  This work concerns controlled Markov chains with finite state and action
spaces. The transition law satisfies the simultaneous Doeblin condition, and
the performance of a control policy is measured by the (long-run)
risk-sensitive average cost criterion associated to a positive, but otherwise
arbitrary, risk sensitivity coefficient. Within this context, the optimal
risk-sensitive average cost is characterized via a minimization problem in a
finite-dimensional Euclidean space.
