Agent-based Modeling and
Simulation for the Social Scientist
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Amineh Ghorbani, Virginia Dignum, Pieter Bots, Gerard Dijkema, Bert Belder
Delft
University of
Technology
Challenge the future
Goal
• Framework for agent-based conceptualization and simulation
• Rich enough to capture a diverse range of social systems
• Support developers with little/no programming/software engineering
knowledge
• Application areas
• Policy design / public goods problems
• Social systems: complex behavior / discrete entities
• Approach
• Collaborative modelling
• Institutional analysis (Ostrom)
• Model driven engineering (MDE)
• meta-modeling and semi-automatic code generation
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Applications
Domains
Commonalities
• Wood-fuel market
• Domain characteristics
• E-Waste recycling
• ‘What-if’ analysis of policies
• Consumer lighting
• Problem-owners /domain
• Basic income grants
experts had limited
• Family-based care
simulation knowledge
•…
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Common characteristics
• Effect of incentives / policies
• Social networks and institutions
• Individual interests
• Global consequences
• Multi-criteria decision making
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What is MAIA?
• Modeling Agents based on Institutional Analysis
• Formal meta-model
• Institutional perspective (IAD – Ostrom)
• Web based design tool
• Declarative rather than procedural
• Semi-automatic simulation generation
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MAIA Architecture
• The MAIA meta-model
finetuning
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Institutions
An institution is any structure or mechanism of social order and
cooperation governing the behavior of a set of individuals within a
given human community.
Institutions are identified with a social purpose and permanence,
transcending individual human lives and intention by enforcing rules
that govern cooperative human behavior
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Institutions
Individuals do
activities
(repetitive)
By product of
interactions
outcomes affect
others too
1- Rules accepted by
everyone
2- Used in practice
3- Durability
Rules created
to manage
activities
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Institutional frameworks
• Institutions have two sides:
• Enable interactions, provide stability, certainty, and form the basis for trust.
• Cause power relations and may hamper reform.
• Important to understand effects of institutions
Institutional (re)design
Analyze and Understand for Design Institutional Frameworks
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Institutional Analysis and Design
unit of analysis
Elinor Ostrom
Nobel laureate
1933-2012
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Institutional Analysis and Development
Framework (IAD)
1. Participants
Physical world
Community
Rules
Action Arena
2. Positions
1. Position rules
3. Actions
Action2. Boundary rules
Patterns of
4. Potential
Situation
interaction
3. Authority rules outcomes
4. Aggregation5.rules
Functions that
5. Scope rules
Evaluation
map actions
Criteria
6. Information rules
into outcomes
Participants
7. Payoff rules 6. Information
7. Cost and
benefits
Outcomes
Resources, preferences, information
and selection criteria
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Extending IAD
• Formalization of concepts
• MAIA formal model
• Robust information and consensus
• MAIA online tool supports flexible conceptualization through
participatory exploration
• Supports reflection and discussion
• Outward looking
• Information collected directly reflects the experiences and
perceptions of stakeholders themselves
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MAIA Meta model
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Collective structure = set of agents
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Constitutive Structure
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Institutions: ADICO
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Physical components
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Operational structure
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MAIA Modelling Environment
http://test1.eeni.tbm.tudelft.nl/maia/ MAIA
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Translation to Java Code
• MAIA MM is developed as an e-core model
• EMF environment in Eclipse for model-driven software development.
• XML specification.
• Output of MAIA web-tool is based on MAIA MM
• Explicit, fixed, rules to convert MAIA model (XML) to Java
simulation
• Current work: translator code, for automatic generation of code
from a MAIA-based model.
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From rules to code
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Agent behaviour
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MAIA Approach
declarative
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Conclusions
MAIA framework for agent-based simulation
• Rich enough to capture a diverse range of social systems
• Support developers with little/no programming/software
engineering knowledge
• Based on Institutional analysis (Ostrom)
• Formal model
• Verification
• Model driven engineering (MDE) for semi-automatic code generation
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Future work
• Extend and validate code generation
• Visualisation of simulation results
• Library of agent behaviours
• Extensive evaluation
• Transformation of MAIA models into other simulation
environments (e.g. Netlogo or Repast)
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MAIA Architecture
More info:
a.ghorbani@tudelft.nl
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