Consumer information programs can be more effective if they are conceived within a marketing framework which views consumer information as a product to be marketed. A methodology is outlined which can assist consumer information program developers in identifying information needs from the consumer's point of view, rather than the policy maker's.
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.
This paper is concerned with the effects of capital risk on optimal individual savings decisions in a simple two-period setting. We investigate the respective roles played by risk and time preferences in answering the following related questions: Q1: Will savings increase, remain constant or decrease in response to an increase in capital risk? Q2: Is optimal saving in the presence of capital risk greater than, equal to or less than optimal saving in the certainty case where the rate of return equals the mean (uncertain) return?
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.
For problems involving choices over "certain x uncertain" consumption pairs, it is almost universally assumed that the decision maker's preferences can be represented by an expected TPC (two-period cardinal) utility function.
An example for undiscounted multichain Markov Renewal Programming shows that policies may exist such that the Policy Iteration Algorithm (PIA) can converge to these policies for some (but not all) choices of the additive constants in the relative values, and as a consequence that the PIA may cycle if the relative values are improperly determined.
This paper is concerned with the optimality equation for the average costs in a denumerable state semi-Markov decision model. It will be shown that under each of a number of recurrency conditions on the transition probability matrices associated with the stationary policies, the optimality equation has a bounded solution. This solution indeed yields a stationary policy which is optimal for a strong version of the average cost optimality criterion.