Course Coordinator: Prof. Uri Wilensky

Required Textbooks: None. Course reading packet.

Course Goal: Construct and analyze multi-agent models in both spatial and network topologies

Prerequisites: None for EECS 372. EECS 472 requires graduate school enrollment.

Detailed Course Topics:

  • What is an agent?
  • Stationary and moveable agents
  • Interactions between agents
  • Agent topologies
  • Properties of networks
  • Applications of ABM
  • Artificial Life
  • Comparison with Systems Dynamics Models
  • Integration of Machine Learning
  • Evolutionary computation
  • Systematic exploration of model parameter space
  • Verification of model specification
  • Replication of models
  • Validation of models
  • Connecting ABM with physical devices
  • Sensors and motors
  • Combining human and virtual agents
  • Participatory simulations


  • No exams for this class
  • 20% Participation
  • 30% Homework
  • 50% Final Project

Course Objectives for students:

When a student completes this course, he/she should be able to:

  • • Identify core mechanisms of novel agent-based models
  • • Identify trade-offs in the design and use of agent topologies
  • • Construct original multi-agent models
  • • Use behavior run and analysis tools to analyze model parameter space
  • • Verify and validate agent-based models
  • • Apply agent-based modeling to both scientific and everyday phenomena

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