Decision-making involving low probability high consequence events under risk and uncertainty

Abstract

Research in progress described in this paper addresses the problem of decision making in situations involving low probability high consequence events. The traditional Expected Utility Model (EU) has significant limitations in such circumstances as documented in multiple research results. The models discussed in this paper is an adaptation of the Multiple Quantile Model (MQT) representing a rational decision support scheme suited to regular as well as low probability high consequence events to the complex dynamic scenarios, in which decision making has to be based on highly uncertain, often unreliable heterogeneous data and information. The core of this scheme is a combination of the Multiple Quantile Theory with the Transferable Belief Model (TBM) and Anytime Decision making. An example of this approach with numeric simulations is given and the directions of future work are outlined.