Preferred Rules in Continuous Time Markov Decision Processes

Motivated by a planning horizon result for continuous time Markov decision chains, we study decision rules, called preferred, which may be used in the initially stationary part of nearly optimal policies. We characterize these rules and then, under conditions involving state recurrence and accessibility, consider finding such rules. We also discuss the connection between preferred rules and certain discounted process decision rules, and the role of preferred rules in optimal policies.