Stochastic Programming, Utility, and Sequential Decision Problems in Farm Management

Abstract

Abstract This paper presents a further development of discrete stochastic programming, viewed within the context of Bayesian decision theory. Some probability models and information structures (with and without additional information) are discussed, followed by an indication of how the stochastic programming matrix may be set up to reflect the various information structures. Some expected utility theories are then reviewed, and their usefulness in allowing the specification of a wide variety of objective functions for the stochastic programming model is illustrated. Lastly, a method is presented for determining the money value of additional information, additional resources, and the expected cost of uncertainty.