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

2. Samer Hassan MABS 2009 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

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

4. Samer Hassan 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)

5. Samer Hassan MABS 2009 5 Classical Logic of Simulation

6. Samer Hassan MABS 2009 6 Proposal for Data-Driven ABM

7. Samer Hassan 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

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

9. Samer Hassan 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

10. Samer Hassan 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

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

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

13. Samer Hassan 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

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

15. Samer Hassan 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

16. Samer Hassan 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

17. Samer Hassan 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

18. Samer Hassan MABS 2009 18 Fuzzy Friendship Evolution

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

20. Samer Hassan MABS 2009 20 Results

21. Samer Hassan 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

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

23. Samer Hassan 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

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

25. Samer Hassan 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