Knocking on Heaven's Door (30 page)

Any risk assessment is plagued by the difficulty of evaluating the risk that the underlying assumptions are incorrect. Without such estimates, any estimate becomes subject to intrinsic prejudices. On top of the calculational problems and hidden prejudices buried in these underlying assumptions, many practical policy decisions involve unknown unknowns—factors that can’t be or haven’t been anticipated. Sometimes we simply can’t foresee the precise unlikely event that will cause trouble. This can make any prediction attempts—that will inevitably fail to factor in these unknowns—completely moot.

MITIGATING RISK

Luckily for our search for understanding, we are extremely certain that the probability of producing dangerous black holes is minuscule. We don’t know the precise numerical probability for a catastrophic outcome, but we don’t need to because it’s so negligible. Any event that won’t happen even once in the lifetime of the universe can be safely ignored.

More generally, however, quantifying an acceptable level of risk is extremely difficult. We clearly want to avoid major risks altogether—anything that endangers life, the planet, or anything we hold dear. With risks we can tolerate, we want a way of evaluating who benefits and who stands to lose, and to have a system that would evaluate and anticipate risks accordingly.

The risk analyst Joe Fragola’s comment to me about climate change, along with other potential dangers he is concerned with, was the following: “The real issue is not if these could happen, nor what their consequences would be, but rather what is their probability of occurrence and the associated uncertainty? And how much of our global resources should we allocate to address such risks based not only on the probability of occurrence but also on the probability that we might do something to mitigate them?”

Regulators often rely on so-called cost-benefit analysis to evaluate risk and determine how to deal with it. On the surface, the idea sounds simple enough. Calculate how much you need to pay versus the benefit and see if the proposed change is worth it. This might even be the best available procedure in many circumstances, but it might also dangerously generate a deceptive patina of mathematical rigor. In practice, cost-benefit analysis can be very hard to do. The problems involve not just measuring cost and benefit, which can be a challenge, but defining what we mean by cost and benefit in the first place. Many hypothetical situations involve too many unknowns to reliably calculate either, or to calculate risk in the first place. We can certainly try, but these uncertainties need to be accounted for—or at least recognized.

A sensible system that anticipates costs and risks in the near term and in the future would certainly be useful. But not all trade-offs can even be evaluated solely according to their cost. What if that which is at risk can’t be replaced at all?
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Had the creation of an Earth-eating black hole by the LHC been something that could happen with reasonable probability within our lifetime, or even within a million years, we certainly would have pulled the plug.

And even though we ultimately benefit quite a bit from research in basic science, the economic cost of abandoning a project is rarely calculable either, because the benefits are so difficult to quantify. The goals of the LHC include achieving fundamental knowledge, including a better understanding of masses and forces, and possibly even of the nature of space. The benefits also include an educated and motivated technically trained populace inspired by big questions and deep ideas about the universe and its composition. On a more practical front, we will follow the information advance CERN made with the World Wide Web, with the “grid” that will allow a global processing of information, as well as improvements in magnet technology that will be useful for medical devices such as MRIs. Possible further applications from fundamental science might ultimately be found, but these are almost always impossible to anticipate.

Cost-benefit analyses are difficult to apply to basic science. A lawyer jokingly applied a cost-benefit approach to the LHC, noting that along with the extremely tiny proposed enormous risk, the LHC also had a minuscule chance of stupendous benefits by solving all the problems of the world. Of course, neither outcome readily fits into a standard cost-benefit calculation, though—incredibly—lawyers have tried.
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At least science benefits from its goals being “eternal” truths. If you find the way the world works, it’s true no matter how quickly or slowly you found it. We certainly don’t want scientific progress to be slow. But the year’s delay showed us the danger of too quickly turning on the LHC. In general, scientists try to proceed safely.

Cost-benefit analysis is riddled with difficulty for almost any complex situation—such as climate change policy or banking. Although in principle a cost-benefit analysis makes sense and there may be no fundamental objection, how you apply it makes an enormous difference. Defenders of cost-benefit analysis essentially make a cost-benefit argument to justify the approach when they ask how can we possibly do better—and they might even be right. I’m simply advocating that where we do apply the method, we do it more scientifically. We need to be clear about the uncertainties in any numbers we present. As with any scientific analysis, we need to take errors, assumptions, and biases into account and be open in presenting these.

One factor that matters a great deal for climate change issues is whether the costs or benefits refer to an individual, a nation, or the globe. The potential costs or benefits can also cross these categories, but we don’t always take this into account. One reason that American politicians decided against the Kyoto Protocol was they concluded that the cost would have exceeded the benefit to Americans—American businesses in particular. However, such a calculation didn’t really factor in the long-term costs of instabilities across the globe or the benefits of a regulated environment where new businesses might prosper. Many economic analyses of the costs of climate change mitigation fail to account for the potential additional benefits to the economy through innovation or to stability through less reliance on foreign nations. Too many unknowns about how the world will change are involved.

These examples also raise the question of how to evaluate and mitigate risk that crosses national borders. Suppose black holes really had posed a risk to the planet. Could someone in Hawaii constructively sue an experiment planned for Geneva? According to existing laws, the answer is no, but perhaps a successful suit could have interfered with American financial contributions to the experiment.

Nuclear proliferation is another issue where clearly global stability is at stake. Yet we have limited control over the dangers generated in other nations. Both climate change and nuclear proliferation are issues that are managed nationally but whose dangers are not restricted to the institutions or nations creating the menace. The political problem of what to do when risks cross national boundaries or legal jurisdictions is difficult. But it’s clearly an important question.

As an institution that is truly international, CERN’s success hinges on the shared common goals of many nations. One nation can try to minimize its own contribution, but aside from that, no individual interests are at stake. All involved nations work together since the science they value is the same. The host countries, France and Switzerland, might receive slightly greater economic advantages in labor and infrastructure, but on the whole, it’s not a zero-sum game. No one nation benefits at the expense of another.

Another notable feature of the LHC is that CERN and the member states are responsible should any technical or practical problems occur. The 2008 helium explosion had to be repaired through CERN’s budget. No one, especially those working at the LHC, benefits from mechanical failure or scientific disasters. Cost-benefit analyses, when applied to situations where costs and benefits aren’t fully aligned and the benefactors don’t have full responsibility for the risk they take on, are less useful. It is very different from applying this type of reasoning to the types of closed systems that science tries to address.

In any situation, we want to avoid moral hazards, where people’s interest and risk are not aligned so they may have an incentive to take on greater risk than they would if no one else effectively contributed insurance. We need to have the right incentive structures.

Consider hedge funds, for example. The general partners get a percentage of profits from their fund each year when they make money, but they don’t forfeit a comparable percentage if their fund faces losses or if they go bankrupt. Individuals keep their gains, while their employers—or taxpayers—share the losses. With these parameters, the most profitable strategy for the employees would encourage large fluctuations and instabilities. An efficient system and effective cost-benefit analysis should take into account such allocation of risks, rewards, and responsibilities. They have to factor in the different categories or scales of the people involved.

Banking, too, has obvious moral hazards where risks and benefits aren’t necessarily aligned. A “too big to fail” policy combined with weak leveraging limits yields a situation in which the people who are accountable for losses (taxpayers) are not the same as those who stand to benefit the most (bankers or insurers). One can debate whether bailouts were essential in 2008, but preventing the situation in the first place by aligning risk with responsibility seems like a good idea.

Furthermore, at the LHC, all data about the experiments and risks are readily available. The safety report is on the web. Anyone can read it. Certainly any institution that would expect a bailout were it to fail, or even one that simply speculates in a potentially unstable fashion, should provide enough data to regulatory institutions so that the relative weight of benefits against risks can potentially be evaluated. Ready access to reliable data should help mortgage experts or regulators or others anticipate financial or other potential disasters in the future.

Though not in itself a solution, another factor that could at least improve or clarify the analyses would again be to take “scale”—in terms of categories of those subject to benefits and risks, as well as time ranges—into account. The question of scale translates into the issue of who is involved in a calculation: is it an individual, an organization, a government, or the world, and are we interested in a month, a year, or a decade? A policy that is good for Goldman Sachs might not ultimately benefit the economy as a whole—or the individual whose mortgage is currently under water. That means that even if there were perfectly accurate calculations, they would guarantee the right result only if they were applied to the correct carefully thought through question.

When we make policy or evaluate costs versus benefits, we tend to neglect the possible benefits of global stability and helping others—not just in a moral sense, but in the long-term financial sense as well. In part, this is because these gains are difficult to quantify, and in part it is due to the challenge in making evaluations and creating robust regulations in a world that changes quickly. Still, it’s clear that regulations that consider all possible benefits, not just those to an individual or an institution or a state, will be more reliable, and may even lead to a better world.

The time frame can also influence the computed cost or benefit for policy decisions as do the assumptions the deciding parties make, as we saw with the recent financial crisis. Time scales matter in other ways as well, since acting too hastily can increase risk while rapid transactions can enhance benefits (or profits). But even though fast trades can make pricing more efficient, lightning-fast transactions don’t necessarily benefit the overall economy. An investment banker explained to me how important it was to be able to sell shares at will, but even so he couldn’t explain why they needed to be able to sell them after owning them a few seconds or less—aside from the fact that he and his bank make more money. Such trades create more profits for bankers and their institutions in the short term, but they aggravate existing weaknesses in the financial sector in the long-term. Perhaps even with a short-term competitive disadvantage, a system that inspires more confidence could be more profitable in the long term and therefore prevail. Of course, the banker I mentioned made $2 billion for his institution in a single year, so his employers might not agree on the wisdom of my suggestion. But anyone who ultimately pays for this profit might.

THE ROLE OF EXPERTS

Many people take away the wrong lesson and conclude that the absence of reliable predictions implies an absence of risk. In fact, quite the opposite applies. Until we can definitively rule out particular assumptions or methods, the range of possible outcomes is within the realm of possibility. Despite the uncertainties—or perhaps because of them—with so many models predicting dangerous results, the probability of something very bad happening with climate or with the economy—or with offshore drilling—is not negligibly small. Perhaps one can argue that the chances are small within a definite time frame. However, in the long run, until we have better information, too many scenarios lead to calamitous results to ignore the dangers.

People interested only in the bottom line rally against regulation while those who are interested in safety and predictability argue for it. It is too easy to be tempted to come down on one side or the other, since figuring out where to draw the line is a daunting—if not an impossible—task. As with calculating risk, not knowing the deciding point doesn’t mean there is none or that we shouldn’t aim for the best approximation. Even without the insights necessary to make detailed predictions, structural problems should be addressed.

This brings us to the last important question: Who decides? What is the role of experts, and who gets to evaluate riskiness?

Given the money and bureaucracy and careful oversight involved in the LHC, we can expect that risks were adequately analyzed. Furthermore, at its energies, we aren’t even really in a new regime where the basic underpinnings of particle physics should fail. Physicists are confident the LHC is safe, and we look forward to the results from particle collisions.