An Ergodic Theorem for Stochastic Programming Problems

To justify the use of sampling to solve stochastic programming problems one usually relies on a law of large numbers for random lsc (lower semicontinuous) functions when the samples come from independent, identical experiments. If the samples come from a stationary process can one appeal to the ergodic theorem proved here. The proof relies on the 'scalarization' of random lsc functions.