Complex Systems Models in the Social Sciences

Instructor(s):

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

The nonlinear dynamics exhibited by complex systems often pose difficult problems for modelers of those systems, especially when the complex systems are adaptive. The growing availability of computers has led to a recent proliferation of "bottom-up, agent-based" models of complex adaptive systems. These models consist of a number of interacting agents, and each agent's behavior is governed by a small set of simple rules. However, the interaction of the agents can produce complex "emergent" structures and dynamic behaviors of individuals and groups. This workshop will give an introduction to bottom-up approaches to computer modeling and compare them to more traditional mathematical (analytical) approaches and to top-down computer models (e.g., typical macro-economic models). It will also offer a survey of the field of Evolutionary Computation (EC), including a discussion of the role of EC in agent-based models. A number of social science applications will also be reviewed and analyzed.

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 21 - August 15

Time: 9:00 AM - 10:00 AM

Instructor(s):

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

Syllabus:

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