Psychology | Adaptive and Multiagent Models of Social Behavior
P747 | 26593 | Smith, E

P747: Adaptive and Multiagent Models of Social Behavior Eliot Smith
and Robert Goldstone, Psychology Fall 2005

In complex systems, adaptive behavior emerges from interactions of
many parts – for example, from the interactions (e.g., competition
or negotiated exchanges) between multiple independent
agents. Complex adaptive systems display many dynamic properties
	Emergent behavior
	Cooperative/competitive interactions
	Decentralized control
Notably, these properties are found in systems that appear totally
dissimilar (businesses, social networks, insect colonies, neural
networks), suggesting that they derive from fundamental aspects of
dynamically interacting elements rather than from highly specific
details of particular systems.  

The goals of this course are to enable students to understand the
behavior of complex adaptive systems, by analyzing multiple specific
examples in diverse areas. Students will develop facility in the
Netlogo language, and will use it to produce a meaningful simulation
model over the course of the semester. We will particularly
emphasize the use of complex systems thinking as an approach for
theory-building in social psychology, drawing on the ability of this
approach to simultaneously model interacting processes at multiple
levels (individual actions, social interactions, and emergent group
behavior) that are relevant for human social behavior.

The course would be appropriate for graduate students in many areas
of psychology or related fields who are interested in complex
adaptive systems or multiagent modeling, as well as for those
interested in applying these techniques to understanding topics
within social psychology. No prior background in computational
modeling will be assumed.