A differentiable path-following method to compute subgame perfect equilibria in stationary strategies in robust stochastic games and its applications

As an effective paradigm to address uncertainty in payoffs and transition probabilities, robust stochastic games have been formulated in the literature. This paper is concerned with the computation of subgame perfect equilibria in stationary strategies (SSPEs) in robust stochastic games. To tackle this problem, we develop in this paper a globally convergent differentiable path-following method by exploiting the structures of the games. Incorporating a logarithmic-barrier term into each player’s payoff function with an extra variable between zero and one, we constitute a logarithmic-barrier robust stochastic game in which each player solves in each state a convex optimization problem. An application of the optimality conditions to the barrier game together with a fixed-point argument yields a polynomial equilibrium system for the barrier game. As a result of this system, we establish the existence of a smooth path that starts from an arbitrary mixed strategy profile and ends at an SSPE as the extra variable descends from one to zero. As an alternative scheme, we make up a convex-quadratic-penalty robust stochastic game and attain a globally convergent convex-quadratic-penalty differentiable path-following method for SSPEs in robust stochastic games. Numerical comparisons show that the logarithmic-barrier path-following method significantly outperforms the convex-quadratic-penalty path-following method. To further evince the value of the proposed methods, we apply the logarithmic-barrier path-following method to solve a supply chain configuration problem and a market entry problem from medical waste recycling.