Knocking on Heaven's Door - Lisa Randall [91]
CALCULATING RISK
Ideally, one of the first steps would be to calculate risks. Sometimes people simply get the probabilities wrong. When John Oliver interviewed Walter Wagner, one of the LHC litigants, about black holes on The Daily Show, Wagner forfeited any credibility he might have had when he said the chance of the LHC destroying the Earth was 50—50 since it either will happen or it won’t. John Oliver incredulously responded that he “wasn’t sure that’s how probability works.” Happily, John Oliver is correct, and we can make better (and less egalitarian) probability estimates.
But it’s not always easy. Consider the probability of detrimental climate change—or the probability of a bad situation in the Middle East, or the fate of the economy. These are much more complex situations. It’s not merely that the equations that describe the risks are difficult to solve. It’s that we don’t even necessarily know what the equations are. For climate change, we can do simulations and study the historical record. For the other two, we can try to find analogous historical situations, or make simplified models. But in all three cases, huge uncertainties plague any predictions.
Accurate and trustworthy predictions are difficult. Even when people do their best to model everything relevant, the inputs and assumptions that enter any particular model might significantly affect a conclusion. A prediction of low risk is meaningless if the uncertainties associated with the underlying assumptions are much greater. It’s critical to be thorough and straightforward about uncertainties if a prediction is to have any value.
Before considering other examples, let me recount a small anecdote that illustrates the problem. Early in my physics career, I observed that the Standard Model allowed for a much wider range of values for a particular quantity of interest than had been previously predicted, due to a quantum mechanical contribution whose size depended on the (then) recently measured and surprisingly large value of the top quark mass. When presenting my result at a conference, I was asked to plot my new prediction as a function of top quark mass. I refused, knowing there were several different contributions and the remaining uncertainties allowed for too broad a range of possibilities to permit such a simple curve. However, an “expert” colleague underestimated the uncertainties and made such a plot (not unlike many real-world predictions made today), and—for a while—his prediction was widely referenced. Eventually, when the measured quantity didn’t fall within his predicted range, the disagreement was correctly attributed to his overly optimistic uncertainty estimate. Clearly, it’s better to avoid such embarrassments, both in science and in any real-world situation. We want predictions to be meaningful, and they will be only if we are careful about the uncertainties that we enter.
Real-world situations present even more intractable problems, requiring us to be still more careful about uncertainties and unknowns. We have to be cautious about the utility of quantitative predictions that cannot or do not take account of these issues.
One stumbling block is how to properly account for systemic risks, which are almost always difficult to quantify. In any big interconnected system, the large-scale elements involving the multiple failure models arising from the many interconnections of the smaller pieces are often the least supervised. Information can be lost in transitions or never attended to in the first place. And such systemic problems can amplify the consequences of any other potential risks.
I saw this kind of structural issue firsthand when I was on a committee addressing NASA safety. To accommodate the necessity of appeasing