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 some marginal distributions or the underlying dependence structure.
In this talk, we present an approach for analyzing decisions in such cases. Specifically, we propose a simulation procedure to create a collection of joint distributions that match the known information. We demonstrate our procedure using an actual oil & gas exploration decision and compare our method to the use of copulas and maximum entropy.
J. Eric Bickel is an assistant professor in the Graduate Program in Operations Research and the Department of Petroleum and Geosystems Engineering (by courtesy) at The University of Texas at Austin. In addition, Eric is a fellow in the Center for Petroleum Asset Risk Management (CPARM). His research interests include the theory and practice of decision analysis and its application to energy exploration & development, climate change, and sports. His research has been covered in The Wall Street Journal, The New York Times, The Financial Times, and Sports Illustrated.
Eric is the Vice President/President-Elect of the INFORMS Decision Analysis Society, which has over 1200 members. He holds an MS and PhD from the Department of Engineering-Economic Systems at Stanford University and a BS in Mechanical Engineering, with a minor in Economics, from New Mexico State University.