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Course Description
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 Learning Sciences undergraduate students, it fulfills:
a Design of Learning Environments requirement
- 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
- For graduate students in the TSB program, it counts as either Graphics and Interactive Media, or Cognitive Systems
Course Objectives for students:
- When students complete this course, they 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.
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