Setting environmental policy when experts disagree

How can a decision-maker assess the potential of environmental policies when a group of experts provides divergent estimates on their effectiveness? To address this question, we propose and analyze a variant of the well-studied $\alpha$-maxmin model in decision theory. In our framework, and consistent to the paper's empirical focus on renewable-energy R\&D investment, experts' subjective probability distributions are allowed to be action-dependent. In addition, the decision maker constrains the sets of priors to be considered via a parsimonious measure of their distance to a benchmark ``average'' distribution that grants equal weight to all experts. While our model is formally rooted in the decision-theoretic framework of Olszewski~\cite{o07}, it may also be viewed as a structured form of sensitivity analysis. We apply our framework to original data from a recent expert elicitation survey on solar energy. The analysis suggests that more aggressive investment in solar energy R\&D is likely to yield significant dividends even, or rather especially, after taking expert ambiguity into account.