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 an application to intelligent transportation systems.