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Farber on Uncertainty & Risk

By Lawrence Solum

Daniel A. Farber (University of California, Berkeley - School of Law) has posted Uncertainty on SSRN. Here is the abstract:

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 ?-maxmin.

And from the paper:

These models are sometimes called ?-maxmin models, with ? representing the weighting factor between best and worst cases.120 One way to understand these models is that we might want to minimize our regret if we make the wrong decision, where we regret disastrous outcomes that lead to the worst-case scenario but we also regret having missed the opportunity to achieve the best-case scenario. Alternatively, ? can be a measure of the balance between our hopes (for the best case) and our fears (of the worst case).121

Applying these ?-maxmin models as a guide to action leads to what we might call the ?-precautionary principle. Unlike most formulations of the precautionary principle, ?-precaution is not only aimed at avoiding the worst-case scenario; it also involves precautions against losing the possible benefits of the best-case scenario.122 In some situations discussed in this article, the best-case scenario is more or less neutral, so that ?- precaution is not much different than pure loss avoidance unless the decision maker is very optimistic and uses an especially low ?. But where the best-case scenario is potentially extremely beneficial, unless the decision maker?s ? is very high, ?-precaution will suggest a more neutral attitude toward uncertainty in order to take advantage of potential upside gains.

Highly recommended. Download it while its hot!

Full post as published by Legal Theory Blog on February 22, 2010 (boomark / email).