This paper considers undiscounted two-person, zero-sum sequential games with finite state and action spaces. Under conditions that guarantee the existence of stationary optimal strategies, we present two successive approximation methods for finding the optimal gain rate, a solution to the optimality equation, and for any ϵ > 0, ϵ-optimal policies for both players.
This paper establishes a rather complete optimality theory for the average cost semi-Markov decision model with a denumerable state space, compact metric action sets and unbounded one-step costs for the case where the underlying Markov chains have a single ergotic set.
This paper discusses the structural equations, forecasting properties, dynamic characteristics, and economic policy implications of a quarterly econometric model of U.S. livestock and feedgrain markets. Quarterly, semi-annual, and annual endogenous variables are incorporated by allowing individual structural equations to be estimated and to enter into the solution of the model with different periodicities. Commodity prices are determined by market equilibrium conditions rather than by autoregressive and other time-series techniques.
This paper considers undiscounted Markov decision problems. With no restriction (on either the periodicity or chain structure of the problem) we show that the value iteration method for finding maximal gain policies exhibits a geometric rate of convergence, whenever convergence occurs. In addition, we study the behaviour of the value-iteration operator; we give bounds for the number of steps needed for contraction, describe the ultimate behaviour of the convergence factor and give conditions for the existence of a uniform convergence rate.
This paper studies economic policy toward feed grain and livestock markets by applying optimal control theory to a quarterly microeconometric model.
A survey is given of the present state of the art of value-iteration and related successive approximation methods, as well as of resulting turnpike properties, in both the discounted and undiscounted version of finite state and action Markov Decision Problems.
In this paper we consider a set of denumerable stochastic matrices where the paramter set is a compact metric space. We give a number of simultaneous recurrence conditions on the stochastic matrices and establish equivalences between these conditions. The results obtained generalize corresponding results in Markov chain theory to a considerable extent and have applications in stochastic control problems.
This paper investigates the solutions to the functional equations that arise inter alia in Undiscounted Markov Renewal Programming. We show that the solution set is a connected, though possibly nonconvex set whose members are unique up to the n* constants, characterize n* and show that some of these n* degrees of freedom are locally rather than globally independent.
This paper is concerned with the properties of the value-iteration operator which arises in undiscounted Markov decision problems. We give both necessary and sufficient conditions for this operator to reduce to a contraction operator, in which case it is easy to show that the value-iteration method exhibits a uniform geometric convergence rate.