Using inverse optimization to learn cost functions in generalized Nash games

As demonstrated by Ratliff et al. (2014), inverse optimization can be used to recover the objective function parameters of players in multi-player Nash games. These games involve problems multiple which affect each other their functions. In generalized equilibrium (GNEPs), a player’s set feasible actions is also impacted taken game. We extend framework (2014) find solutions for specific class GNEPs known as jointly convex GNEPs. The resulting formulation then applied simulated transportation problem on road network. see that our model recovers parameterizations produce same flow patterns original and this holds true across networks, different assumptions regarding players’ perceived costs, majority restrictive capacity settings associated numbers players. Code project found at: https://github.com/sallen7/IO_GNEP .