We consider a class of noncooperative hierarchical N-player games where the ith player solves a parametrized stochastic mathematical program with equilibrium constraints (MPEC) with the caveat that the implicit form of the ith player's in MPEC is convex in player strategy, given rival decisions. Few, if any, general purpose schemes exist for computing equilibria, motivating the development of computational schemes in two regimes: (a) Monotone regimes. When player-specific implicit problems are convex, then the necessary and sufficient equilibrium conditions are given by a stochastic inclusion. Under a monotonicity assumption on the operator, we develop a variance-reduced stochastic proximal-point scheme that achieves deterministic rates of convergence in terms of solving proximal-point problems in monotone/strongly monotone regimes with optimal or near-optimal sample-complexity guarantees. Finally, the generated sequences are shown to converge to an equilibrium in an almost-sure sense in both monotone and strongly monotone regimes; (b) Potentiality. When the implicit form of the game admits a potential function, we develop an asynchronous relaxed inexact smoothed proximal best-response framework, requiring the efficient computation of an approximate solution of an MPEC with a strongly convex implicit objective. To this end, we consider an ti-smoothed counterpart of this game where each player's problem is smoothed via randomized smoothing. In fact, a Nash equilibrium of the smoothed counterpart is an ti-approximate Nash equilibrium of the original game. Our proposed scheme produces a sequence and a relaxed variant that converges almost surely to an ti-approximate Nash equilibrium. This scheme is reliant on resolving the proximal problem, a stochastic MPEC whose implicit form has a strongly convex objective, with increasing accuracy in finite-time. The smoothing framework allows for developing a variance-reduced zeroth-order scheme for such problems that admits a fast rate of convergence. Numerical studies on a class of multi-leader multi-follower games suggest that variance-reduced proximal schemes provide significantly better accuracy with far lower run-times. The relaxed best-response scheme scales well with problem size and generally displays more stability than its unrelaxed counterpart.
