What is the true value of forecasts?

Understanding the economic value of weather and climate forecasts is of tremendous practical importance. Traditional models that have attempted to gauge forecast value have focused on a best-case scenario, in which forecast users are assumed to be statistically sophisticated, hyperrational decision makers with perfect knowledge and understanding of forecast performance. These models provide a normative benchmark for assessing forecast value, but say nothing about the value that actual forecast users realize. Real forecast users are subject to a variety of behavioral effects and informational constraints that violate the assumptions of normative models. In this paper, one of the normative assumptions about user behavior is relaxed—users are no longer assumed to be in possession of a perfect statistical understanding of forecast performance. In the case of a cost–loss decision, it is shown that a model of users’ forecast use choices based on the psychological theory of reinforcement learning leads to a behavioral adjustment factor that lowers the relative value score that the user achieves. The dependence of this factor on the user’s decision parameters (the ratio of costs to losses) and the forecast skill is deduced. Differences between the losses predicted by the behavioral and normative models are greatest for users with intermediate cost–loss ratios, and when forecasts have intermediate skill. The relevance of the model as a tool for directing user education initiatives is briefly discussed, and a direction for future research is proposed