It is not immediately clear how to discount distant-future events, like climate change, when the distant-future discount rate itself is uncertain. The so-called “Weitzman-Gollier puzzle” is the fact that two seemingly symmetric and equally plausible ways
of dealing with uncertain future discount rates appear to give diametrically opposed results with the opposite policy implications. We explain how the “Weitzman-Gollier puzzle” is resolved. When agents optimize their consumption plans and probabilities
are adjusted for risk, the two approaches are identical. What we would wish a reader to take away from this paper is the bottom-line message that the appropriate long run discount rate declines over time toward its lowest possible value.
With climate change as prototype example, this paper analyzes the implications of structural uncertainty for the economics of low probability, high-impact catastrophes. Even when updated by Bayesian learning, uncertain structural parameters induce a critical “tail fattening” of posterior-predictive distributions. Such fattened tails have strong implications for situations, like climate change, where a catastrophe is theoretically possible because prior knowledge cannot place sufficiently narrow bounds on overall damages. This paper shows that the economic consequences of fat-tailed structural uncertainty (along with unsureness about high-temperature damages) can readily outweigh the effects of discounting in climate-change policy analysis.