Neuro -Psychological Social Theorizing and Simulation with Computational Multi-Agent System ETHOS

1. Neuro-Psychological Social Theorizing and Simulation with Computational Multi-Agent System ETHOS Luís Moniz Pereira * Centro de Inteligência Artificial – CENTRIA Universidade Nova de Lisboa Istituto di Studi Avanzati U. Bologna, Giugno 14, 2004 * jointwork with ex-Ph.D. student Jorge Simão

2. Talk Outline • ETHOSSimulation Framework Design Goals • Current Agent Based Model Simulation Frameworks • ETHOSSimulation Framework Overview • Human Mate Choice: case study I • The Cultural Evolution of Preferences: case study II • Conclusions and Future Work

3. 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

4. ETHOS Simulation Framework:design goals (2) General Computational Requirements of Frameworks: • Expressiveness and Flexibility • Extensibility and Modifiability • Transparency • Performance • Scalability • Portability • Ease of Use

5. 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

6. 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

7. ETHOS Simulation Framework Overview (2)

8. 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

9. ETHOS Simulation Framework Overview (4) Ethos’s Class Hierarchy:

10. 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

11. ETHOS Simulation Framework Overview (6) ETHOS’s GUI look-and-feel

12. 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 ?

13. 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.

14. 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

15. 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.

16. 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)

17. 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%

18. Simulation results (2) Distribution of age at mating (marriage) time -- right-skewed bell-curve --

19. 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

20. The Cultural Evolution of Preferences:case study II What do miniskirts, afro haircuts, and body tattoos have 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

21. An agent-based model of fashion: emergence (1) Agent attributes: ai = < qi , ti , v0i , v1i >. Model pseudo-code: repeat (T) { for all agent { update trait values ; switch to most preferred trait ; } }

22. An agent-based model of fashion:emergence (2) Trait value update rules: v1i (t) = v1i (t-1) ·  + 1/N ·  qj · (1- ) v0i (t) = v0i (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 small E assortment 4 r  0.75  1 - learning rate 0.2 fast learning  standard deviation 2 D delay 4 cognitive or material aj: ajMi∧tj=1 aj: ajMi∧tj=0

23. Simulation Results (1) Bit map of trait usage across time (D = 4): Frequency of trait usage across time (D = 4):

24. 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

25. Simulation Results (3): sensitivity analyses Bit map of trait usage across time (D = 10): Bit map of trait usage across time (D = 0):

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

27. 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