The Markov chain approximation method is a widely used, relatively easy to use, and
efficient family of methods for the bulk of stochastic control problems in continuous time for
reflected-jump-diffusion-type models. It has been shown to converge under broad
conditions, and there are good algorithms for solving the numerical problems if the
dimension is not too high. We consider a class of stochastic differential games with a
reflected diffusion system model and ergodic cost criterion and where the controls for the two …

The Markov chain approximation method is a widely used, robust, relatively easy to use, and
efficient family of methods for the bulk of stochastic control problems in continuous time for
reflected-jump-diffusion-type models. It has been shown to converge under broad
conditions, and there are good algorithms for solving the numerical problems if the
dimension is not too high. Versions of these methods have been used in applications to
various two-player differential and stochastic dynamic games for a long time, and proofs of …