A Simulation-Based Approach to Decision Making with Partial Information

The construction of a probabilistic model is a key step in most decision and risk analyses. Typically this is done by defining a single joint distribution in terms of marginal and conditional distributions. The difficulty of this approach is that often the joint distribution is underspecified. For example, we may lack knowledge of the marginal distributions or the underlying dependence structure. In this paper, we suggest an approach to analyzing decisions with partial information. Specifically, we propose a simulation procedure to create a collection of joint distributions that match the known information. This collection of distributions can then be used to analyze the decision problem. We demonstrate our method by applying it to the Eagle Airlines case study used in previous studies.