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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 (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 (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
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%
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