The output of agricultural economics research is information, much of it aimed at designing or improving institutions. Bayesian decision theory and economic surplus analysis have been suggested as possible approaches to evaluate that information. This article takes a critical look at the strengths and weaknesses of combining those approaches for empirical evaluation of agricultural economics research. It presents three case studies, utilizing prior and posterior probabilities elicited from key decision makers. Direct application of the probabilities to a simple set of states and actions is best suited to situations involving a single policy‐making center, or a consensus decision‐making process.
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Schimmelpfennig et al. (2003) studied this question.
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