The “α-Precautionary Principle”: Weighing Optimism against Pessimism

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Law professor proposes a new tool to analyze potentially catastrophic uncertainties.

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Regulators constantly face threats that are ripe with uncertainty — or non-quantifiable likelihood of danger. The analytical problem becomes especially acute in situations where there exists the possibility of outcomes much more extreme than is usually the case, which economists refer to as a “fat tail.”

According to a new article by law professor Daniel Farber of the University of California, Berkeley, existing analytical tools such as risk assessment and the precautionary principle are inadequate in the face of extreme outcomes. He argues that in analyzing uncertainty in the cases of “fat tails” regulators should use “the α-precautionary principle.” The α-precautionary principle resides, he says, in “a middle space between conventional versions of risk assessment and the precautionary principle.” It considers both the worst case and best case scenarios and produces a practical framework for estimating the degree of appropriate precaution.

According to Farber, fat tails increase uncertainty since they make it harder for analysts to measure the distribution’s tail.  In analyzing these fat-tailed distributions, the α-precautionary principle directs decision makers to apply “α maxmin models,” where α represents the weighted average of the best and worst case scenarios.  The weight placed on each scenario reflects the decision maker’s optimism and pessimism about it.

Simply put, the α-precautionary tool poses the following three questions, in Farber words:

(1) What is the best-case outcome that is plausible enough to be worth considering?


(2) What is the worst-case scenario that is worth considering?


(3) How optimistic or pessimistic should we be in balancing these possibilities?

Farber argues that the α-precautionary principle makes decision making more transparent by enabling people other than technical experts to make significant value judgments. He explains that the tool is simple enough for policymakers and the public to understand, yet complex enough to account for the extreme outcomes of a given uncertainty without exact probability information.

Farber also argues that employing this tool can help coordinate government policy by having an outside agency, like the Office of Management and Budget (OMB), provide benchmark values for α and ensure that an agency’s weight determinations are “consistent with administrative policy.”

To illustrate how this approach works, Farber applies the α-precautionary principle to two prominent policy issues and arrives at opposite conclusions. The analysis “suggests a highly precautionary approach to the uncertainties surrounding climate change but a less precautionary approach to the uncertainties of nanotechnology.”

In his climate change analysis, Farber defines the worst-case scenario as the end of civilization and the best-case scenario as a modest climate change impact.  He suggests that decision makers assign hardly any weight to the worst-case scenario, as they are very optimistic about avoiding the end of civilization, and add great weight to the best-case scenario, as they are very optimistic about climate change having only a modest impact.

Farber then uses the equation: αHw + (1—α)Hb, where Hw and Hb are the harm in the worst and best case scenarios, respectively, and α is the “weighting factor between the best and worst cases.” Farber assumes that, for climate change, the worst-case scenario translates into monetary terms as a collapse of world GDP, which equates to about $1,000 trillion dollars. Farber also assumes that in the best-case scenario climate change would yield about $1 trillion dollars. In accordance with the scenario posed above, Farber gives “α” a value of 0.01, meaning the best-case scenario receives 99 times more weight than the worst-case scenario. In this situation, the world would still face a $9 trillion dollar loss.

Farber explains that when these scenarios are averaged, the potential harm of the worst-case scenario, even without much weight, is so severe that it outweighs even the most heavily weighed best-case scenario. As Farber states, “even if we are highly optimistic about the best-case scenario, a serious investment in climate mitigation would still be warranted if the downside risk is as severe as [experts] suggest.” Thus, Farber argues that the α-precautionary principle justifies climate policy with “a high degree of precaution to avoid the negative uncertainties.”

In applying the α-precautionary principle to nanotechnology regulation, Farber explains that in light of nanotechnology’s large upside and downside, the α-precautionary principle would not support the delay of research and development in this area. Authorities currently regulate nano-materials as existing materials rather than as new materials, which are subject to more stringent regulation. Farber advocates a middle path, in which nanotechnology research focuses on potential public and environmental health effects, until further risk information becomes available.

Farber concludes that “[j]ust because you do not know exactly how big a number is, there is no reason to assume it to be zero.”  By accounting for the best and worst case scenarios, he believes the α-precautionary principle enables decision makers to analyze better the uncertainties associated with potential catastrophic consequences and thus make better, more informed policy decisions.