EECS 372/472:
Designing & Constructing Models With Multi-Agent Languages

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Course Description

• Brief Course Summary
• Curricular Requirement Satisfaction
• Course Objectives
• Topics Covered
• Grading

Brief Course Summary

This course focuses on the exploration, construction and analysis of multi-agent models, which simulate emergent scientific phenomena in a wide variety of content domains (such as birds flocking, diseases spreading, or consumers affecting market dynamics). We use state of the art agent-based modeling and complexity science tools, and discuss methodology for replication, verification and validation of models.

Since agent-based modeling is a useful technique in numerous disciplines, we believe that students from a broad spectrum of backgrounds can benefit from this course, and we encourage diversity in enrollment, which is why there are no pre-requisites for this course. In addition to computer/cognitive/learning scientists, we welcome engineers, economists, linguists, biologists, mathematicians, industrial engineering and management scientists, business students, and any other students interested in complex systems, social networks, or other phenomena with many interacting elements.

Curricular Requirement Satisfaction

• For Computer Science undergraduate majors, it counts towards:
  one credit of the project course requirement
  the A.I. or Interfaces area breadth and depth requirements

• For Cognitive Science undergraduate majors, it counts as:
  an advanced elective credit for the Cognitive Science major.

• For Learning Sciences graduate students, it fulfills:
  a computational methods requirement (and possibly a design course requirement with permission of instructor)

• For graduate students working the Cognitive Science Specialization: it counts as:
  one course credit toward specialization

• For graduate students working with the Northwestern Institute on Complex Systems (NICO), this is a core course

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-based 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
  Understand the methodological implications of using agent-based modeling as a research tool

Topics Covered

• What is an agent?
• 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

Grading

• Class-participation: 15%
• Homework Assignments: 35%
• Final Project: 50%
  
• There will be no exams for this course.
  
• Graduate students are required to do additional work.