Maximin

48 Pages Posted: 29 Oct 2019 Last revised: 5 May 2020

See all articles by Cass R. Sunstein

Cass R. Sunstein

Harvard Law School; Harvard University - Harvard Kennedy School (HKS)

Date Written: October 19, 2019

Abstract

For regulation, some people argue in favor of the maximin rule, by which public officials seek to eliminate the worst worst-cases. The maximin rule has not played a formal role in regulatory policy in the Unites States, but in the context of climate change, pandemics, or new and emerging technologies, regulators who are unable to conduct standard cost-benefit analysis might be drawn to it. In general, the maximin rule is not a good idea for regulatory policy, because it is likely to reduce rather than to increase well-being. But under four imaginable conditions, that rule is attractive. (1) The worst-cases are very bad, and not improbable, so that it may make sense to eliminate them under conventional cost-benefit analysis. (2) The worst-case outcomes are highly improbable, but they are so bad that even in terms of expected value, it may make sense to eliminate them under conventional cost-benefit analysis. (3) In circumstances of Knightian uncertainty, where observers (including regulators) cannot assign probabilities to imaginable outcomes, the maximin rule may make sense. (4) The probability distributions may include “fat tails,” in which very bad outcomes are more probable than is usual; it may make sense to eliminate those outcomes for that reason. With respect to (3) and (4), the challenges arise when eliminating dangers also threatens to impose very high costs or to eliminate very large gains. There are also reasons to be cautious about imposing regulation when technology offers the promise of “moonshots,” or “miracles,” offering a low probability or an uncertain probability of extraordinarily high payoffs. Miracles may present a mirror-image of worst-case scenarios.

Suggested Citation

Sunstein, Cass R., Maximin (October 19, 2019). Yale Journal on Regulation, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3476250 or http://dx.doi.org/10.2139/ssrn.3476250

Cass R. Sunstein (Contact Author)

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Harvard University - Harvard Kennedy School (HKS) ( email )

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