Obtaining Lower and Upper Bounds on the Value of Seasonal Climate Forecasts as a Function of Risk Preferences

A methodological approach to obtain bounds on the value of information based on an inexact representation of the decision maker's utility function is presented. Stochastic dominance procedures are used to derive the bounds. These bounds provide more information than the single point estimates associated with traditional decision analysis approach to valuing information, in that classes of utility functions can be considered instead of one specific utility function. Empirical results for valuing seasonal climate forecasts illustrate that the type of management strategy given by the decision maker's prior knowledge interacts with the decision maker's risk preferences to determine the bounds. Interest in ascertaining decision makers' willingness to pay for climate/weather forecasts has increased in recent years. Empirical studies such as Sonka et al.; Winkler, Murphy, and Katz; Baquet, Halter, and Conklin; and Brown, Katz, and Murphy have demonstrated that current and improved climate forecasts have potential economic value in decision making. This value depends critically on the structure of the decision set, the structure of the payoff function, degree of uncertainty in the decision maker's prior knowledge of climatic conditions, and the nature of the information system (Mjelde, Sonka, and Peel; Hilton). Embedded in the structure of the payoff function is the decision maker's relative preference for outcomes or, equivalently, the decision maker's risk preferences. Because risk preferences are difficult to quantify, this characteristic has re