Neuro-Psychological Social Theorizing and Simulation with Computational Multi-Agent System ETHOS Luís Moniz Pereira * Centro de Inteligência Artificial.

Neuro-PsychologicalSocial Theorizing and

Simulationwith ComputationalMulti-Agent System

ETHOS

Luís Moniz Pereira *

Centro de Inteligência Artificial – CENTRIA

Universidade Nova de Lisboa

Istituto di Studi Avanzati U. Bologna, Giugno 14, 2004

* joint work with ex-Ph.D. student Jorge Simão

Talk Outline

ETHOS Simulation Framework Design Goals

Current Agent Based Model Simulation Frameworks

ETHOS Simulation Framework Overview

Human Mate Choice: case study I

The Cultural Evolution of Preferences: case study II

Conclusions and Future Work

ETHOS Simulation Framework:design goals (1)

ETHOS is an Object-Oriented Simulation Framework

Implemented in Java Download from: http://centria.di.fct.unl.pt/~jsimao/ethos

Gives Computational Support for Social Theory Building to:

Reify in software useful theoretical constructs

(shared and/or plausible)

Experiment with variations of theoretical constructs

Re-use theoretical constructs

Easily (re-)implement and extend a large array of models

Easily explore the model and theory spaces of possibilities

ETHOS Simulation Framework:design goals (2)

General Computational Requirements of Frameworks:

Expressiveness and Flexibility

Extensibility and Modifiability

Transparency

Performance

Scalability

Portability

Ease of Use

Current ABM Simulation Frameworks• Swarm, RePast, Ascape

+ Good Support for General Computational Service- Lack Specific Support for Social Theory Building

• PS-I+ Support for Social Theory- Targeted only to a Specific Set of Mid-RangeTheories:

constructivist identity theories• Evo

+ Support for Evolutionary Discovery of Behaviour Strategies- Limited Plausible set of Mechanisms (Evolutionary Programming)

• Starlog, AgentSheets+ Easy to Use- Mostly Limited to “Toy” Models

• Sugarscape, Consumat, . . . (and other highly parameterized models)+ Interesting Case Studies- Not a Generic Simulation Framework

ETHOS Simulation Framework Overview (1)

Physical Environment Structure:

– Space is the unit of spatial layout; provides

topological arrangement of Site

– Site have any number of Body

– Body represents a physical entity:

(Human) Agent, Resource, Organization

– World as aggregation of Space

ETHOS Simulation Framework Overview (2)

ETHOS Simulation Framework Overview (3)

(Human) Agent Structure:

Agent = Genome + Visible Attributes + Social Networks + Control

Genome is a set of inherited traits Attr is a visible agent attribute (e.g. sex, quality) Tie is a connection between agents in a

SocialNet Selector objects used as reusable selection criteria

mechanism: SocialNet, . . . Control is the behaviour control mechanism, on the

basis of the Task Env

ETHOS Simulation Framework Overview (4)

Ethos’s

Class

Hierarchy:

ETHOS Simulation Framework Overview (5)

Event Scheduling and Population Structure:

Population are aggregations of Body; coordinates their activities

Population can contain other Population; composite structure

Population also place-holder for operations at aggregate level

Each Space contains a top level Population to add other

Population

Population set associated with a Space

Selectable Scheduling Policy:

• single or multi-phase

• syncronous or asyncronous

• fixed or variable time, per agent

ETHOS Simulation Framework Overview (6)

ETHOS’s GUI look-and-feel

Human Mate Choice: case study I

Emergent population-level patterns in human mating systems:

Assortative Mating• Couples highly correlated in attractiveness (0.4 - 0.6)• (But) Individuals prefer more attractive partners• Matching hypothesis?

Distribution of age at mating time• Right-skewed bell-curve (robust cross-culturally)• Explanation ?

Previous Models of Mate Choice

• S. Kalick and T. Hamilton

”The matching hypothesis re-examined”,

in Journal of Personality and Social Psychology, 4:(51), 1986.

• P. Todd and G. Miller

”From pride and prejudice to persuasion: satisficing in mate search”,

in Simple Heuristics that Make Us Smart, Oxford UP, 1999.

• Rufus Johnstone

”The tactics of mutual mate choice and competitive search”,

in Behavioral Ecology and Sociobiology, 1:(40), 1997.

Courtship Based Model: social ecology (1)

Parameter Description Value(s)

P population size/2 50

L reproductive lifetime 200

µ, 2 quality distribution 10, 4

Y meeting rate 0.1 – 1.0

K courtship time 5 - 50

Courtship Based Model: social ecology (2)

• Fixed population size (2 x P) and sex ratio (50%)

• (Quasi) normal distribution of qualities:

mean µ and variance 2 (0 < Qmin ≤ q ≤ Qmax).

• Meeting rate Y (0.1 – 1.0). Discrete time steps.

• List of alternatives: one has ”special status” -- the date.

• (Age depended) Courtship time K before mating; current time ct .

• Limited reproductive life time L (> K) = 200.

Individual mate choice strategies

Fitness function: F(qm, t) = qm · (L - t)/L

Decision rules:

• Partner switching (risk insensitive): F(qa, t + Ki) > F(qd, t + Ki - ct)

• Partner acceptance/aspiration level setting:

q*i new

= q*i old · (1 - ) + · qj ·

• Aspiration level dropping with time: tmax = · (L – t)/L · (1 – qb / q*)

• Age dependent minimum courtship time: Ki = K · (1 – ti / L)

Simulation results (1)

Robust Empirically Validated Results:

• Mean correlation of qualities in mated pairs: 0.6 - 0.8

• Mean number of alternatives seen before settling with the last date: 2 - 10

• Percentage of individuals in the population that are able to mate: ≥ 90%

Simulation results (2)

Distribution of age at mating (marriage) time-- right-skewed bell-curve --

Conclusions from Model

More realistic results than previous models

Model assumptions more psychologically plausible and more relevant to humans

Future work:

• Other mating systems: Serial Monogamy, and Divorce

• More complex preferences: structure and dynamics

The Cultural Evolution of Preferences:case study II

What do miniskirts, afro haircuts, and body tattooshave in common?

• They are all forms of body accessories that have had a characteristic

fashion-like career.• They emerge out of obscurity and spread through a population very fast.• Shortly after they have reached their maximum popularity:

• vanish again from the cultural landscape• sometimes surge again long after

Current explanations:– Simmel Effect – Information cascades– Externalities – Decay of value

Our proposal: Individual Conditioning drives collective behaviour

An agent-based model of fashion: emergence (1)

Agent attributes: ai = < qi , ti , v0i , v1

i >.

Model pseudo-code:

repeat (T) {for all agent {

update trait values ;switch to most preferred trait

;}

}

An agent-based model of fashion:emergence (2)

Trait value update rules:

v1i (t) = v1

i (t-1) · + 1/N · qj · (1- )

v0i (t) = v0

i (t-1) · + 1/N · qj · (1- )

Parameter settings:

Parameter Description Value(s) Note P population size 50 small sample

N number of role models 5 smallE assortment 4 r 0.75 1 - learning rate 0.2 fast learning standard deviation 2D delay 4 cognitive or material

aj: ajMi t∧ j=1

aj: ajMi t∧ j=0

Simulation Results (1)

Bit map of trait usage

across time (D = 4):

Frequency of

trait usage across

time (D = 4):

Simulation Results (2): deterministic model

Bit map of trait usage

across time (D = 4)

with deterministic

selection of model:

Notes:

• Small deterministic neighborhood changes behaviour of model

• Propagation of trait usage / avoidance is more regular

• General caveat: spatial analogies of social strata can bias results

Simulation Results (3): sensitivity analyses

Bit map of trait usage

across time (D = 10):

Bit map of trait usage

across time (D = 0):

Conclusions from Model

Fashion like collective behaviour can emerge from individual conditioning

Model is very sensitive to delay parameter D

Complex networks of traits may have morecomplex dynamics

Models with multi-valued trait may also have more complex dynamics

Conclusions and Future Work

ABM Software support for Social Theory Building

• Is Feasible: Identifies Key Foundational Abstractions

• Is Useful: Simplifies Theory Building, Comparison, and Testing

• Is Desirable: Contributes to the Unification of the Social Sciences

Further Developments in ETHOS

• (Re-) Implement Additional Models

• Refine and Add Abstractions (if and as needed)

• Make Software Publicly Available