PSYCHOLOGY 120 Section:
Spring Quarter 2007
Course description: This course provides an introduction to agent-based modeling. Agent-based modeling is a computer simulation strategy aimed at modeling the behavior and interactions of individuals in an environment. Individuals can be any entities that behave and interact with each other. Typical agents or individuals of interest are animals and humans. Agent-based modeling is often used in three different ways. The first approach is to explore and analyze theoretical ideas that typically involve complex interactions and environmental structures that are difficult to analyze with other quantitative methods. For example, I was interested in exploring the question of whether estrous synchrony among female rats affected reproductive success. I wanted this simulation to be based on real data about rat habitats and individual behavior. This required developing an agent-based simulation model. The second approach is to develop detailed predictive models of group or population behavior. Agent-based models are often used in ecology in just this way. These models have met with limited success because the quantity of precise data required to specify rules for how individuals behave and interact. The third approach is to use agent-based models to analyze and interpret data. This approach may use genetic algorithms to fit the behavior of the agents to data.
In this course, we will learn how to build and analyze agent-based models using a state-of-the-art simulation environment, mason. You will learn step-by-step how to build, visualize, analyze, and interpret agent-based models. Try out a simulation here.
Course format: This is a 4-unit course comprised of combined lecture and lab. To learn how to develop computer simulation models requires a lot of hands on experience, which is best accomplished in an environment that combines lecture and lab.
Course topics: Topics include (1) how to use the java-based programming environment, mason; (2) a series of step-by-step examples of how to build and visualize agent-based models with increasingly sophisticated capabilities; and (3) techniques for analyzing and interpreting models.
Grading: Grades will be based model-building assignments and a final project. The final project can be done individually or as a team, but if several people work on a final project, it should reflect a team effort. Final projects can be on any topic (including implementing a previously published model), but it must be improved by the instructor at least 3 weeks before the end of the quarter.
Textbook Information not Available Yet
|Classroom||Class Schedule||Course Website|
|188 Young||T R 2:10 PM - 4:00 PM||http://psychology.ucdavis.edu/COURSES/Schank/PSC120/Syllbus120-s07.htm|
|Instructor||Instructor Email||Office||Office Hours|
|Jeffrey Schank , Ph.D.||268D Young Hall||W 9:00 to 12:00 and by appointment|