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Agent-Based Modelling

This overview presents Agent-Based Modeling (ABM) as a powerful tool for simulating complex systems, emphasizing intelligent agents driven by simple rules. Key features include autonomy, reactivity, and decision-making. The integration of ABM with tools like Repast and MASON demonstrates its capabilities in various applications, from urban simulation to resource management. While ABM offers significant insights into emergent behavior and system dynamics, it also presents challenges in validation and prediction. This work aims to bridge theory and practice in the field of computational modeling.

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Agent-Based Modelling

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  1. Agent-Based Modelling Piper Jackson PhD Candidate Software Technology Lab School of Computing Science Simon Fraser University

  2. History • Von Neumann machines: • Self-reproducing • Cellular Automata • Object oriented programming (OOP)

  3. Example: Boids • Simple agents • 3 rules for movement • Complex, realistic movement • Small changes  different behaviour http://cs.gmu.edu/~eclab/projects/mason/ Separation Alignment Cohesion

  4. Agents • Interact with others and/or environs • Intelligent and purposeful • Goal driven and decision making • Bounded rationality

  5. Agents • Features: • Autonomy • Social Ability • Reactivity • Proactivity • Characteristics: • Perception • Performance • Motion • Communication • Action • Memory • Policy N. Gilbert (2008) Agent-Based Models

  6. Characteristics Complex Emergent Chaotic Dynamic Interactive

  7. Benefits • Isolating prime mechanics • Interaction of micro & macro • What if? scenarios • Finding equlibria • Clarity & Transparency

  8. Ontological Correspondence • Entities organized in an easily comprehensible fashion • Conceptual model validation • Embedded in theory • Communication & Visualization • Reproducibility

  9. Drawbacks • Analysis • Not a replacement for analytical methods • Operational Validation • Many assumptions • Improbable or unmeasurableIRL • Difficult for prediction

  10. Example: Sugarscape • Mobile agents on a grid • Collecting & metabolizing sugar • Sugar: metaphor for any resource • Evolution, marital status, inheritance http://sugarscape.sourceforge.net/

  11. Example: Mastermind

  12. Tasks & Requirements • Identify phenomena • Agents, events, factors • Formalize domain concepts • Formal methods, equations • Simplify! • Reduce, group, isolate

  13. Abstract State Machines • First order structures & state machines • ASM Thesis • Ground model • Refinement

  14. Control State Diagrams

  15. Agent Specifics • Scenario parameters • Variables • Functions: what an agent can do • Model of intelligence • Logic

  16. Models of Intelligence • Reactive • Beliefs, Desires & Intentions • OODA

  17. Implementing Logic • Conditionals • state machine • Fuzzy • Deterministic/Non-Deterministic

  18. Programming • Agent-Based simulation software: • Repast • MASON • Object oriented programming • Java, Python, C#

  19. Iterative Experimentation

  20. From R. Sargent(2010) Verification And Validation Of Simulation Models

  21. Hybrid Models • Geographical CA/ABM Hybrid • Y. Xie, M. Batty, and K. Zhao (2007) “Simulating Emergent Urban Form Using Agent-Based Modeling: Desakota in the Suzhou-Wuxian Region in China” • 2 kinds of agents: developers, townships • Active at different scales • Cellular landscape: suitability variable

  22. CoreASM • Abstract State Machine paradigm • Executable • Validation by testing • Open source • Interaction with Java

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