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

Samer Hassan, UCM MABS Contents Objective Case Study: Mentat Deepening: A Methodology Deepening Demographics Results & Conclusions

Samer Hassan, UCM MABS 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

Samer Hassan, UCM MABS Contents Objective Case Study: Mentat Deepening: A Methodology Deepening Demographics Results & Conclusions

Samer Hassan, UCM MABS 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

Samer Hassan, UCM MABS 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

Samer Hassan, UCM MABS 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

Samer Hassan, UCM MABS Mentat in action Thousands of agents in continuous interaction Graphics & Stats

Samer Hassan, UCM MABS Contents Objective Case Study: Mentat Deepening: A Methodology Deepening Demographics Results & Conclusions

Samer Hassan, UCM MABS 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

Samer Hassan, UCM MABS 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

Samer Hassan, UCM MABS Contents Objective Case Study: Mentat Deepening: A Methodology Deepening Demographics Results & Conclusions

Samer Hassan, UCM MABS 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

Samer Hassan, UCM MABS 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

Samer Hassan, UCM MABS 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

Samer Hassan, UCM MABS 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)

Samer Hassan, UCM MABS Contents Objective Case Study: Mentat Deepening: A Methodology Deepening Demographics Results & Conclusions

Samer Hassan, UCM MABS Results

Samer Hassan, UCM MABS 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

Samer Hassan, UCM MABS Thanks for your attention! Samer Hassan Dep. Ingenieria del Software e Inteligencia Artificial Universidad Complutense de Madrid

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