Markov-achievable payoffs for finite-horizon decision models

Victor Pestien and Xiaobo Wang

Stochastic Processes and their Applications, 1998, vol. 73, issue 1, 101-118

Abstract: Consider the class of n-stage decision models with state space S, action space A, and payoff function g : (S x A)n x S --> R. The function g is Markov-achievable if for any possible set of available randomized actions and all transition laws, each plan has a corresponding Markov plan whose value is at least as good. A condition on g, called the "non-forking linear sections property", is necessary and sufficient for g to be Markov achievable. If g satisfies the slightly stronger "general linear sections property", then g can be written as a sum of products of certain simple neighboring-stage payoffs.

Keywords: Markov; decision; model; Payoff; function; Markov; plan (search for similar items in EconPapers)
Date: 1998
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