Decision-Making Under Uncertainty

Decision-making under uncertainty is an important area of study in numerous disciplines. The variety of quantitative methods that have been proposed to address environmental and water resources problems reflects the importance of this subject. In a review of the literature, methods were compared and contrasted and promising areas for future research were identified. Conclusions drawn from the review were that 1) large gains may be realized from cross-disciplinary research, 2) significant benefits may be realized from considering uncertainty, 3) advanced algorithms—probabilistic search methods and efficient methods for Bayesian analysis—and increased computing power should greatly extend the applicability of existing methods, and 4) in particular, decision-theoretic methods that have wide application for sequential decision-making. A new decision-theoretic method, Bayesian programming (BP), was developed that takes advantage of the increased computing power and improvements in Bayesian analysis methods. The method has wide applicability, suitable for problems in which there is 1) uncertainty in the modeling, 2) stochastic behavior in the systems that are modeled, 3) the possibility to reduce uncertainty through data collection, and 4) the opportunity for a recourse decision after a period of data collection. The approach combines systematic search method