Mentat: A Data-Driven Agent-Based Simulation of Social Values Evolution Samer Hassan Luis Antunes...

Mentat: A Data-Driven Agent-Based Simulation of Social Values Evolution

Samer Hassan

Luis Antunes

Juan Pavón

Universidad Complutense de Madrid

University of Surrey

Universidade de Lisboa

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

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Contents

Methodological approach

The Sociological Problem

Mentat: Architecture

Mentat: Social Dynamics

Mentat: Results

Future work

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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)

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Classical Logic of Simulation

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Proposal for Data-Driven ABM

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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

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Contents

Methodological approach

The Sociological Problem

Mentat: Architecture

Mentat: Social Dynamics

Mentat: Results

Future work

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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

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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

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Contents

Methodological approach

The Sociological Problem

Mentat: Architecture

Mentat: Social Dynamics

Mentat: Results

Future work

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Mentat: architecture

Agent:

Mental State attributes

Life cycle patterns

Demographic micro-evolution: • Couples• Reproduction• Inheritance

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Mentat: architecture

World: 3000 agents

Grid 100x100

Demographic model

8 indep. parameters

Social Network: Communication with

Moore Neighbourhood

Friends network

Family network

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Contents

Methodological approach

The Sociological Problem

Mentat: Architecture

Mentat: Social Dynamics

Mentat: Results

Future work

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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

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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

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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

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Fuzzy Friendship Evolution

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Samer Hassan MABS 2009 19

Contents

Methodological approach

The Sociological Problem

Mentat: Architecture

Mentat: Social Dynamics

Mentat: Results

Future work

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Results

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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

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Contents

Methodological approach

The Sociological Problem

Mentat: Architecture

Mentat: Social Dynamics

Mentat: Results

Future work

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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

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Thanks for your attention!

Samer Hassansamer@fdi.ucm.es

University of Surrey

Universidade de Lisboa

Universidad Complutense de Madrid

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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

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