Complex Systems Models in the Social Sciences

Instructor(s):

  • Daniel Katz, Michigan State University
  • Kyle Joyce, University of California, Davis

The nonlinear dynamics exhibited by complex systems often pose difficult problems for modelers of those systems, especially when the complex systems are adaptive. This workshop is about modeling complex adaptive systems with an emphasis on agent-based modeling. Agent-based models consist of a number of diverse agents, the behaviors of which are governed by (often simple) decision rules. The dynamic interaction of the agents with one another and with their environment at the micro-level can produce emergent patterns and structures at the macro-level. This workshop provides an introduction to agent-based modeling and compares this approach to more traditional mathematical approaches (e.g., game theory). A variety of applications in the social sciences are used to introduce modeling complex adaptive systems including social/economic/political networks, electoral politics, civil war, and culture. There is a lab session that introduces students to the computer tools used to build agent-based models.

Note: Workshop lectures are held from 9 a.m.-10 a.m., while hands-on computer labs are held from 5 p.m.-6 p.m.

Fees: Consult the fee structure.

Tags: complexity, non-linear systems, agent-based models, Adaptive Systems

Course Sections

Section 1

Location: ICPSR -- Ann Arbor, MI

Date(s): July 20 - August 14

Time: 9:00 AM - 10:00 AM

Instructor(s):

  • Daniel Katz, Michigan State University
  • Kyle Joyce, University of California, Davis

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