Carl P. Simon, University of Michigan
Models in the social, biological and decision sciences play a key role in our understanding of and intervention in real world phenomena in those areas. Traditionally, modelers have had to make strong simplifying assumptions to analyze their models mathematically. Such assumptions include: homogeneous agents, random mixing among these agents, static equilibrium, and perfectly rational agents without need for learning or adaptation. Real world economies, ecologies and epidemics, for example, have diverse agents who make mistakes but learn from them, who choose with whom they interact in structured ways, and who are part of a truly dynamic process. The complex systems approach focuses on these real world properties. This talk will present an overview of models of the spread of HIV and will indicate what we have learned and can learn about this epidemic through a complex systems approach.
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