we have succeeded. The solution is genuinely new to us and
we do not need Plato's theory of remembering to explain how we recognize it.
There is now a growing body of evidence that the activity called human problem
solving is basically a form of means-ends analysis that aims at discovering aprocess description of the path that leads to a desired goal. The general paradigm
is: Given a blueprint, to find the corresponding recipe. Much of the activity ofscience is an application of that paradigm: Given the description of some natural
phenomena, to find the differential equations for processes that will produce thephenomena.
The Description of Complexity in Self-Reproducing Systems
The problem of finding relatively simple descriptions for complex systems is ofinterest not only for an understanding of human knowledge of the world but also
for an explanation of how a complex system can reproduce itself. In my
discussion of the evolution of complex systems, I touched only briefly on therole of self-reproduction.
Atoms of high atomic weight and complex inorganic molecules are witnesses tothe fact that the evolution of complexity does not imply self-reproduction. If
evolution of complexity from simplicity is sufficiently probable, it will occur
repeatedly; the statistical equilibrium of the system will find a large fraction of
the elementary particles participating in complex systems.
If, however, the existence of a particular complex form increased the probabilityof the creation of another form just like it, the equilibrium between complexes
and components could be greatly altered in favor of the former. If we have a
description of an object that is sufficiently clear
23.

The Works of Plato, B. Jowett, translator (New York: Dial Press, 1936), vol. 3, pp. 26 35. See H.
A. Simon, "Bradie on Polanyi on the Meno Paradox," Philosophy of Science, 43 (1976):147 150.

Page 212

and complete, we can reproduce the object from the description. Whatever theexact mechanism of reproduction, the description provides us with the necessary
information.
Now we have seen that the descriptions of complex systems can take many
forms. In particular we can have state descriptions, or we can have processdescriptions blueprints or recipes. Reproductive processes could be built aroundeither of these sources of information. Perhaps the simplest possibility is for thecomplex system to serve as a description of itself a template on which a copy canbe formed. One of the most plausible current theories, for example, of the
reproduction of deoxyribonucleic acid (DNA) proposes that a DNA molecule, inthe form of a double helix of matching parts (each essentially a "negative" of theother), unwinds to allow each half of the helix to serve as a template on which a
new matching half can form.
On the other hand, our current knowledge of how DNA controls the metabolismof the organism suggests that reproduction by template is only one of theprocesses involved. According to the prevailing theory, DNA serves as atemplate both for itself and for the related substance ribonucleic acid (RNA).
RNA in turn serves as a template for protein. But proteins according to current
knowledge guide the organism's metabolism not by the template method but byserving as catalysts to govern reaction rates in the cell. While RNA is a blueprint
for protein, protein is a recipe for metabolism.24
Ontogeny Recapitulates Phylogeny
The DNA in the chromosomes of an organism contains some, and perhaps most,of the information that is needed to determine its development and activity. We
have seen that, if current theories are even approximately correct, the informationis recorded not as a state description of the organism but as a series of"instructions" for the construction and maintenance of the organism fromnutrient materials. I have already used the24.

C. B. Anfinsen, The Molecular Basis of Evolution (New York: Wiley, 1959), chapters 3 and 10,
will qualify this sketchy, oversimplified account. For an imaginative discussion of some mechanismsof process description that could govern molecular structure, see H. H. Pattee, "On the Origin of
Macromolecular Sequences," Biophysical Journal, 1 (1961):683 710.

Page 213

metaphor of a recipe; I could equally well compare it with a computer program,which is also a sequence of instructions governing the construction of symbolic
structures. Let me spin out some of the consequences of the latter comparison.
If genetic material is a program viewed in its relation to the organism it is a
program with special and peculiar properties. First, it is a self-reproducing
program; we have already considered its possible copying mechanism. Second, it
is a program that has developed by Darwinian evolution. On the basis of ourwatchmakers argument, we may assert that many of its ancestors were also
viable programs programs for the subassemblies.
Are there any other conjectures we can make about the structure of this program?
There is a well-known generalization in biology that is verbally so neat that wewould be reluctant to give it up even if the facts did not support it: ontogeny
recapitulates phylogeny. The individual organism in its development goes
through stages that resemble some of its ancestral forms. The fact that the humanembryo develops gill bars and then modifies them for other purposes is a familiar
particular belonging to the generalization. Biologists today like to emphasize thequalifications of the principle that ontogeny recapitulates only the grossest
aspects of phylogeny, and these only crudely. These qualifications should not
make us lose sight of the fact that the generalization does hold in roughapproximation it does summarize a very significant set of facts about theorganism's development. How can we interpret these facts?
One way to solve a complex problem is to reduce it to a problem previously
solved to show what steps lead from the earlier solution to a solution of the newproblem. If around the turn of the century we wanted to instruct a workman tomake an automobile, perhaps the simplest way would have been to tell him how
to modify a wagon by removing the singletree and adding a motor andtransmission. Similarly a genetic program could be altered in the course ofevolution by adding new processes that would modify a simpler form into a more
complex one to construct a gastrula, take a blastula and alter it!
The genetic description of a single cell may therefore take a quite different form
from the genetic description that assembles cells into a multi celled organism.
Multiplication by cell division would require as a

Page 214

minimum a state description (the DNA, say), and a simple "interpretive process"to use the term from computer language that copies this description as a part ofthe larger copying process of cell division. But such a mechanism clearly would
not suffice for the differentiation of cells in development. It appears more natural
to conceptualize that mechanism as based on a process description and asomewhat more complex interpretive process that produces the adult organism ina sequence of stages, each new stage in development representing the effect of an
operator upon the previous one.
It is harder to conceptualize the interrelation of these two descriptions.
Interrelated they must be, for enough has been learned of gene-enzyme
mechanisms to show that these play a major role in development as in cell
metabolism. The single clue we obtain from our earlier discussion is that thedescription may itself be hierarchical, or nearly decomposable, in structure, thelower levels governing the fast, "high-frequency" dynamics of the individual celland the higher-level interactions governing the slow, "low-frequency" dynamics
of the developing multicellular organism.
There is a rapidly growing body of evidence that the genetic program isorganized in this way.25 To the extent that we can differentiate the genetic
information that governs cell metabolism from the genetic information thatgoverns the development of differentiated cells in the multicellular organization,
we simplify enormously as we have already seen our
25.

For extensive discussion of these matters, see J. D. Watson, op. cit., especially chapters 8 and 14.
For a review of some of the early evidence, see P. E. Hartman, "Transduction: A Comparative
Review," in W. D. McElroy and B. Glass (eds.), The Chemical Basis of Heredity (Baltimore: Johns
Hopkins Press, 1957), pp. 442 454. Evidence for differential activity of genes in different tissues andat different stages of development is discussed by J. G. Gall, "Chromosomal Differentiation," in W.
D. McElroy and B. Glass (eds.), The Chemical Basis of Development (Baltimore: Johns Hopkins
Press, 1958), pp. 103 135. Finally, a model very like that proposed here has been independently, andfar more fully, outlined by J. R. Platt, "A 'Book Model' of Genetic Information Transfer in Cells andTissues," in M. Kasha and B. Pullman (eds.), Horizons in Biochemistry (New York: Academic Press,
1962), pp. 167 187. Of course this kind of mechanism is not the only one in which development
could be controlled by a process description. Induction, in the form envisaged in Spemann'sorganizer theory, is based on a process description in which metabolites in already formed tissue
control the next stages of development.

Page 215

task of theoretical description. But I have perhaps pressed this speculation far
enough.
The generalization that we might expect ontogeny partially to recapitulate
phylogeny in evolving systems whose descriptions are stored in a process
language has applications outside the realm of biology. It can be applied asreadily, for example, to the transmission of knowledge in the educational
process. In most subjects, particularly in the rapidly advancing sciences, theprogress from elementary to advanced courses is to a considerable extent a
progress through the conceptual history of the science itself. Fortunately therecapitulation is seldom literal any more than it is in the biological case. We do
not teach the phlogiston theory in chemistry in order later to correct it. (I am notsure I could not cite examples in other subjects where we do exactly that.) But
curriculum revisions that rid us of the accumulations of the past are infrequentand painful. Nor are they always desirable partial recapitulation may, in many
instances, provide the most expeditious route to advanced knowledge.
Summary: The Description of Complexity
How complex or simple a structure is depends critically upon the way in which
we describe it. Most of the complex structures found in the world are enormously
redundant, and we can use this redundancy to simplify their description. But touse it, to achieve the simplification, we must find the right representation.
The notion of substituting a process description for a state description of nature
has played a central role in the development of modern science. Dynamic laws,
expressed in the form of systems of differential or difference equations, have in alarge number of cases provided the clue for the simple description of thecomplex. In the preceding paragraphs I have tried to show that this characteristicof scientific inquiry is not accidental or superficial. The correlation between state
description and process description is basic to the functioning of any adaptive
organism, to its capacity for acting purposefully upon its environment. Ourpresent-day understanding of genetic mechanisms suggests that even indescribing itself the multicellular organism finds a process description agenetically encoded program to be the parsimonious and useful representation.

Page 216

Conclusion
Our speculations have carried us over a rather alarming array of topics, but that isthe price we must pay if we wish to seek properties common to many sorts ofcomplex systems. My thesis has been that one path to the construction of a
nontrivial theory of complex systems is by way of a theory of hierarchy.
Empirically a large proportion of the complex systems we observe in nature
exhibit hierarchic structure. On theoretical grounds we could expect complex
systems to be hierarchies in a world in which complexity had to evolve from
simplicity. In their dynamics hierarchies have a property, near decomposability,that greatly simplifies their behavior. Near decomposability also simplifies thedescription of a complex system and makes it easier to understand how the
information needed for the development or reproduction of the system can be
stored in reasonable compass.
In science and engineering the study of "systems" is an increasingly popular
activity. Its popularity is more a response to a pressing need for synthesizing andanalyzing complexity than it is to any large development of a body of knowledge
and technique for dealing with complexity. If this popularity is to be more than afad, necessity will have to mother invention and provide substance to go with thename. The explorations reviewed here represent one particular direction of
search for such substance.

Page 217

Becker, Gary S., 164
Dahlberg, A. O., 14
Berliner, Hans, 90
Dansereau, Donald F., 69

Name Index

Bigelow, J., 5, 172
Dawkins, R., 44

A

Boulding, Kenneth, 173
de Finetti, B., 116

Akin, Ömer, 92, 131

Bradshaw, Gary, x, 107

Alchian, A. A., 46

Brooks, R., 52

Alexander the Great, 196

Bruner, J. S., 61

Allen, T. F. H., 175

Bugelski, B. R., 65

de Groot, Adriaan D., 71
de Groot, S. R., 202
Debreu, Gerard, 33
Dym, Clive L., 128, 131, 135
Amarel, Saul, 132

Burks, A. W., 180

Anderson, J. R., 104, 105Bush, Vannevar, 166

Ando, Albert, 198

Ebbinghaus, Hermann, 64, 65
CElsasser, W. M., 184, 207

Andronov, A. A., 177

Carson, Daniel F., 21
Elstein, Arthur, 88

Anfinsen, C. B., 212

Chase, William, 71
Ericsson, Anders, x

Anzai, Y., x, 105

Chomsky, Noam, 75

Aristotle, 3Coles, L. Stephen, xiii, 78-79

Arrow, Kenneth, 33, 35, 116
Ashby, W. R., 173, 194

Compton, Karl Taylor, xi, xiii,
112-113Austin, G. A., 61

Corbett, A. T., 105
Corner, George W., xiii

B

Cournot, Augustin, 37

Babbage, Charles, 19

Courtois, P. J., 198

Barnard, Chester, 153

Cvitanovic *, P., 176

Bartlett, F., 54, 62
Baylor, George W., 127

E

F
Farmer, J. D., 181
Feigenbaum, E. A., xiii, 21, 64,
66
Feigenbaum, M. J., 177
Feldman, Julian, 21
Fisher, Bobby, 91
Flory, P. J., 191
Friedman, M., 29, 46

D
Dahl, Robert A., 31

Page 218

K

Manheim, Marvin L., 125-126
Kacser, H., 192

March, J. G., 41, 155, 162

Gaither, H. Rowan, xi, xiii

Kadane, J. B., 127

Marshall, Alfred, 74-75

Gall, J. G., 214

Kahneman, D., 29

Marx, Karl, 139

Gallhofer, I. N., 30

Kaplan, C. A., 109

McCarthy, John, 18, 194

Gerard, Ralph W., 173

Katona, George, 39, 101

McGeoch, J. A., 65

Gobet, Fernand, x, 90

Katz, Jerrold J., 76

McLean, R. S., 69, 70, 71

Gode, D. J., 32

Koedinger, K. R., 105

Meadows, Donella, 147

Gombrich, E. H., 206

Korf, R. E., 131

Meier, Richard H., xiii

Goodnow, J. J., 61

Kotovsky, Kenneth, 106

Michon, John A., 132

Gordon, I. I., 177

Kulkarni, Deepak, xMiller, George A., 67, 206

Grason, John, xiii, 129

Kunreuther, H., 29

Miller, J. G., 173

GGreenberg, Joseph H., 76

Minsky, Marvin L., 9, 18

L

Gregg, Lee W., xiii, 59, 64, 69,
70, 71
H

Lange, Oscar, 34

Modigliani, Franco, 149

Langley, Patrick W., x, xiii,
107

Monod, J., 205
More, Sir Thomas, 139

Hamilton, Alexander, 140

Langton, C. G., 180, 181

Hartman, P. E., 214

Larkin, Jill, x, 75

Hayes, John R., 92, 95

Lawrence, T. E., 196

Hillier, L. A., 137

Le Moigne, Jean-Louis, x

Holland, J. H., 180, 206

Lenat, D. B., 106Holt, Charles C., 149

Leonardo, Anthony, x, 75

Hovland, Carl I., 64-65

Leontovich, E. A., 177

Hurwicz, Leo, 33

Lindblom, Charles E., 31

Newell, Allen, x, xiii, 19, 22,
54, 59, 75, 102, 194, 195, 210

Lindsay, Robert K., xiii

Newton, Isaac, 1, 64, 108

Isaacson, L. M., 137

Linschitz, H., 191, 192

Neyman, J., 116

Iwasaki, Yumi, x, 198

Lorenz, E. N., 176

Norman, D. A., 68

Lotka, A. J., 173

Novak, Gordon S., 96

Morgenstern, Oscar, 37
Mozart, W. A., 91
Muth, J. F., 38, 149
N
Nelson, R. R., 48
Neves, D. M., xiii, 104-105

I

J

Lucas, R. E., 38

Jacob, F., 205

Luce, R. D., 37

Jacobson, H., 189
Jay, John, 140

M

Johnson, Samuel, 33

Madison, James, 140

Jørgenson, Jørgen, 118

Maier, A. G., 177

Page 219

Saris, W. E., 30
Savage, L. J., 116PVvan der Rohe, Mies, 151

Pattee, H. H., 212

Schaeffer, J., 90

Pauling, Linus, 203

Schoene, Andrew, xiii

Pelletier, R., 105

Schrödinger, E., 191

Pfefferkorn, Charles E., 133

Searle, John, 79

Phillips, A. W., 14

Setlow, R. B., 189, 191, 201

Plante, R. D., 198

Shalla, Leon, 21

Plato, 139, 210, 211

Shannon, Claude E., 18, 173,
194

Wald, Abraham, 116

Platt, John R., xiii, 184, 207,
214

Shaw, J. C., 194

Walter, W. Grey, 52, 173

Poincaré, H., 176

Shen, Weimin, x, 106

Watson, J. D., 195, 214

Polgar, Judit, 91

Shepard, Roger N., 68

Waugh, N. C., 68

Pollard, E. C., 189, 191, 201

Siklóssy, Laurent, xiii, 78-79,
100

Weaver, W., xiii, 183

Simon, Arthur, 10

Wilde, Oscar, 159

Simon, Dorothea P., 194

Wilkes, Maurice V., 20

Skinner, B. F., 70

Williams, Donald S., xiii

Van Lehn, Kurt, 13
von Bertalanffy, L., 173
von Hayek, Friederich, 34
von Neumann, J., 18, 37, 82,
116
W

Wiener, Norbert, 5, 172, 194

Poznyanskaya, E. D., 73
Prescott, E. C., 38
Q
Qin, Yulin, x, 75, 108

Smith, V. L., 32

Quillian, Ross, xiii

Williams, Thomas G., xiii

Smithburg, D. W., 155

R

Williamson, O. E., 40

Smuts, J. C., 170
Radner, Roy, 38

Starr, T. B., 175

Raiffa, H., 37

Staszewski, J. J., x, 66

Ramsey, F. P., 116

Stevin, Simon, 1-2

Rasmussen, S., 181

Stigler, G. J., 164

Reitman, Walter R., 137

Sunder, S., 32

Richards, I. A., 78

Sutherland, Ivan E., 133

Richman, Howard, x, 66
Robinson, George A., 21
Rogers, D. F., 198
Rosenblueth, A., 5, 172
Russell, Bertrand, 16
S

T
Tabachneck, Hermina, x, 75
Taylor, C., 181
Thom, R., 175
Thompson, V. A., 155
Tikhomirow, O. K., 73

Sargent, T. J., 38, 39

Tversky, A., 29

Winter, S. G., 46, 48
Wise, William, xiii
Wos, Lawrence, 21Z
Zhang, Guojung, xZhu, Xinming, x, 105
Zytkow, Jan, x, 107finding, 121-124

bottlenecks in, 143

generation of, 129

management of, 161-162

Altruism, 45, 157

Authority, formal, 185

Page 221

Subject Index

AM program, 106-108

A

Analysis, and design, 111
Actions, 122-123

Anarchism, 155. See also
Libertarianism

basic, 19, 22
Ant, parable of the, 63
component, 123
Architectural design, 129, 132
of productions, 102
Architecture, 92, 111, 151
Adaptation, 8, 46, 48, 100.
See also Problem solving;
Rationality, limits ofArchitecture of mind,
simplicity of, 86-87
Artifacts, 3, 10, 22

limits of, 12
as ''interface", 6-7
predictive, 8
computers as, 17-18
Adaptive expectations, 39
symbol systems as, 21-23
Adaptive organisms, 210
Artificial, the, 3-4, 110, 111
Adaptive production system,
103
Adaptive systems, 8, 16-17, 82,
169, 172-173

creating the, 111-138
defined, 4
psychology as science of, 54, 75

Adaptivity, 22, 110
sciences of, 111-112
Administration. See
Organizations

versus synthetic, 4

Aesthetics. See Music

Artificial Intelligence (AI), 4,
49

Afferent channels, 121
vs. OR, 27-28
Aggregations. See Chunk
Algebra, learning, 103
Algorithms, 27, 48, 134, 135.
See also Search strategy

Artificial systems, 16-17, 26,87
and planning, 33
Artificiality, xi-xii, 2, 13

Allocation of resources, 25,
124-127of behavior, 54, 81

for search, 134

Aspiration levels, 28-30, 160

Alternatives, 114, 116.

Association, retrieval by, 88

See also Search strategy

Associations, 69, 122

admissible, 120-121

Associative memory, 88

evaluation of, 129

Attention, 109, 143, 144, 165

Page 222

Catastrophe theory, 175-176
Complex designs, 163

Automata, 58, 64
Automatic design, 136
Automatic translation, 77
Automobile emission
standards, 145-146

Cellular automata, 169, 174,
180, 181

Complex information
processing, 4Chaos, 169, 174, 181
Complex problems, 213
and complexity, ix, 176-178
and rationality, 178-179

Complex systems, 7, 128, 130,
155, 173, 181, 183-184, 191,
196, 216

Chaotic systems, 176-178

Bevolution of, 188-197
Backgammon, 89-90

and dynamic systems, 176-177

BACON program, 107-108

and physical systems, 176

Bayesian decision theory, 144

weather as example, 176, 179

in planning, 126

Chess, 86, 89-91, 118-119, 195

Behaving systems. See Systems

rapid transit, 90

Behavioral sciences, 183

Chess-playing programs, 127

Biological evolution, 204-206

Chunk, 63-68, 72, 81, 86, 87,
89, 91and holism, 170
simple descriptions of, 208-210
Complexity, 21, 49, 64, 68, 8081, 99, 109-110, 165, 184, 188197
alternative views of, 169-181
architecture of, 183-216
and adaptive systems, ix

Biological sciences, 183
Biological systems, 186-187,
191

City planning, 130, 151

and artificiality, xii

Classes, of complex systems,
173

and chaos, ix, 176-178

Biology, evolutionary, 46

computational, 35Classical economics, 49

Bond strength, chemical, 201

conceptions of, 169-174
Client, 166

hierarchy of, 201

description of, 206-216
identifying the, 150-154

Boundaries, organizationmarket, 40-41

and evolution, 179-181
society as the, 153-154
and genetic algorithms, ix, 180

Bounded rationality. See
Rationality, boundedClub of Rome report, 147-148
Cognition, 75

in self-reproducing systems,
211-212human, 110of plans, 139

Brain, 23, 82-83
as symbol system, 22
Bubbles, 36

Cognitive processes,
simulation of. See Simulation

Business cycle, 36

Cognitive system, human, 100

Business education, 111

Combinatorial problems, 28

Business firm, 25-26, 28, 31,
48, 87, 185

Command variables, 116-117

Business organizations, 40-43

Communication systems,
design of, 143

like computers, 41Competition, 38
imperfect, 37

CCatastrophe, 174

perfect, 32, 36
Compiler, 93

Page 223

141
as resource allocation, 124-127

Comprehensibility, anddecomposability, 204, 207-210

Constraints, 116
as valued activity, 164
Cooperation, 38

Computational limits, 34-35,
120, 157

criteria for, 8
Coordination, methods of, 31
hierarchy in, 128-131

Computational procedures, 27,
135

through markets, 31
horizons for, 156-162
Cost-benefit analysis, 125, 146
large-scale, 139

Computational techniques,
efficient, 119

Cost curve, 25-26
logic of, 114-124

Computer-aided design, 133
Computer programs, 20, 93,
95, 129, 135, 212-213
automatic generation of, 97

Criteria, for ill-structured
problems, 106

objective, 12

Crossover, 180of buildings, 129, 120

Cryptarithmetic problems, 5458, 62, 81, 85

of evolving artifacts, 139-167

for natural language, 78
Computer reliability, 18

Curriculum, for design, 134135

Computer science, 112

for social design, 166-167

as empirical science, 18

Cybernetics, 169

Computer simulation, 21, 66,
82, 177, 180

and general systems theory,172-174

of evolutionary processes, 180181

D

organization of, 134
problems, large-scale, 161
representation of, 134, 141146
process of, 137
representation of, 131-134, 141146
science of, 111-138

Computers, 14, 21, 23, 27, 55,137

Data, for planning, 146-147

with alternatives not given, 124

information stored as, 110

Designation, 22

and thought, 21

reliability, 147

Diet problem, 116-117

as abstract objects, 18-19

Data-driven production, 103

as artifacts, 17-18

Decentralization, 41

Differences, in problem
solving, 122-123

as empirical objects, 19-21

Decision making, 12Differential equations, 177,
210, 215

business organizations as, 6Declarative logic, reduction to,115-118

Discovery processes, 105-108

stored-program, 172, 173
Concept, extensional definitionof, 59Concept, intensional definitionof, 60Concept attainment, 74, 81, 86

Decomposability, 197-204
Decomposition, 128of complex systems, 128, 184
Description, 184

limits on speed of, 59-63

Descriptive, and normative, 5,26

Conditions, of productions, 102

Design, 4, 92

Conflict of interest, 38, 153

activities, cycle of, 92

Constitution, American, 140-

and functional description, 9Disequilibrium, 175

Page 224of complex systems, 188-197
education, xii, 111-114, 134-135

DNA, 212of organizations, 44-48
profession, 152

Docility, 45
Enjoyment, capacity for, 164of multi-cellular organisms,
192-193

Entrepreneur, 25

speed of, 189

Entropy, 172, 192

test, 46and information, 189

Evolutionary processes, 45-49

Environment, as mold, 5-13,

Examples, learning from, 103105

Doing, learning by, 105E
Economic actors, 25-30, 40, 49
Economic behavior, 87
Economic Cooperation
Administration (ECA), 141143
Economic dynamics, 198
Economic evolution,mechanisms of, 48. See also
Evolution

benign, 12

Expectations, 35-40, 43

complexity of, 21

mutual, 36

external, 23, 93, 100

rational (See Rational
expectations)fragility of, 160
inner, 6, 11, 15, 17, 23, 25, 26,
54, 59-63, 67, 68, 80, 83, 87,110, 113, 116, 128, 136

Expected value, 116memory as part of, 53

Expert systems, 28

Economic system, models of,13

outer, 6, 11, 25, 54, 83, 87, 110,
113, 116, 135, 149

Explanation, 7, 16-17

Economics, xii, 30, 154

shape of, 62

as abstract rationality, 23

taxing, 12External world, information in,
98-99

Economy, the, 25, 30

EPAM (Elementary Perceiverand Memorizer), 71, 100-101

Externalities, 42, 150, 152

Economic man, 12, 46
Economic rationality, 23-24,
25-50

Ecosystem, 33
Educational process, 215
Efferent channels, 122
Egoism, 157

Expertise, 105-111

levels of, 83

Eye movements, 73
Equilibration, dynamics of, 198
Equilibrium, 32, 36, 48, 175,
178, 198. See also Strange
attractor

F
Factorization of differences,
124

Emergence, 171

Evaluation, theory of, 118-121,
134

Families, 185

weak, 171, 172

Evolution, 6, 157, 180, 211

Feedback, 35, 149, 166, 169,
173, 179

Emotion, 53

and complexity, 179-181

negative, 8Empire building, 196

and social planning, 164-166

of information, 195

Empty world hypothesis, 209

biological, 189-192, 204-206

Feedback control, 172

Ends. See Mean-ends analysis,
Energy-environment problem,
158

creative, 169

Engineering, 4, 111

generators, 46

design, 15, 116, 132

improbability of, 192

drawings, 133

myopia of, 47, 164

economic vs. biological, 48

Page 225

Hierarchy, 184-188, 204, 216
General systems, 169

Feedback Control Systems,
172. See also Servomechanism
TheoryGeneral systems theory, 173,
174

flat, 186

Feedback loops, 33

and cybernetics, 172-174

formal, 185

Feedforward, 149

Generator-test cycle, 128-130

in biological systems, 186-187

based on prediction, 36

Genetic algorithms, 169, 174,
180, 181

in design, 128-131

assumption of, 174

Fitness, 44, 45, 47, 48, 165,
180

Genetic description, 213-214

evolutionary explanation of,
196-197

and growth, 48

Genetic mechanisms, 215

in physical systems, 186-187

and profitability, 48

Genetic program, 214

in symbolic systems, 187-188

Fixation, in memory, 62, 66-67

Gestalts, 169of list structures, 77, 80

Fixation time, 68

Goal-driven production, 103

social, 186

Flexibility, 43

Goal-seeking systems, 22

Highway design, 125-126

designing for, 166

Goal attainment, 11Holism, 169, 170-172, 184

in planning, 158

Goals, 5, 12, 44

and complex systems, 170

Forecasting, 166

as motivators, 162

and living systems, 170-172

Foresight, limits of, 140-141

design without, 162-166

and reductionism, 170-172, 173

Formal organization, See
Organizations, formal,
Franchise, 40

final, 162-166

Homeostasis, 8, 149, 169, 173

in production conditions, 103

Horizons, for design, 156-163,
166

Freedom, as value in planning,
140-141of plans, 139

Functional analysis, 17, 128
Functional description, 9-12,
19

problem solving without, 106

time and space, for design, 156162

Government, as organization,
155

House floor plans, 133

Functional equivalence, 9Grammar. See Syntax,
Gravitational systems, 179

Functional explanation, 7-8

Growth, as fitness test, 48

Future, attitudes toward, 157162

Human nature, malleability of,152
Human problem solving. See
Problem solving,
Hyperinflation, 36

H
Hypothesis testing, 63
Happiness, human, 29, 160

discount rate, 157-158
significance for planning, 147148
G
Games, theory of, 37-38, 166
and social planning, 153-154
General Problem Solver (GPS),94, 122, 172

Heuristic search, 20, 27-28, 48,
87, 106, 134, 194, 195-196

I
Identification, 43-45

selectivity in, 194, 195-196

consequences of, 43-44

Hierarchic description, 214evolutionary basis for, 44-45

Hierarchic span, 202-204
Hierarchic systems, 134, 184188, 197-198, 209

Page 226

social rate of, 157-159

Ill-defined tasks, 98

Interface, 6, 9, 83, 113

Imageless thought, 70

Interpretation, of programs, 93

Images, mental, 98Interpretive process, 214

visual, 70

Interruption and memory, 67

Imperatives, logic of. See
Logic, imperative,
Improbability, of evolution,
192

Intuition, 89-90

Learning programs, 86, 102105
Levels, of explanation. See also
Hierarchy, Explanation
Libertarianism, 155. See also
Anarchism
Library of Congress, 99Life of the Mind, 135-138

Invariants, 100, 177.

Limiting resources, 143-144

See also Universal numbers
Index, of LTM, 88, 99, 101,
110

Linear differential equations,176

of human thinking, 110
Inversion, 180

Indirect effects, detection of,159

Linear programming, 27, 117,
134

Invisible Hand, The, 32
Information, 169

Linguistic theory, 77-80

Irreversible commitments, 163
filtering systems, 144

LISP, 104, 106

ISAAC program, 96-98
gathering, search as, 127
in LTM, quantity of, 91
quantity of, 212
and skill acquisition, 100-101

Input-output matrix, 199
Instability, 37, 38, 149
Integer programming, 27Intelligence, as computation,
23

Kepler's third law, 107

Living systems, and holism,
170-172

declarative, 115-118, 121, 122123, 134

limited, 45, 177

deontic, 115
imperative, 115, 134

L
Language, acquisition of, 80

modal, 115, 117

through pictures, 78-79

Logical inference problems, 86

universals, 76

MLanguage processing,semantics in, 77-80

Magic square, 131-132

Learning, 100-105

Magnetic Resonance Imaging.
See also MRI, 82

from examples, 103-105
incremental, 64Interaction, intensity of, 187

multiplicity of forms of, 100

social, 186

one-trial, 64Interdependence, international,
159

rote and meaningful, 101

Interest, rate of, 157-158

Logic, 114-118

transmission of, 215

theories, 66, 70-71, 74, 77

Initial conditions, establishing,163

linked, 88

from simulation, 15

human, 62-64, 203

Information theory, 172, 173,
189

Judgment, 28, 135

Knowledge, and skill, 93

Information-processing system,
22, 102

Information superhighway, 144

List structure, 71, 74, 77, 79,
82, 86, 96, 99

Jwith understanding, 101Page 227

Management information
systems, 144Management science, 27, 111,
116
Marginal cost, 145
Marginal returns, 145Market economy, vs.
organization & market
economy, 32, 35, 40-41, 46
Markets, 30-45
and optimality, 32-33
and social coordination, 30-31,
49

external, 61, 91-92
for processes, 93-94

Nhuman, 65, 99

NASA, 139-140

information in, 90-93

Nativist theory of language
acquisition, 76

long-term (LTM), 62, 81, 85,
87-88, 98
organization of, 68-75
parameters of, 63-67
short-term (STM), 61, 62, 67,
81, 82, 89, 102-103
capacity of, 68
visual, 70-74
Mental arithmetic, 68

clearing, 32
Merit, figures of, 119
function of, 31
Mind, human, 76, 81-83
MATER program, 127
and neurophysiology, 82-83
Mathematical representations,133

Myopia of evolution, 47, 157

Missionaries and Cannibals
problem, 85

Maxima, local vs. global, 4647

Models, three-dimensional, 134

Maximization, 116

Motivation, 53n.

Maze problem, 54, 58, 123,
193, 195

Motor channels, 121

Natural language processing,75-80, 95, 98, 134
semantics in, 77-79
Natural laws, 2, 12, 15, 18,108, 113, 118
Natural science, 1, 6, 16, 111,
136
Natural selection, 7, 46, 180
problem solving as, 193-195
Natural systems, 175
Near decomposability, 209,
214, 216
assumption of, 174
and comprehensibility, 204,
207-208
Nearly decomposable systems,
197-204

Motor controller, design of, 10

Neo-Darwinian evolutionary
theory, 44

Meaning. See Semantics,
Meaningfulness, 65

Motor sequences, 122-123

Nerve cells, 187

Means-ends analysis, 94, 121122, 124, 134, 210

MRI. See also Magnetic
Resonance Imaging, 82

Medical, care, cost, andquality, 152

Multi-cellular organisms,
evolution of, 192-193

diagnosis, 86, 88, 93

Music, 136

education, 111, 112

composition of, 91, 129

profession, 152

computer composition of, 136137

Memorizing, 81
enjoyment of, 164
Memoryhierarchy in, 188
associative, 69, 81-82
Mutation, 180
capacity of, 58
compared with encyclopedia, 88

Mutilated checkerboard
problem, 109

Page 228

members, as designers, 153

Physics,

Neurophysiology, 82-83

properties of, 17

rediscovering, 107-108

and psychology, 21

as representations, 141-143

understanding, 96-98

New institutional economics(NIE), 40

rules of, 154

Physics problems, 86, 99

as social schemes, 49
in social design, 154-156, 166

Physiological psychology. See
Brain; Neurophysiology, PittsMcCulloch neurons, 18

theory of, 27, 129, 153

Planners, 33-34, 139

Niches, environmental, 165
Nonlinear equations, 177
Normative theories, 25-26
Normative, and descriptive, 4Planning, 125-126

P

Novelty, 105, 162

Painting, 92, 163

data for, 146-147, 166

Number scrabble, 131-132

Paper and pencil, 61-62

horizons, 148

Paradoxes of imperative logic,
115

social, 139-167

O

Planning groups, 162

Ohm's law, 171

Parameters, 63, 110

Ontogeny recapitulates
phylogeny, 212-215

estimation, 63, 67

Political constitutions as
designs, 140

fixation, 68-69

Possible worlds, 117, 118, 121

fixed, 116

Prediction, 13, 15, 35, 149, 177

vs. artificial intelligence, 27-28

Pareto optimality, 33, 34, 35

of global warming, 148

Optimality, 28-29, 32-33, 4142, 134

Parsimony, 92

social, 147-149

Operations research (OR), 2728, 46

Particles, elementary, 185, 186

vs. understanding, 178

Optimizing, 37-38, 46-48, 115119, 121,130, 146

Partitioning, of systems, 185

Prices, 36

Order, without planner, 33-35

Pattern discovery, 86

Prisoners' Dilemma game, 3738

Organization-market boundary,
40-41

Perception, 74
Probability, 116, 144
Perceptual productions, 103
and speed of evolution, 189

Organizations, xii, 30-35
Perceptual tests, 103
administrative, 13
Performance, limits of, 59
business, 8Periodic cycles, 175
and decentralization, 40, 41-42
Phrase structure, 76
design of, 113
hierarchic, 95
formal, 161-162
Phylogeny, 212-215
goals of, 154-155
Physical laws, 98
hierarchic, 31
Physical symbol systems, 21
and identification, 43-45
Physical systems, 186
and loyalty. See also
Identification

Physicochemical systems, 201202

Page 229

Probability distributions, 126
Problem representation, 108109, 132
Problem solving, 54, 210-211
as change in representation, 132

Profit maximization, 8, 25. See
also Satisficing

task, 68

Programs. See Computer
programs

Reductionism, and holism,
169-173

Progress, 160-161, 166

Redundancy, 101, 210, 215

and evolution, 165

Reference works. See external
memory, Reinforcement, 60

in human knowledge, 160-161

as natural selection, 193-195

Representation, 94-98, 146
moral, 160

Problem-solving programs,
126-127

adaptation of, 108
Protein, 212change in, 132

Problem, short-run and longrun, 161

Psychology, xii, 21, 63ff.
discovery of, 108-109
as science of artificial, 54-59

Procedural rationality, 26-27,
49

for new problems, 108-109
Purpose, 7internal, 22, 79

tools ofand natural law, 3
nonnumerical, 144-146

OR and AI, 27-28, 49

Q

Procedures, information storedas, 110Process as determinant of style,130-131
Process description, 210-211,
215
Processes, 22elementary, 58

of designs, 131-134, 166
Qualitative analysis, 177
Queuing theory, 27

Rof learned material, 101
of problem states, 96, 97
problem solving as, 94-95, 132

Rational expectations, 23-24,
38-39, 154spatial, 132-133
taxonomy of, 133-134

Rationality, 8, 12-13, 23, 35
Reproduction, 180, 196
and chaos, 178-179
and goals, 162

Resources, allocation of, 124127
Revenue curve, 26

Production systems, 102-103

bounded, 38, 39, 43, 44, 45,
140, 150, 166

adaptive, 103

definition of, 37, 38, 39

Productions, 102

economic, 25-50

Professional-client relations,150-153, 166

limits of, 12, 23-24, 55, 87

memory for, 93-94
Prodigies, 91

Professional education, xii, 111
and design, 111
Professional role, 150
Professional schools, 111
Profit, 33
vs. risk, 26
Profitability, as fitness test, 48

Rewards, 45
RNA, 212
Rote learning, 74
procedural, 23, 25, 26-27
substantive, 25, 26
Recall, 67
Recapitulation, 215
Recoding, 209
Recognition, 90, 93
processes, 89
retrieval by, 88Page 230

S

196, 211-212

hierarchy in, 186

Semantic knowledge, 98

near decomposability of, 200201

Satisficing, 27, 28-30, 38, 39,
46, 48, 119-121, 134, 147

Semantically rich domains, 8694, 97

Space of alternatives, 123

Scan and search strategy. See
Search, strategies for

Semantics, 77

Span, 187, 189, 190, 197of physics problems, 98

hierarchic, 201-204

Sensitivity analysis, 148of control, 187

Sensors, 22

Spatial propinquity, 187

Sequence extrapolation, 106

Spatial representation, 132-133

Serial systems, 81-82

Specialization, 92, 192, 193

Servomechanism theory, 172.
See also Feedback Control
Systems

Stability, 175of complex forms, 189

Similarity, in verbal learning,
65

State descriptions, 210-211,
214, 215

Simplicity, 68, 80-81, 100,
109-110, 138

States of the world, 148

Scarcity, 25, 26
Scenarios, 148
Schemas, 98, 102
Science, 112
and analysis, 4applied, 111, 112
artificial, 3empirical, 19
natural (See Natural science)parsimony in, 92
Search, 88, 124
best-first, 106
evaluation of, 126-127, 130
exhaustive, 55
for alternatives, 121
heuristic, sources of, 194, 195196
logic of, 122-125
selective, 55-56
strategies for, 55-59
theory of, 83, 126-127
trial-and-error, 57
Search-guiding mechanisms,
110, 126-127

and size, 98-100
of behavior, 51-53, 64

Statistical decision theory, 116,
118

of human thinking, 85

Stimuli, visual, 70

Simulation, 13

Stimulus, 60

as source of knowledge, 14-15

Stimulus-driven production,
103of cognitive processes, 4Stimulus-response pair, 103
of poorly understood systems,
15-17

Stimulus chunking, 69

techniques of, 13-14

Strange attractor, 178. See also
Equilibrium

SKETCHPAD program, 133
Strategies, 62, 110, 131-132
Social design
learning of, 62-63
curriculum for, 166
organizations in, 154-156, 166
process of, 166-167

Search tree, 127

representation in, 141-143

Selective information, 173

Social planning, 139-167

Selectivity, in search. See
Heuristic searchand evolution, 164-166
Social systems, 186

Self-reproducing program, 213
dynamics of, 200
Self-reproducing systems, 181,

Static equilibria, 175

Page 231

20

Strategy, efficient discovery,
60-61

Universal numbers, 177. See
also Invariants

Theory, normative, 26
University, 112
positive, 26

Structure, internal, 16

Urban transit planning, 154theory of, 134

Thinking. See Psychology,
Thought processes, 138

Style, 130

Time-sharing systems, 19

Subassemblies, 189, 190

Time perspectives, changes in,
158-160

Subsystems, 184, 197, 201202, 209

Time, for fixation. See Fixationtime

Symbol structures, 22

Utility, 116, 118, 144
Utility function, 26, 28, 116,130
Utopian thought, 152
V
Valence, chemical, 201, 202

Tower of Hanoi puzzle, 85,105

Symbol systems, 21
basic capabilities of, 22

Values. See Evaluation; Search

Transformational linguistics,
75

Symbolic systems, 187-188

Variety, trend in, 165
Vectors, 180

Traveling salesman problem,
120

Symbols, 2, 22
Syntactic ambiguities, 78-79

Synthesis, 9Tree, for search. See also
Problem solving; Searchstrategy, Trial-and-error search,
193

Synthetic, the, 4

selective, 194

System evolution, 179-180

Turbulence, 177, 179

System, inner. See
Environment, inner

Two cultures, 136-138

Syntax, 77, 79-80

Visual memory. See Memory,visualVisual stimuli, 81
W
Watchmaker parable, 188-189,
191, 192, 193, 195, 213
Whorfian hypothesis, 80
Working, backward, 103

U
Systems, hierarchic, 183-216

forward, 103
Uncertainty, 27, 35, 42-43, 153

Systems, study of, 216
and expectations, 35-39
T
Table of connections, 122
Target states, alternative, 148

and standardization andcoordination, 42
UNDERSTAND program, 9596, 97, 101

Task domains, professional, 86
Task environment, 12, 83

Understanding, and learning,
101

Taxation, 45

and representation, 94-98

Tea Ceremony problem, 94, 99

by simulating, 13-17

Technology, 139, 150

processes of, 95-96

medical, 152

vs. prediction, 178

Teleology, 172

Understanding stories, 98

The Game of Life, 180-181

Understanding systems, 100101

Theorem proving by computer,