Should climate policy account for ambiguity?


Climate change is fundamentally an `out-of-sample’ problem – our available information does not tightly constrain predictions of the consequences of rapid increases in greenhouse gas concentrations. Moreover, the fact that we haven’t observed much warming up to the present makes it very diffcult to validate scientifc and economic models of the medium to long-run consequences of climate change. We have many models, each based on roughly plausible assumptions about the dynamics of the climate-economy system, but we do not have confidence in our ability to select between them.

Traditional approaches to decision under uncertainty do not permit a decision maker’s confidence in her information set to influence her choices. They require us to combine probability distributions from different models into a single summary distribution, even if we are not confident of our ability to discern which model captures reality best. Since all probabilities are created equal in this framework, this summary distribution is treated the same as a distribution that arises from a single well validated model. The decision framework forces us to make subjective probability judgments, but decision makers do not distinguish them from probabilities derived by constraining models with data.

We suggest that approaches to decision under uncertainty that allow us to work with probabilities of different quality without combining them into a summary distribution may provide an attractive set of tools for analysis of climate policies. We set out the conceptual arguments for a departure from expected utility theory, give examples of alternative approaches to decision making under uncertainty, and discuss several common objections to them. We then suggest practical applications of these tools to integrated assessment models of climate policy, and investigate what
difference they might make to policy recommendations.