AGENTBASED MODELING

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• AGENT-BASED MODELING
• Güven Demirel

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OUTLINE

• What is Agent-Based Modeling (ABM)
• Complexity Science ABM
• Methodological Status of ABM
• Building Blocks of ABM
• Agent Definitions Characteristics
• How to Model An Agent?

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OUTLINE (ctd.)

• Different Approaches to Agents in ABM
• Multiple Agents Interactions Among Agents
• Environment Types
• The Process of ABM Building
• An Example Model SugarScape Model
• SD-ABM Links Comparison

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ABM Definitions

• The main concern and interest of Agent-Based
  Modeling is the experimental study and modeling
  of agents in a common world of compound and
  unpredictable effects of a population of agents
  in a common world.
• (Castelfranchi, C., Simulating with Cognitive
  Agents The Importance of Cognitive Agents, MABS,
  1998)
• Agent-based modeling focuses on the interaction
  of rule-based agents. The power and intelligence
  of real life system, for example, is mimicked in
  the laboratory populated by agents that act in
  the model space exactly according to rules
  observed in real life.
• (Scholl, H., J., Looking Across the Fence
  Comparing Findings From SD Modeling Efforts with
  those of Other Modeling Techniques)
• The aim of agent-based modeling is to look at
  global consequences of individual or local
  interactions in a given space.
• (Reynolds, C., W., Individual-Based Modeling,
  1999)

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Complexity Science ABM

• The modeling domain of ABM complex systems.
• Complexity Science the science of modeling and
  analysis of complex systems.
• Science of Emergence
• Examples of emergence in sand pile, el Farol bar
  and firefly models
• Unexpectedness and unpredictability
• Focus on nonlinear interactions
• Wholistic nature
• (Ref. Castelfranchi, C., Simulating with
  Cognitive Agents The Importance of Cognitive
  Emergence, MABS, 1998)

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Complexity Science ABM

• Application Areas
• Agent-Based Computational Economics
• Agent-Based Computational Demography
• Individual-Based Modeling in Ecology and Biology
• Political Science
• Anthropology
• Sociology
• Physics, etc.
• (Ref. Billari, C., F., et. al. Agent-Based
  Computational Modeling, 2006)

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Complexity Science ABM

• Research Institutes
• Santa Fe
• New England
• Brookings
• Some Important Researchers in the Field
• R. Axtell J. Epstein (Growing Artificial
  Societies Social Science From the Bottom Up,
  1996)
• R. Axelrod (The Complexity of Cooperation
  Agent-Based Models of Competition and
  Collaboration, 1997)
• D. Farmer
• T. Schelling (Micro Motives and Macro
  Behavior,1978)

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Methodological Status of ABM

• ABM as a Social Simulation Methodology (as an
  alternative to qualitative theories)
• Computer simulation of complex social processes
• Descriptive modeling
• More precise and formal definitions compared to
  qualitative social sciences
• Used to test theoretical hypotheses
• Production of data and validation
• Toy-world character and policy analysis
• (Ref. Conte, R., MAS and Social Simulation,
  MABS, 1998
• Gilbert, N., Terna, P. How to build and use
  agent-based models in social science,1999)

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Methodological Status of ABM

• ABM as a Micro Modeling Approach
• Constitution of Artificial Societies of
  Artificial Agents
• Models of Individual Agents and Interactions
• Capturing Heterogeneity
• Analysis of Emergent Phenomena
• Micro-Macro Link
• Application of Distributed Artificial
  Intelligence Techniques
• Is micro modeling always good?
• (Ref. Axtell, R., Epstein, J. Growing Artificial
  Societies Social Science From Bottom Up,1996)

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Methodological Status of ABM

• ABM as an Alternative to Assumptions of
  Mathematical Models
• An alternative to RAP
• Bounded rationality, not global optimization
• Rule-based formulation, where heuristic utility
  maximization is only one rule
• Deductive or Inductive
• Deductive in the sense that behavioral rules are
  given
• Inductive in the sense that these rules can be
  adaptive and if so should be analyzed
• Inductive in the sense that behavior is analyzed
• (Ref. Gilbert, N., Terna, P. How to build and
  use agent-based models in social science,1999
• Gilbert N. Quality, Quantity and Third
  Way, 2001)

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Building Blocks of ABM

• Multiple Agents
• Individual Agent Layer
• Interactions Among Agents Layer
• Environment

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What is an Agent?

• Perceive-Reason-Act Approach
• perceive the environment, reason about its
  perceptions, and act based on the reasoning
  (traditional AI).
• Thermostat, the simplest agent, people,
  organizations, robots, etc.

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What is an Agent?

• An (intelligent) agent perceives its environment
  via sensors and acts rationally upon that
  environment with its effectors.
• Rational mapping between percept sequences and
  actions.
• Agents as computational processes that try to
  model the capabilities of humans.
• Agent as an autonomous software component capable
  to interact with environment and other agents and
  act according to its agent program.
• (Ref. Wooldridge, M., Multiagent Systems,2001)

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More on Agents

• Beliefs
• Goals
• Plans
• Agent Function Agent Program
• Emergence of ABM from Distributed AI.
• Agents as computational processes implemented on
  a computer that have
• Autonomy
• Social ability
• Reactivity
• Proactivity

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Agent Characteristics

• Autonomy (Freedom to act independently)
• Heterogeneity (different designs, no need to
  expose internal structure)
• Adaptivity (Learning, being able to change
  decision rules, goals, plans, etc. based on
  feedback from system performance)
• Self-interested (work to reach own goals)
• (Ref. Wooldridge, M., Multiagent Systems,2001)

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Types of Agents

• Simple Reflex Agents
• Model-Based Reflex Agents
• Goal-Based Agents
• Utility-Based Agents
• Learning-Agents
• Note This part is prepared mainly on Russell,
  S., Norvig, P. Artificial Intelligence A Modern
  Approach, 2003

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Modeling of Simple Reflex Agents
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Model-Based Reflex Agents
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Goal-Based Agents
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Utility-Based Agents
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Learning Agents
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Different Approaches to Agents in ABM

• KISS Principle
• Complex Agents Perspective
• Agent type depends on model purpose and domain.
• Dominance of usage of collection of
  condition-action rules in ABM.
• More use of AI algorithms, techniques. For the
  design of adaptive (learning) agents
• Learning by Induction
• Reinforcement Learning
• Learning through Artificial Neural Networks
• Learning by Genetic Algorithms
• (Ref. Billari, C., F., et. al. Agent-Based
  Computational Modeling, 2006)

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Genetic Algorithm

• Evolutionary algorithm
• Biological analogy Evolution by natural
  selection
• A population of individuals, each with some
  degree of fitness, a metric explicitly defined
  by the modeler.
• The fittest individuals are reproduced by
  breeding them with other fit individuals to
  produce offspring that share the features from
  parents.
• Average fitness increases as population adapts to
  environment.
• Individuals of GA may be
• Agents Evolution of the agent population as a
  whole
• Agent Algorithms Evolution of better algorithms,
  i.e. increases utility.
• (Ref. Gilbert, N., Terna, P. How to build and
  use agent-based models in social science,1999)

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Multiple Agents Interactions Among Agents

• Communication is done via messaging.
• Interaction Types
• Coordination
• Cooperation
• Negotiation
• Competition

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Environment Types

• Fully vs. Partially Observable
• Deterministic vs. Stochastic
• Episodic vs. Sequential
• Static vs. Dynamic
• Discrete vs. Continuous

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The Process of ABM Building

• Determination of whether the model to be
  constructed will be a specific or generic model
• Determination of actual actors in the real system
  and software agents are representations of a
  subset of these actors.
• Determination of model abstraction/aggregation
  level.
• Determination of what cognitively oriented
  computations the agents can perform
• Selection of agent architecture
• Selection of software platform and model
  implementation
• (Ref. Doran, J. Agent design for Agent-based
  modeling,2006)

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An Example Model

• SUGARSCAPE

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ABM vs. SD

• Two different perspectives in multiagent systems
  modeling
• Focus on individual, disaggregated agent actions
  (micro-motives) modeling, the model overall
  behavior emerges as a result of unfolding of the
  structure agents decisions and interactions.
  (Agent-Based Modeling)
• Focus on macro, aggregated system variables, the
  relations among the variables determine the model
  behavior. (System Dynamics)

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ABM vs. SD

• SD
• Stock-flow structure
• Differential/difference equations, thus also
  called equation-based modeling.
• Feedback theory, circular causality
• ABM
• Agent Program
• Behavioral Rules Interactions
• Adaptation and Learning

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ABM vs. SD

• Aggregation
• SD is based on aggregation philosophy. SD makes
  an abstraction from single events and
  individuals and forms a macro level modeling
  approach. Focus on system-level variables,
  observables.
• ABM is a micro level modeling approach. ABM
  focuses on the individual agents actions. ABM
  defines behavior at individual level.
• Policy Analysis
• It is more natural to model multiagent systems by
  ABM for policy analysis regarding individual
  agents.

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ABM vs. SD

• Adaptation
• SD model structures are typically static.
• Agent decisions, reasoning, goals, etc. change by
  learning adaptation. As a result system
  structure may show adaptation to changing
  conditions.
• Heterogeneity
• SD does differentiate among individuals.
• Each agent has its own design, need not expose
  its internal structure to others in the system.

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ABM vs. SD

• Implementation
• ABM may have more direct consequences for
  implementation compared to SD, reference to
  direct inferences about individual agent
  behaviors.
• Validation
• Structural Validation
• Behavioral Validation
• (Ref. Scholl, H., J., Agent-Based and System
  Dynamics Modeling A Call for Cross study and
  Joint Research, 2001
• Scholl, H., J., Looking Across the Fence
  Comparing Findings From SD Modeling Efforts with
  those of Other Modeling Techniques
• Parunak, H., V., D. et. al., Agent-Based
  Modeling vs. Equation-Based Modeling A Case
  Study and Users Guide, MABS, 1998
• Rahmandad, H., Heterogeneity and Network
  Structure in the Dynamics of Contagion Comparing
  Agent-Based and Differential Equation Models,
  ISDC, 2004
• Demirel, G., Aggregated and
  Disaggregated Approaches to Multiple Agent
  Dynamics, ISDC, 2006 )

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• THANKS