Agent Theory: A Missing Requirement of Generative Social Science

Rosaria Conte

Laboratory of Agent Based Social Simulation

Institute of Cognitive Science and Technology

CNR, Rome

Agent 2007

15-17 November

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.

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.

MR

lr1

lr2

lr3

lrn

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.

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?

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)

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…

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).

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?

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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

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.

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?

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!

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

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.

Norm-conformityRequires 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)

What about thoughtless conformity?

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)

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As Bargh would argue, this is cognitive nonsense: “running where we don’t know how to walk yet!” (Bargh, 2006)

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)

… nor black box is not fully

conditioned

Although sometimes…

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 = automaticThe question is not which one

explains action, but to what extent.

Deliberate Vs automatic mental processesDeliberate 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.).

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?

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)

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!

Dynamic local rules: to sum up

Generation Bottom up Top down

• Learning• Social influence • Downward

causation

Agent Theory

ABSS MR

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!

"What can we learn from simulating poorly understood systems?" (Simon,

1969)