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Mentat: A Data-Driven Agent-Based Simulation of Social Values Evolution

This paper discusses the Mentat ABM, which studies the evolution of Spanish society from 1980-2000 using AI techniques. It explores the methodological approach, architecture, social dynamics, and results of the simulation.

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Mentat: A Data-Driven Agent-Based Simulation of Social Values Evolution

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  1. Mentat: A Data-Driven Agent-Based Simulation of Social Values Evolution Samer Hassan Luis Antunes Juan Pavón Universidad Complutense de Madrid University of Surrey Universidade de Lisboa

  2. Objectives of the Mentat ABM • Case Study of Data-Driven ABM approach • Study the evolution of the Spanish society in the period 1980-2000 • Framework for the application of different AI techniques MABS 2009

  3. Contents • Methodological approach • The Sociological Problem • Mentat: Architecture • Mentat: Social Dynamics • Mentat: Results • Future work MABS 2009

  4. Heading towards Data-Driven ABM • Learning from Microsimulation: • Minimizing random initialisation • Feeding the simulation with representative survey samples • Explicit rules can be problematic • Empirical probability equations to determine changes in the micro behaviour • Injecting more data into ABM • From other sources (e.g. qualitative) • In other stages (e.g. design) MABS 2009

  5. Classical Logic of Simulation MABS 2009

  6. Proposal for Data-Driven ABM MABS 2009

  7. Methodological aspects for Data-driven ABM • Microsimulation concepts • Initialisation with survey data • Empirically grounded probability equations • Design fed with data • Qualitative info, equations • Life cycle, micro-processes • Validation with different empirical data • ‘Deepening KISS’ for exploring the model space MABS 2009

  8. Contents • Methodological approach • The Sociological Problem • Mentat: Architecture • Mentat: Social Dynamics • Mentat: Results • Future work MABS 2009

  9. The Problem • Aim: simulate the process of change in social values • in a period • in a society • Plenty of factors involved • To which extent the demographic dynamics can explain the mental change? • Inertia of generational change MABS 2009

  10. The Problem • Input Data loaded: EVS-1980 • Quantitative periodical info • Representative sample of Spain • Allows Empirical Validation • Intra-generational: • Agent characteristics remain constant • Macro aggregation evolves MABS 2009

  11. Contents • Methodological approach • The Sociological Problem • Mentat: Architecture • Mentat: Social Dynamics • Mentat: Results • Future work MABS 2009

  12. Mentat: architecture • Agent: • Mental State attributes • Life cycle patterns • Demographic micro-evolution: • Couples • Reproduction • Inheritance MABS 2009

  13. Mentat: architecture • World: • 3000 agents • Grid 100x100 • Demographic model • 8 indep. parameters • Social Network: • Communication with Moore Neighbourhood • Friends network • Family network MABS 2009

  14. Contents • Methodological approach • The Sociological Problem • Mentat: Architecture • Mentat: Social Dynamics • Mentat: Results • Future work MABS 2009

  15. Understanding Friendship Dynamics • “Meeting” & “Mating”: strangers => acquaintances => friends => partner • “Meeting”: depends on opportunities alone • space & time • “Mating”: depends on both opportunities & attraction • Proximity principle: ‘the more similar two individuals are, the stronger their chances of becoming friends’ • Features channel individual preferences • Homogeneous friendship choices MABS 2009

  16. Mentat: Social Dynamics • Meeting • Agents randomly distributed in space • Mating • Similarity operator => Friendship • Matchmaking • Couple chosen among “candidates” • Quantity? The more friends, the more couples • Quality? Couples should be similar MABS 2009

  17. Be Fuzzy, my Friend • Similar, Friend: fuzzy concepts • Fuzzification • Improves accuracy of similarity • Improves realism of friendship • Improves quality of couples • But friendship develops through time: Dynamic evolution! • Hypothesis: Logistic function MABS 2009

  18. Fuzzy Friendship Evolution MABS 2009

  19. Contents • Methodological approach • The Sociological Problem • Mentat: Architecture • Mentat: Social Dynamics • Mentat: Results • Future work MABS 2009

  20. Results MABS 2009

  21. Results • It may arise new sociological assumptions: In the prediction of social trends in Spain, Demographic Dynamics probably have, attending to the results, a key importance MABS 2009

  22. Contents • Methodological approach • The Sociological Problem • Mentat: Architecture • Mentat: Social Dynamics • Mentat: Results • Future work MABS 2009

  23. Future Work • Mentat as a stage-based modular framework • Enabling/Disabling modules for exploration • ceteris paribus • Explore the application of other AI techniques: • NLP: biography of a representative individual • Complementary output in natural language • Events tracing -> XML -> NL • DM: clustering over the input and output • Helpful in design and validation MABS 2009

  24. Thanks for your attention! Samer Hassan samer@fdi.ucm.es Universidad Complutense de Madrid University of Surrey Universidade de Lisboa MABS 2009

  25. Contents License • This presentation is licensed under a Creative Commons Attribution 3.0 http://creativecommons.org/licenses/by/3.0/ • You are free to copy, modify and distribute it as long as the original work and author are cited MABS 2009

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