From the economist Martin L. Weitzman‘s website, a new draft paper On Modeling and Interpreting the Economics of Catastrophic Climate Change(pdf). He proposes a method for including unlikely but extreme events (fat tails) in cost-benefit analyses, such as the uncertainty surrounding climate sensitivity. Considering the possibility of such events can completely change the results of an analysis, and favour action as a type of catastrophe insurance.
Abstract: Using climate change as a prototype example, this paper analyzes the implications of structural uncertainty for the economics of low-probability high-impact catastrophes. The paper is an application of the idea that having an uncertain multiplicative parameter, which scales or amplifes exogenous shocks and is updated by Bayesian learning, induces a critical tail fattening of posterior-predictive distributions. These fattened tails can have very strong implications for situations (like climate change) where a catastrophe is theoretically possible because prior knowledge cannot place sufficiently narrow bounds on overall damages. The essence of the problem is the difficulty of learning extreme-impact tail behavior from finite data alone. At least potentially, the ináuence on cost-benefit analysis of fat-tailed uncertainty about climate change, coupled with extreme unsureness about high-temperature damages, can outweigh the influence of discounting or anything else.
The paper concludes:
In principle, what might be called the catastrophe-insurance aspect of such a fat-tailed unlimited-exposure situation, which can never be fully learned away, can dominate the social-discounting aspect, the pure-risk aspect, or the consumption-smoothing aspect. Even if this principle in and of itself does not provide an easy answer to questions about how much catastrophe insurance to buy (or even an easy answer in practical terms to the question of what exactly is catastrophe insurance buying for climate change or other applications), I believe it still might provide a useful way of framing the economic analysis of catastrophes.