AM-79 - Agent-based models

Learning Objectives: 
  • Compare and contrast agent-based models and cellular automata as approaches for modeling spatial processes
  • Describe how agent-based models use object-oriented programming constructs of inheritance and encapsulation to represent the behavior of heterogeneous and interactive and adaptive actors
  • Describe how multiple, different types of agents in a given system behave and interact with each other and their environment
  • Generate multiple, different types of agents in a given system
  • Describe how multiple parameters (e.g., number of agents, variability of agents, random number seeds for different series of random events or choices during each simulation) can be set within an agent-based model to change the model behavior
  • Explain how agent behaviors can be used to represent the effects actors have on each other and on their environment
  • Design simple experiments with an agent-based model
  • Design and implement a simple agent-based model using appropriate commercial or open source development tools
  • Conduct simple experiments with an agent-based model, analyze results, and evaluate their statistical significance with respect to degrees of freedom, sensitivity analyses, and uncertainty in the model
  • Describe how measurements on various inputs and outputs of a model can be used to describe model behavior and to relate model outcomes to various initial conditions
  • Describe how various parameters in an agent-based model can be modified to evaluate the range of behaviors possible with a model specification
  • Determine if an agent-based model has been run enough times with enough different random number seeds for rigorous inference of its results