You are currently viewing an archived version of Topic Agent-based Modeling.
If updates or revisions have been published you can find them at Agent-based Modeling.
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
You are currently viewing an archived version of Topic Agent-based Modeling. If updates or revisions have been published you can find them at Agent-based Modeling.
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