Explores a multi-agent resource allocation problem in an environment of incomplete information. The paper posits the existence of a central policy maker who integrates information acquisition with decision making, in order to optimally allocate resources among multiple agents. The study focuses on three salient aspects: first, the policy maker uses a nonparametric revealed preference approach for the elicitation of each agent's preference profiles. This allows the policy maker to avoid distorting the allocation decision and emerging with suboptimal allocations. Second, the process of information gathering and decision making is a challenging task in view of the extremely large numbers of possible profiles for each agent, even for problems of modest size. To find an optimal allocation, the policy maker can use schemes that utilize partial preference information to handle the complexity of the problem. Third, the incorporation of costs into the information process and the minimal assumptions made about the problem environment allow the policy maker to realistically analyze the resource allocation problem.>

We present the file search problem in a decision-theoretic framework, and discuss a variation of it that we call the common index problem. The goal of the common index problem is to return the best available record in the file, where best is in terms of a class of user preferences. We use dynamic programming to construct an optimal algorithm using two different optimality criteria, and we develop sufficient conditions for obtaining complete information.