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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
Samer Hassan MABS 2009 3
Contents
Methodological approach
The Sociological Problem
Mentat: Architecture
Mentat: Social Dynamics
Mentat: Results
Future work
Samer Hassan MABS 2009 4
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
Samer Hassan MABS 2009 9
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
Samer Hassan MABS 2009 11
Contents
Methodological approach
The Sociological Problem
Mentat: Architecture
Mentat: Social Dynamics
Mentat: Results
Future work
Samer Hassan MABS 2009 12
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
Samer Hassan MABS 2009 14
Contents
Methodological approach
The Sociological Problem
Mentat: Architecture
Mentat: Social Dynamics
Mentat: Results
Future work
Samer Hassan MABS 2009 15
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
Samer Hassan MABS 2009 18
Fuzzy Friendship Evolution
Para ver esta película, debedisponer de QuickTime™ y de
un descompresor TIFF (sin comprimir).Para ver esta película, debedisponer de QuickTime™ y de
un descompresor TIFF (sin comprimir).
Para ver esta película, debedisponer de QuickTime™ y de
un descompresor TIFF (sin comprimir).
Para ver esta película, debedisponer de QuickTime™ y deun descompresor TIFF (sin comprimir).
Para ver esta película, debedisponer de QuickTime™ y de
un descompresor TIFF (sin comprimir).
Samer Hassan MABS 2009 19
Contents
Methodological approach
The Sociological Problem
Mentat: Architecture
Mentat: Social Dynamics
Mentat: Results
Future work
Samer Hassan MABS 2009 20
Results
Samer Hassan MABS 2009 21
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
Samer Hassan MABS 2009 22
Contents
Methodological approach
The Sociological Problem
Mentat: Architecture
Mentat: Social Dynamics
Mentat: Results
Future work
Samer Hassan MABS 2009 23
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
Samer Hassan MABS 2009 24
Thanks for your attention!
Samer [email protected]
University of Surrey
Universidade de Lisboa
Universidad Complutense de Madrid
Samer Hassan MABS 2009 25
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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