1 / 33

Data-Driven Agent-Based Social Simulation of Moral Values Evolution

Data-Driven Agent-Based Social Simulation of Moral Values Evolution. Samer Hassan Universidad Complutense de Madrid University of Surrey. Contents. The Problem ABM Mentat: Design ABM Mentat: Results AI: Fuzzy Logic AI: Natural Language Processing AI: Data Mining. Objective.

juttal
Download Presentation

Data-Driven Agent-Based Social Simulation of Moral Values Evolution

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data-Driven Agent-Based Social Simulation of Moral Values Evolution Samer Hassan Universidad Complutense de Madrid University of Surrey

  2. Contents • The Problem • ABM Mentat: Design • ABM Mentat: Results • AI: Fuzzy Logic • AI: Natural Language Processing • AI: Data Mining SSASA 2008

  3. Objective • Study the evolution of Spanish society in the period 1980-2000 • Data-Driven Agent-Based Modelling • Applying several Artificial Intelligence techniques SSASA 2008

  4. The Problem • Aim: simulate the process of change in moral values • in a period • in a society • Plenty of factors involved • Nowadays, centred in the inertia of generational change: • To which extent the demographic dynamics explain the mentality change? SSASA 2008

  5. The Problem • Input Data loaded: EVS-1980 • Quantitative periodical info • Representative sample of Spain • Allows Validation • Intra-generational: • Agent characteristics remain constant • Macro aggregation evolves SSASA 2008

  6. Contents • The Problem • ABM Mentat: Design • ABM Mentat: Results • AI: Fuzzy Logic • AI: Natural Language Processing • AI: Data Mining SSASA 2008

  7. Design of Mentat • Agent: • EVS  Agent MS attributes • Life cycle patterns • Demographic micro-evolution: • Couples • Reproduction • Inheritance • World: • 3000 agents • Grid 100x100 • Demographic model • Network: • Communication with Moore Neighbourhood • Friends network • Family network SSASA 2008

  8. Friendship Network SSASA 2008

  9. Friendship Network SSASA 2008

  10. Friendship Network SSASA 2008

  11. Friendship Network SSASA 2008

  12. Friendship Network SSASA 2008

  13. Friendship Network SSASA 2008

  14. Friendship Network SSASA 2008

  15. Friendship Network SSASA 2008

  16. Methodological aspects • Data-driven ABM • Microsimulation concepts • Design with qualitative info • Life cycle, micro-processes • Introduction of empirical equations • Life expectancy, birth rate, different probabilities • Initialisation with survey data • Validation with different empirical data SSASA 2008

  17. Mentat in action SSASA 2008

  18. Contents • The Problem • ABM Mentat: Design • ABM Mentat: Results • AI: Fuzzy Logic • AI: Natural Language Processing • AI: Data Mining SSASA 2008

  19. Results SSASA 2008

  20. Results • It may arise new sociological knowledge: Demographic Dynamics are a key factor for the prediction of social trends in Spanish society SSASA 2008

  21. Contents • The Problem • ABM Mentat: Design • ABM Mentat: Results • AI: Fuzzy Logic • AI: Natural Language Processing • AI: Data Mining SSASA 2008

  22. Introduction of AI: Fuzzy Logic • Why Fuzzy Logic? • Social sciences are characterized by uncertain and vague knowledge • Different concept than probability Age Young Adult Old 10 1 0 0 20 0.8 0.8 0.1 30 0.5 1 0.2 40 0.2 1 0.4 50 0.1 1 0.6 SSASA 2008

  23. Fuzzification • Attributes • Similarity • Friendship & its evolution • Couples SSASA 2008

  24. Contents • The Problem • ABM Mentat: Design • ABM Mentat: Results • AI: Fuzzy Logic • AI: Natural Language Processing • AI: Data Mining SSASA 2008

  25. Introduction of AI: NLP • Fuzzy logic helps for ABM qualitative input • NLP helps for ABM qualitative output • Experimenting with life-events generation: • Output in natural language: life-story of a representative individual (Ex: hyper-inflation) • Applications: • NL format makes direct comparison with real stories possible • Information very simple for any individual to understand • Complementing explanations of quantitative research SSASA 2008

  26. Quantitative & Qualitative Output Generation SSASA 2008

  27. An example: part of the XML output <LogId="i49"> <Description /> <AttributeId="name" Value="rosa" /> <AttributeId="last_name" Value="pérez" /> <AttributeId="sex" Value="female" /> <AttributeId="ideology" Value="left" /> <AttributeId="education" Value="high" /> ... <Events> <EventId="e1" Time="1955" Action="birth" Param="" /> <EventId="e2" Time="1960" Action="friend" Param="i344" /> <EventId="e3" Time="1960" Action="friend" Param="i439" /> <EventId="e4" Time="1961" Action="friend" Param="i151" /> <EventId="e5" Time="1962" Action="horrible" Param="childhood" /> <EventId="e6" Time="1963" Action="best friend" Param="i151" /> <EventId="e7" Time="1964" Action="believe" Param="god" /> <EventId="e8" Time="1964" Action="every week go" Param="church" /> ... <EventId="e16" Time="1968" Action="problems" Param="drugs" /> <EventId="e17" Time="1971" Action="grow" Param="adult" /> <EventId="e18" Time="1971" Action="friend" Param="i98" /> <EventId="e19" Time="1972" Action="involved" Param="labourunion" /> <EventId="e20" Time="1972" Action="friend" Param="i156" /> <EventId="e21" Time="1973" Action="get" Param="arrested" /> <EventId="e22" Time="1973" Action="learn" Param="play guitar" /> <EventId="e23" Time="1975" Action="became" Param="hippy" /> ... <EventId="e36" Time="1985" Action="divorce" Param="i439" /> <EventId="e37" Time="1987" Action="couple" Param="i102" /> <EventId="e38" Time="1987" Action="live together" Param="i102" /> <EventId="e39" Time="1987" Action="have" Param="abortion" /> ... </Log> <LogId="i50"> <Description /> <AttributeId="name" Value=“francisco" /> ... SSASA 2008

  28. An example: part of the life-story generated • Rosa Pérez was born in 1955, and she met Luis Martínez, and she met Miguel López. She suffered a horrible childhood, and she had a very good friend: María Valdés, and she believed in God, and she used to go to church every week. . . . • When she was a teenager, (...) she had problems with drugs, and she became an adult, and she met Marci Boyle, and while she was involved in a labour union, she met Carla González and she got arrested. She learned how to play the guitar, and so she became a hippy, getting involved in a NGO. . . . • She met Sara Hernández, and she stopped going to church, and she met Marcos Torres, and she fell in love, desperately, with Marcos Torres, but in the end she went out with Miguel López, and she co-habitated with Miguel López, and she had a child: Melvin López. . . . • She met Sergio Ruiz, and she separated from Miguel López, and she went out with Sergio Ruiz, and she co-habitated with Sergio Ruiz. She had a abortion, and so she had a depression, and she had a crisis of values. She was unfaithful to Sergio Ruiz with another man. . . . • Nowadays she is an atheist. SSASA 2008

  29. Contents • The Problem • ABM Mentat: Design • ABM Mentat: Results • AI: Fuzzy Logic • AI: Natural Language Processing • AI: Data Mining SSASA 2008

  30. Introduction of AI: Data Mining • Data Mining is the process of extracting patterns and relevant information from large amounts of data • Design: • Allows simplification, locates redundant attributes • Pre-processing of empirical data (surveys): • Clustering: selection of qualitative “ideal types” • Post-processing of simulation output: • Clustering: • Shows non-visible patterns • Comparison of patterns • Different life-stories for each pattern • Classification: evolution of “ideal types” SSASA 2008

  31. Limitations & Future Work • Enough demography! • Overcome methodological limitation: implementing diffusion of moral values • Quest for a proper cognitive model for this task • ...or forget about it • definitely not BDI • Improve other aspects: • ABM design (Ex: friendship ties may weaken) • Fuzzy inference • Quality of biographies SSASA 2008

  32. Thanks for your attention! Samer Hassan samer@fdi.ucm.es SSASA 2008

  33. 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 SSASA 2008

More Related