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Deepening the Demographic Mechanisms in a Data-Driven Social Simulation of Moral Values Evolution

Deepening the Demographic Mechanisms in a Data-Driven Social Simulation of Moral Values Evolution. MABS 2008. Samer Hassan Luis Antunes Mill á n Arroyo.

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Deepening the Demographic Mechanisms in a Data-Driven Social Simulation of Moral Values Evolution

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  1. Deepening the Demographic Mechanisms in a Data-Driven Social Simulation of Moral Values Evolution MABS 2008 Samer Hassan Luis Antunes Millán Arroyo Acknowledgments. This work has been developed with support of the project TIN2005-08501-C03-01, funded by the Spanish Council for Science and Technology.

  2. Contents • Objective • Case Study: Mentat • Deepening: A Methodology • Deepening Demographics • Results & Conclusions MABS 2008

  3. Objective • Compromise between simplification and expressiveness • Gradually increase complexity of a KISS ABM • Case Study of Data-driven ABM with difficulties in handling demography • Deepening significantly improves output MABS 2008

  4. Contents • Objective • Case Study: Mentat • Deepening: A Methodology • Deepening Demographics • Results & Conclusions MABS 2008

  5. Case Study • Objective: simulate the process of change in moral values • in a period • in a society • Plenty of factors involved • To which extent the demographic dynamics explain the mental change? • Explore the inertia of generational change MABS 2008

  6. Case Study • Input Data loaded: EVS-1980 • Quantitative periodical info • Representative sample of Spain • Allows Validation • Intra-generational: • Agent characteristics remain constant • Macro aggregation evolve MABS 2008

  7. Design of Mentat • Agent: • EVS  Agent MS attributes • Life cycle patterns • Demographic micro-evolution: couples, reproduction, inheritance • World: • Grid 100x100 • Demographic model • Network: • Communication with Moore Neighbourhood • Friends network • Family network MABS 2008

  8. Mentat in action • Thousands of agents in continuous interaction • Graphics & Stats MABS 2008

  9. Contents • Objective • Case Study: Mentat • Deepening: A Methodology • Deepening Demographics • Results & Conclusions MABS 2008

  10. Deepening as a methodology • Only over a KISS ABM already designed • Gradually increase complexity, step by step: • Isolate every candidate section • Re-implement each one increasing complexity • Analyze output • Compare it to: • The previous outputs • The parallel outputs • The real data MABS 2008

  11. Deepening as a methodology • Example of sequence of deepening a single concept: • “C” constant • ->variable • ->random distribution • ->empirically validated distribution • ->dedicated mechanism for calculating “C” • ->adaptive mechanism for calculating “C” • ->substitute “C” altogether by a mechanism MABS 2008

  12. Contents • Objective • Case Study: Mentat • Deepening: A Methodology • Deepening Demographics • Results & Conclusions MABS 2008

  13. Demographics: Missing Children • Problem: no initial children • Cause: methodological. In surveys, no underage (0->17 years old) • Effects: • 23% missing • In 20 years they would reproduce • Population drops (generation missing) • Solution: insertion of 700 children based on EVS-1980 MABS 2008

  14. Demographics: Initial Marriages • Problem: no births in first years • Cause: design. Agents begin isolated • They are close but with no links • Effects: • First years: building robust linked network • Afterwards: births & expected macro output MABS 2008

  15. Demographics: Initial Marriages • Solution: modification of design • Phase A: initialization from EVS • Phase B: “warming-up” simulation • years counter frozen: no ageing • agent steps: • Communication • Building friendship and couples • Phase C: usual simulation MABS 2008

  16. Demographics: Population Dynamics • Problem: inaccuracy • Cause: over-simplified design • All distributions Normal • All distributions static • Solution: equations based on empirical data • Birth Rate • Life Expectancy (men/women) • Probability to have children (depend on age) • Probability of being married (depend on age) MABS 2008

  17. Contents • Objective • Case Study: Mentat • Deepening: A Methodology • Deepening Demographics • Results & Conclusions MABS 2008

  18. Results MABS 2008

  19. Conclusions • Deepening Mentat: success • Still simple but more expressive • It may arise new sociological assumption: In the prediction of social trends, Demographic Dynamics has, as we can support by the results, a key importance • Future work would involve: • Study other contexts to support assumption • Increase formalization of the deepening process MABS 2008

  20. Thanks for your attention! Samer Hassan samer@fdi.ucm.es Dep. Ingenieria del Software e Inteligencia Artificial Universidad Complutense de Madrid MABS 2008

  21. 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 2008

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