# Presentation: 090118

## Social Complexity & Agent Simulation

**David L. Sallach**
**Social Science Research Computing**
**University of Chicago**
**sallach@uchicago.edu**

## Social Informatics

**Social informatics is a systematic, interdisciplinary study of the design, uses and consequences of information technologies (IT) in various settings**
**Focus:  interaction between IT and its institutional and cultural contexts.**
**It is the study of the social aspects of computers, telecommunications, and related technologies**
**Examining issues such as the ways that IT shapes organizational and social relations**
**How social forces influence the design and use of  IT**
*Rob Kling*
**(Consider Social ***Science*** Informatics)**

## Social Science Informatics

**Social Science Informatics**
**Ethnography**
**Statistical analysis**
**(Evolutionary) Game theory**
**Social Agent Modeling & Simulation (SAMS) **
**Each has corresponding, evolving epistemic communities**
**We can best understand the future of SAMS through comparative analysis with earlier epistemologies**

## Ethnography as Epistemology

**Ethnographic fieldwork**
**oral and life histories**
**archival work**
**textual analysis**
**interviewing**
**panel discussions**
**focus groups**
**media content analysis**
**participant observation**
**Ethnographic communities**
**Qualitative data analysis**
**Using the versatility of language**

## Statistical Analysis as Epistemology

**M.S. Barlett thrice assessed the history of statistical methodology in fifty year chunks (1940; 1965; 1990).**
**Beginning by emphasizing chance, ending with chaos, and noting the dramatic rise of computational techniques.**
**The informatics of statistical analysts has evolved dramatically in those fifty years.**

## From Calculation to Computation

**Available tools have defined the nature of statistical analysis and the associated community**
**Randall Collins:**
**Rapid-discovery, high consensus science emerges, not from conceptual innovation, but from shared epistemic tools, and the communities that use them**
**The role of statistical tools increases as the focus turns to computation**

## FORTRAN Statistical Code

**SUBROUTINE cov(nreq, var, covmat, dimcov, sterr, ifault)**
**!     ALGORITHM AS274  APPL. STATIST. (1992) VOL.41, NO. 2**
**!     Calculate covariance matrix for regression coefficients for the**
**!     first nreq variables, from an orthogonal reduction produced from**
**!     AS75.1.**
**!     Auxiliary routine called: INV**
**!--------------------------------------------------------------------------**
**INTEGER, INTENT(IN)                   :: nreq, dimcov**
**INTEGER, INTENT(OUT)                  :: ifault**
**REAL (dp), INTENT(OUT)                :: var**
**REAL (dp), DIMENSION(:), INTENT(OUT)  :: covmat, sterr**
**!     Local variables.**
**INTEGER                :: dim_rinv, pos, row, start, pos2, col, pos1, k**
**REAL (dp)              :: total**
**REAL (dp), ALLOCATABLE :: rinv(:)**
**!     Check that dimension of array covmat is adequate.**
**IF (dimcov < nreq*(nreq+1)/2) THEN**
**  ****ifault = 1**
**  ****RETURN**
**END IF**

**CALL INV(nreq, rinv)**
**pos = 1**
**start = 1**
**DO row = 1, nreq**
**  ****pos2 = start**
**  ****DO col = row, nreq**
**    ****pos1 = start + col - row**
**    ****IF (row == col) THEN**
**      ****total = one / d(col)**
**    ****ELSE**
**      ****total = rinv(pos1-1) / d(col)**
**    ****END IF**
**    ****DO K = col+1, nreq**
**      ****total = total + rinv(pos1) * rinv(pos2) / d(k)**
**      ****pos1 = pos1 + 1**
**      ****pos2 = pos2 + 1**
**    ****END DO ! K = col+1, nreq**
**    ****covmat(pos) = total * var**
**    ****IF (row == col) sterr(row) = SQRT(covmat(pos))**
**    ****pos = pos + 1**
**  ****END DO ! col = row, nreq**
**  ****start = start + nreq - row**
**END DO ! row = 1, nreq**

## Scripts for Statistical Packages

**TITLE  STEP SPSSFILE.SPS :  Simple Test for SPSS **
**SET CASE=UPPER/WIDTH 80.                                                                                                                               **
**SHOW ALL.                                                                                                                                                    **
**                                                                                **
**DATA LIST  NOTABLE FIXED/1 OVERALL PRODUCTV COOPRATV INITIATV 1-8               **
**                       ****ABSENTEE 10-12 EMPLOYID 14-18 APTITUDE 20-23             **
**                       ****INIRATNG 25-28 (A).                                      **
**N OF CASES     40.                                                              **
**VAR LABELS     OVERALL  EMPLOYEES OVERALL RATING BY SUPERVISOR.                 **
**VAR LABELS     INIRATNG INITIAL RATING BY PERSONNEL DEPT.                       **
**VALUE LABELS   OVERALL TO INITIATV (1) POOR (2) FAIR (3) GOOD                   **
**                                   ****(4) VERY GOOD (5) EXCELLENT.                 **
**MISSING VALUES APTITUDE (-999).                                                 **
**                                                                                                                                                               **
**SUBTITLE  ***********PROCEDURE CROSSTABS****************                        **
**RECODE PRODUCTV INITIATV (1,2=1) (3=2) (4,5=3).                                 **
**VALUE LABELS PRODUCTV INITIATV 1'POOR OR FAIR' 2'GOOD'                          **
** ****3'VERYGOODEXCELLEN'.                                                           **
**CROSSTABS TABLES=PRODUCTV BY INITIATV                                           **
** ****/CELLS=ROW COLUMN RESID                                                        **
** ****/STAT=CHISQ.                                                                   **
**                                                                        **
**LIST.                                                                                                                                                    **
**FINISH. **

## Statistical Analysis Evolves

- As statistical tools evolve, and the epistemic community adapts, supported types of analysis become easier

## Transcending General Linear Reality			Andrew Abbott

**Fixed entities with attributes that can change**
**In practice, biological individuals, state units & other stable entities**
- less often applied to professions, occupations & social movements whose members & boundaries are constantly changing
**ignores entity change through birth, death, amalgamation & division**
- some strategies, like multidivisional form (MDF), change sample frame
**transformation of entities can only be modeled at a micro level**

## More Limits of Linear Assumptions (Abbott continued)

**ASSUMPTIONS**
**Monotonic causal flow**
**time series uniform temporal units**
**Univocal meaning**
**semantics are generalized and stable**
**The absence of sequence effects**
**Absence of path dependencies**
**Casewise independence**
**No excessive interactions within a case**

## Informatics of Game Theory

**The development of game theory demonstrates the potential significance of mathematical models in modeling social processes**
**The initial community was primarily mathematical, with subsequent growth in the social sciences, especially in economics & political science**
**The epistemic community designs models that are interactive and strategic**

## Serious Stakes

**Game theory played an important role in the cold war**
**Two superpowers, armed with devastating weaponry made it through five decades of tensions without a global war**
**Although game theory required little technology at the outset, some practitioners ultimately acquired substantial tools**
** ... ****like situation rooms**

## Game Theory Evolves

**Game theory is a formalization designed to represent interactive strategies**
**Its representation has evolved over time**
- two-player to N-player
- single-play to infinite play
- transferable to non-transferable utilities
- expected to revealed preferences
**Game theory now relies on computation for simulation**
*Evolutionary game theory ***draws upon & interacts with other research programs:**
- Complex adaptive systems
- Multiagent systems

## Evolution of Cooperation

**Axelrod’s PD computer tournament**
- (Two-person iterated, *E. coli* of the social sciences)
**63 submitted strategies in pair-wise competition**
**After 1000 generations, Axelrod reports that Tit-for-Tat was most successful strategy**
**No mutations considered, so Tit-for-Tat success was ecological rather than evolutionary**

## Qualities underlying Tit-for-Tat’s Success

**Nice**** ****(never first to defect)**
- prevents it from getting in unnecessary trouble
**Provocable**** ****(retaliates)**
- discourages other side from persisting in defection
**Forgiving**
- helps restore mutual cooperation
**Clear**
- behavioral pattern easy to recognize
- best response is to cooperate

**Notes:**
P. 176

## An Alternate View

**Tit-for-Tat only succeeded 15% of time**
**Other strategies followed closely**
**Ken Binmore (1994):**
**Not actually true that Tit-for-Tat “won” Axelrod’s competition.  **
**The strategy that actually “won” is a mixed strategy that calls for Tit-for-Tat to be used less than 15% of the time.**
**Ti****t-for-Tat “success” due to nature of initial population of strategies a.m.a. intrinsic merit**

## Co-Evolutionary Considerations

**Linster starts with a population of 26 two-state strategies**
- After 1000 generations, Grim (unforgiving) captures more than half the population
**Probst uses a GA to mutate strategies**
- Allows finite automata of up to 25 states and considers time horizons of up to 10,000 generations
- Naïve reciprocators do well in the short run
- But eventually nasty automata like Tat-for-Tit displace them
**Cautionary advice**
**“**One must be very cautious indeed before allowing oneself to be persuaded to accept general conclusions extrapolated from computer simulations.  One always needs to ask how things would have changed if the circumstances under which the experiment or computer simulation was conducted had been different.”**	****	****	****	***Ken Binmore*

## SAMS Sources & Benefits

**Prerequisite:  Computational Innovation**
- object-oriented design
- distributed & parallel processing
- mobile online agents
- scientific visualization
**Special Applicability**
- Experimental approach to complex domains
- Enhance analytical intuitions
- Investigate long-duration or rare events
- Explore undesirable or destructive processes

**Notes:**
Answers will emerge over decades of research, and will depend on the nature of the specific problem under study

## Agent-Based Modeling & Simulation

**“****Agent-based modeling is a third way of doing science.  **
**Like deduction, it starts with a set of explicit assumptions.  But unlike deduction, it does not prove theorems.  **
**Instead, an agent-based model generates simulated data that can be analyzed inductively.  Unlike typical induction, however, the simulated data come from a rigorously specified set of rules rather than direct measurement of the real world.”**
*Robert Axelrod (1997)*

## Drawing upon Agent Innovation

**Agent Research Programs**
**Complex adaptive systems**
**Evolutionary game theory**
**Multiagent systems**
**Microsimulation**
**Individual-based simulation**
**How can they most effectively be combined?**
**Which types of hybrids will be most relevant to particular problem domains?**
**What inclusive formalisms can best integrate their most compelling insights?**

## Systems of Locally Interacting Agents

## Self-Forming Neighborhoods (Schelling)

**“****What is instructive about the experiment is the ‘unraveling’ process.  Everyone who selects a new environment affects the environments of those he leaves and those he moves among.”**
**“****A moderate urge to avoid small-minority status may cause a nearly integrated pattern to unravel and highly segregated neighborhoods to form.”**
**“****Getting ‘over the hump’ from one stable equilibrium to another often requires either a large perturbation or concerted action.  Acting in concert, people can achieve an alternative equilibrium.”**

## Large Patterns, Local Interactions

**Slime mold**
**Artificial ants**
**Traffic jams **
**Termites**
**Turtles & frogs**
**Turtles ecology**
**Forest fire**
**Recursive trees**

**to move-demon**
**   ****forward step-size**
**end**
**to radar-demon**
**if (ask patch [radar?])**
**    ****and (step-size > 0.8)**
**   ****[set step-size - 0.4]**
**end**

## Sugarscape

**Trade**
**Migration**
**Disease**
**Distribution of Wealth**
**Social Networks**
**Sexual Reproduction**
**Cultural Processes**
**Combat**

**Sugarscape growback**
**Agent movement**
**Agent trade**
**Agent replacement**
**Agent mating**
**Agent combat**
**Pollution diffusion**
**Disease Transmission**

## Sugarscape Friendship Networks

**Cultural closeness defined by binary tags and measured by Hamming distance.**
**Agent who at some point are neighbors and who are culturally close are considered friends **
**Neighbors & their tags are randomly selected.  When they differ, the neighbor’s value is flipped **
**Each agent defines as friends the five agents encountered that are culturally closest**

## Emerging Polarity ModelLars-Erik Cederman

**Simulates the expansion & contraction of states**
**States expand through conquest, dominating captured provinces**
**Treats international polarity as an emergent feature**
**Allows for protracted & simultaneous conflicts**
**Model represents entity change through demise, amalgamation**
**Expanded version could represent migration & division**

## Situational Models of Social Complexity

**The ‘situation’ can serve as a focus for complexity reduction, or microtranslation**
**Recognizing societies, institutions and classes, indeed, all social orders, structures, entities and norms as (reifications of) accumulations from successive flows of microsituations**
**Even on the largest scales, social dynamics are ultimately produced and reproduced through the activities and interactions of situated agents**
**Microtranslation helps identify & eliminate reifications and, thereby, increases the plausibility of macrotheories **(Collins)
**Mapping social dynamics to their situational source controls the complexity of contextual analysis in a different way, by focusing on the moment of interactional emergence**

## Situation Theory 

**Situation theory develops a theory of meaning, semantic content or information flow**
**Situations are  first-class objects that are also rich**
**First-class objects can be used in any formula or expression**
**Rich objects cannot be fully defined or described**
**Situations define a formalism for reasoning about context**
**Situations are individuated, or intuited, by the agent**

## Ecological Realism

**Information exists prior to language**
**Unit of study is the organism situated in its environment**
**Meaning is located in the interaction of living things and their environment**
**Organisms anticipate events **
**Categorizing situations & being attuned to constraints helps organisms cope**

## Provisional Realism

**Barwise & Perry:**
**Meaning is ***discovered*** by animals, not invented or created by them**
**Peirce:**
**Truth is always relative to a context of interpretation**
**Winograd:  **
**The ultimate grounding is in the potential of continued coherent discourse**
**“****We take the attribution of reality as provisional”**

## Schemes of Individuation

**What is the grain of perception?**
**Spatial units, objects, strategies, and the actions &/or state of interacting agents**
**Some situations and objects are not individuated, but intuited **
**In this case, agents attune to constraints without explicit representation &/or reasoning**
**What is the grain of action primitives for agent types?**
**At this level, explicit control of the simplicity/verisimilitude tradeoff is introduced**

## The Ontology of Situation Theory

**Meaning as a relation between two situations**
**	**S  S’
**Infons**
- a discrete item of information, a semantic object, a fact
**	****<<*****P, x******1******,...,x******n******, i***** >>**
**where *****P***** is an *****n*****-place relation, **
***x******1******,...,x******n***** are objects for the argument roles of *****P***
***i***** represents polarity (truth value) of the fact**
**Propositions:****	**       *s * |=  
**Types:  situations, individuals**

## The Logic of Constraint 

**A relation of constraint**
**Two situation types and a set of background conditions**
**	****C = [ S ******** S’ ] / B**
**As an infon, a constraint is:**
**	****<<involves, S, S’, B, 1>>**
**Types of constraints**
**Nomic  (smoke means fire)**
**Necessary (kissing means touching)**
**Conventional (bell ringing means class over)**
**Agents attune to **(act in accordance with)** constraints**

## Social Indexicality

**Interpretive indexicality:**
- ... use people’s testimony as an indication of how accurately they can see, or as an indication of what was going on in front of them, depending on what else we hold constant.
**Information as indexical:**
- One if by land & two if by sea.
- The Cossack wink.
**Indexical social structure:**
- Property is generally ‘indexical’ ...  It mostly takes the form of the ‘here and now,’ inexpressible in general except by pointing to it in a concrete situation.  One ‘points’ implicitly with one’s body when one occupies, with a sense of appropriating it, one’s own home, one’s place of work, and so forth.  This is usually all that it takes, not a log of high-level processing, to keep the macrostructure intact.

## The Process of Formalization

*When Formality Works:  Authority and Abstraction in Law and Organizations*
- Formalisms define situations, however:
- Application, reconciliation and co-evolution involve situated cognition and action.
- Formalisms applicable in each domain give rise to distinct abstractions
- Application of formalisms requires officials, agencies and others to distinguish & reason about pertinent situations.

**DOMAINS**
- construction blueprints
- civil law and procedures
- commodification and liquidification of residential mortgage pools
- presumptive classification of aliens at border crossings
-  the stratification of scientific knowledge

## The Efficacy of FormalizationThree Qualities

- Formalities of the most various kinds can be described by the degree to which they are cognitively adequate to the situations they govern, are communicable to the people who must act in those situations, and are improvable, and in fact improving.
*Arthur Stinchcombe*
**Cognitive Adequacy**
**Accuracy**
**Cognitive economy**
**Sufficiency**
**Scope**
**CA & Types of informality**

**Communicability**
**Transmissibiity**
**Transparency**
**Durabulity**
**C. & Types of informality**
**Trajectory of Improvement**
**Correctability**
**Robustness**
**Algorithms & Trajectory**
**Informality & Trajectory**

## Rapid-Discovery Social Science?

**The nature of high-consensus, rapid-discovery science**
- High consensus is achieved about what is secure knowledge
- That consensus becomes a foundation for a subsequent stream of research insights
- Rapid-discovery science primarily arises from a continuing flow of innovation in available research technologies (Collins)
**PREMISE:  Agent modeling in practical domains, such as business and public policy, will not succeed without significant progress in the social sciences**
- Corollary:  this means the development of more effective and reliable social science theory
**SAMS may help build a foundation for rapid-discovery social science and, thus, for the requisite progress**