59 Pages Posted: 20 Feb 2010 Last revised: 16 Apr 2011
Date Written: February 18, 2010
Many of the pressing policy issues facing us today require confronting the unknown and making difficult choices in the face of limited information. Economists distinguish between “uncertainty” (where the likelihood of the peril is non-quantifiable) and “risk” (where the likelihood is quantifiable). Uncertainty is particularly pernicious in situations where catastrophic outcomes are possible, but conventional decision tools are not equipped to cope with these potentially disastrous results. This Article describes new analytic tools for assessing potential catastrophic outcomes and applies them to some key policy issues: controlling greenhouse gases, adapting to unavoidable climate change, regulating nanotechnology, dealing with long-lived nuclear wastes, and controlling financial instability.
More specifically, economic modeling and policy analysis are often based on the assumption that extreme harms are highly unlikely, in the technical sense that the “tail” of the probability distributions is “thin” – in other words, that it approaches rapidly to zero. Thin tails allow extreme risks to be given relatively little weight. A growing body of research, however, focuses on the possibility of fat tails, which are common in systems with feedback between different components. As it turns out, fat tails and uncertainty often go together. Economic theories of “ambiguity” deal at a more general level with situations where multiple plausible models of reality confront a decision maker. Ambiguity theories are useful in considering systems with fat tails and in other situations where the probabilities are simply difficult to quantify. The Article considers both the policy implications of fat tails and the use of ambiguity theories such as α-maxmin.
Suggested Citation: Suggested Citation
Farber, Daniel A., Uncertainty (February 18, 2010). Georgetown Law Journal, Vol. 99, p. 901, 2011; UC Berkeley Public Law Research Paper No. 1555343. Available at SSRN: https://ssrn.com/abstract=1555343 or http://dx.doi.org/10.2139/ssrn.1555343