Agent Theory : A Missing Requirement of Generative Social Science

1. Agent Theory: A Missing Requirement of GenerativeSocial Science Rosaria Conte Laboratory of Agent Based Social Simulation Institute of Cognitive Science and Technology CNR, Rome Agent 2007 15-17 November

2. Claim The generative paradigm, aimed at producing the phenomena that are to be explained, is a formidable opportunity for the development of behavioral and social science provided it is based on firmer grounds, i.e. an adequate agent base.

3. The generative paradigm • Agent-based social simulation (ABSS) is a generative approach • “Generation is necessary for explanation” (Epstein, 2005). Formally: forall x(not Gx  not Ex) “if you do not grow x, you cannot explain it.” (ibi.) • But it is not sufficient: many ways, or local rules, may lead to one Macro-Regularity. lr1 lr2 MR lr3 lrn

4. But what is generating? • Many notions. No attempt to unify them • Perhaps, two main compatible lines • Biologically inspired: a generative cause “transmits its nature” to its effects (Heider, 1958). • Computationally inspired: competence-performance, executing rules (Chomsky, 1972). Emphasis on the generating machine, which can be reconstructed from performance. • Epstein: simplified version of the latter: • ”…situate an initial population of autonomous heterogeneous agents (…) in a relevant special environment; • allow them to interact according to simple local rules, • thereby generate - or 'grow' - the macroscopic regularity from the bottom up"(Epstein, 1999, 41; italics are mine). • Emphasis on the generated effect.

5. Problems • 1st order of problems: • What are local rules? Mere trigger? • Which local rules? How avoid ad hoc rules? • 2nd order of problems: • What about dynamic local rules? • What about downward causation?

6. A best-known example: The segregation model • Schelling’s model (1971) = visual metaphor for social, even ethnic segregation • He let agents move around according to various rules. • Happiness rule: stick to current location if happy with own neighbors and move if unhappy Emergence of Clustering after Unhappy Agents Have Been Allowed to Relocate by Random Walk (Repr. From Gilbert, 2002)

7. What is it good for? • Schelling’s model is said to allow us “to understand the decision rules of a small number of individual actors” (quoted from: http://web.mit.edu/rajsingh/www/lab/alife/schelling.html ) • Too optimistic! • Shows the emergent effect (MR) of individual decisions, • Tells what is not needed for MR to obtain! • But tells us little about individual decisions: • Preference for own-type neighbors • Preference for homogeneous neighbors • Attitude to élite-conformity • Different internally consistent migratory attitudes • Different purchasing capacity, etc. • Different paths to the same MR…

8. Why bother with local decisions? • As long as a MR emerges, why bother with how it is obtained? • Otherwise, no generative explanation • If effects share the nature of their generative causes, different decisions lead to different although related effects (see also Sawyer’s 2003): individual decisions do matter ! • If process from local rules to MR is complex and non-linear (loops), a given MR results from causes that it contributes to modify: individual decisions and how they can be influenced matter (see later).

9. Still on segregation. Impact on crime • In US, 95% of violent crime is among group members. Property crime is not. • “Violent crime is better explained by urban flight than by inequality (Kelly, 2000). How generate this phenomenon? • Obviously simple ad hoc rules: • Rob outgroups • Kill ingroups explain nothing! • How prevent ad hoc rules? • Schelling helps: • If neighbours are homogeneous, • People will kill in-groups more often then out-groups. • But why rob out-groups?

10. Motivational hypotheses • How explain difference between crimes against person and crimes against property? • Unlike property crime, violence against person requires no social differences: riots blow up among neighbours • A less trivial hypothesis: violence derives from competition over (non-material) goods in poor homogenous neighbours. • A tricky hypothesis: violence = consequence of social desegregation, loss of self-esteem and self-derogatory attitudes, and therefore is directed against same-type agents. • Hypotheses about motivations to violent crime are needed to build up a non-trivial simulation

11. Three criteria for local rules • Simple include ad hoc rules • Plausible (see Esptein, 2005): too vague • Theory-driven: • Independent of effect • Possibly supported by “independent sources” (Gruene-Yanoff, 2006) • Based upon general theory of agents.

12. Problems • 1st order of problems: • What are local rules? Mere trigger? • Which local rules? How avoid ad hoc rules? • 2nd order of problems: • What about dynamic local rules? • What about downward causation?

13. The role of learning • In current ABSS models, dynamics of local rules = learning. • Consider Santa Fe model of financial (one-stock) market (Arthur 2004) • Learn by creating new hypotheses and discarding poorly performing ones. • Two regimes result • If hypotheses change slowly, convergence on rational expectations • If change fast, chaotic dynamics and no convergence • The faster the learning, the less likely the convergence. • Learning is overestimated: agents converge also on wrong expectations!

14. Social factors of agent change • Social Influence • Downward causation: emergent effect retroacts on local entities. • Example drawn from Gilbert 2002: • poorer reds forced to stay in their poor red districts. • The richer greens move where they want, but they like to be around other greens in green areas. • There are a very few poor greens who are surrounded by reds and who cannot move to more desirable green areas. Model with Downward Causation [Background grey shade marks crime rate (black: high crime rate, low property values; white: low crime rate, high property values

15. Downward causation Different types • Objective influence • see example above • Cognitive influence • Second order emergence (Gilbert, 2002): agents perceive emergent effect and modify their behavior accordingly (the tagged behavior) • Immergence (Castelfranchi, 1998; Conte et al., 2007a): local rules (reasoning, goal pursuit, etc.) are modified; • ex. norm-conformity.

16. Norm-conformity Requires a complex architecture* (cf. Conte et al., 2007b), • Norm-recognition: otherwise how tell norms from coercion • Norm-adoption: otherwise how decide to comply/violate the norm? • Norm-based decision: otherwise how solve norm-conflicts? • Norm-based planning: otherwise how execute intelligently the norm? * (Developed within EMIL, a EU-funded project on norm-innovation)

17. For Epstein thoughtless conformity: “When I got in my car to drive to work, it never crossed my mind to drive on the left. And when I joined my colleagues at launch, I did not consider eating my salad barehanded; without a thought, I used a fork” (Epstein, 2007, 229) As Bargh would argue, this is cognitive nonsense: “running where we don’t know how to walk yet!” (Bargh, 2006) What about thoughtless conformity?

18. The mind: neither homunculus… • In contemporary cognitive science no either-or position: • Mind is not totally autonomous… • Locus of control is not always in a “little person in the head” (Neisser, 1967)

19. … nor black box • is not fully conditioned • Although sometimes…

20. A modular device • Derived from the modular view of the mind proposed by evolutionary psychologists (the “Swiss Army knife”, cf. MacDonald, 2006) • Consciousness covers only a subset of mental processes. • Hence, thought is both • Conscious = controlled • Unconscious = automatic The question is not which one explains action, but to what extent.

21. Deliberate Conscious Intentional Effortful Controlled (Bargh, 2004) In some tasks, awareness and control are counterproductive. Automatic: Reduction of effort Removal of awareness from mental process including goal-pursuit Automated goal pursuit rather than automated response Vigorous goal attainment Persistence in face of obstacles Resumption after disruption (Lewin, 1921, etc.). Deliberate Vs automatic mental processes

22. What about flexibility? • In Bargh and Gollwitzer (2001), automated goal pursuit persisted despite obstacle. • But how adapt automated behaviors to a changing environment? it is possible to disactivate automated behavior when needed? • Flexible persistence: what about automatic conflict resolution?

23. Norm-conformity • Overtime, with frequent consistent pairing between external events and internal behaviors (recognition, adotion, etc.) • Norms may be automatized • not as “static behavioral responses”, • but as “automated strategy.” (Bargh and Barndollar, 1996) • Which are disactivated when demanded by ciscustances (conflicting norm) • Adapted to unexpected circumstances • Integrated with compatible strategies (opportuinistic planning, contrary to duty normative actions) • But before, they must be acquired (immergence)

24. Hence • We need a theory of • Norm immergence • Internalization (from norms to ordinary goals, motivations, dispositions) • Automatization (from conscious, deliberate norm processing to unconscious norm-pursuit) • Interplay between automatic control and deliberate control. • Which requires agent theory!

25. Agent Theory MR ABSS Dynamic local rules: to sum up • Generation • Bottom up • Top down • Learning • Social influence • Downward causation

26. Conclusions • Generative explanation needs • Further conceptual analysis • To be based on A theory-driven and dynamic model of the agent (let us speak of agents, rather than local rules!) • And integrate A theory of top-down process of generation, • No explanation without generation but • No theory of generative process without a theory of generative machines!

27. "What can we learn from simulating poorly understood systems?" (Simon, 1969)