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.

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

Moral Values EvolutionSamer Hassan

Luis Antunes

Millán Arroyo

MABS 2008

Acknowledgments. This work has been developed with support of the project TIN2005-08501-C03-01, funded by the Spanish Council for Science and Technology.

Samer Hassan, UCM MABS 2008 2

Contents Objective

Case Study: Mentat

Deepening: A Methodology

Deepening Demographics

Results & Conclusions


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


Contents Objective

Case Study: Mentat





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


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


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


Mentat in action Thousands of agents in continuous interaction Graphics & Stats


Contents Objective

Case Study: Mentat





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


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


Contents Objective

Case Study: Mentat





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


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


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


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)


Contents Objective

Case Study: Mentat





Results


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


Thanks for your attention!

Samer [email protected]. Ingenieria del Software e Inteligencia ArtificialUniversidad Complutense de Madrid

mailto:[email protected]


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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.

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