Complex systems consisting of a large number of interacting
components are in practice increasingly modeled through computer simulations
rather than via traditional equation based approaches. The resulting model
typically allows for little or no structural assumptions on the form of the
objective function or constraints, thus posing a challenging optimization
problem. We explore in this talk a novel optimization paradigm inherited from
game theory that animates the components of the system within a non-cooperative
game of identical interest. The optimizations take place though individual best
replies of the players, thus vastly reducing the dimensionality of the
optimization problems solved (the components’ joint interactions are reflected
indirectly through their shared objective function). We will illustrate the
approach by discussing applications to dynamic route guidance as well as to a
joint production systems optimization project within the GM Collaborative
Research Laboratory at the University of
Michigan.
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