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Agent-Based Modelling

Agent-Based Modelling. Piper Jackson PhD Candidate Software Technology Lab School of Computing Science Simon Fraser University. History. Von Neumann machines: Self-reproducing Cellular Automata Object oriented programming (OOP ). Example: Boids. Simple agents 3 rules for movement

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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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