A decision model for environmental R&D

In this paper, a simple decision model is formulated to investigate the optimal funding level for an environmental R&D activity. The Bayesian approach is extended to research in situations where the researchers have prior beliefs different from the ultimate decision makers. Sensitivity analysis demonstrated how the optimal level of R&D varies with assumptions about: 1) the shadow price of R&D resources, which is a measure of the scarcity of funds; 2) the technical difficulty of the research area; 3) the potential payoffs involved in the decision that may be influenced by the research outcome; and 4) the prior knowledge of the key decision makers. Among other insights, analysis of the model demonstrated that the optimal research expenditure jumped from zero to a significant amount or conversely with small changes in parameters. The jump occurred when the expected cost of an uninformed decision equals the cost of research plus the expected cost of the contingent decision. In addition, the optimal research expenditure was sensitive to the technical difficulty of the research area.