Knocking on Heaven's Door (31 page)

This isn’t to say that scientists don’t have a big responsibility. We always need to ensure that scientists are responsible and are attentive to risks. We’d like to be as certain with respect to all scientific enterprises as we were with the LHC. If you are creating matter or microbes or anything else that has not existed before (or drilling deeper or otherwise exploring new frontiers on the Earth for that matter), you need to be certain of not doing anything dramatically bad. The key is to do this rationally, without unfounded fearmongering that would impede progress and benefits. This is true not just for science but for any potentially risky endeavor. The only answer to imagined unknowns and even to “unknown unknowns” is to heed as many reasonable viewpoints as possible and to have the freedom to intervene if necessary. As anyone in the Gulf of Mexico will attest to, you need to be able to turn off the spigots if something goes wrong.

Early in the previous chapter, I summarized some of the objections that bloggers and skeptics made about the methods physicists used for black hole calculations, including relying on quantum mechanics. Hawking did indeed use quantum mechanics to derive black hole decay. Yet despite Feynman’s statement that “no one understands quantum mechanics,” physicists understand its implications, even if we don’t have a deep philosophical insight into why quantum mechanics is true. We believe quantum mechanics because it explains data and solves problems that are impenetrable with classical physics.

When physicists debate quantum mechanics, they don’t dispute its predictions. Its repeated success has forced generations of astonished students and researchers to accept the theory’s legitimacy. Debates today about quantum mechanics concern its philosophical underpinnings. Is there some other theory with more familiar classical premises that nonetheless predicts the bizarre hypotheses of quantum mechanics? Even if people make progress on such issues, it would make no difference to quantum mechanical predictions. Philosophical advances could affect the conceptual framework we use to describe predictions—but not the predictions themselves.

For the record, I find major advances on this front unlikely. Quantum mechanics is probably a fundamental theory. It is richer than classical mechanics. All classical predictions are a limiting case of quantum mechanics, but not vice versa. So it’s hard to believe that we will ultimately interpret quantum mechanics with classical Newtonian logic. Trying to interpret quantum mechanics in terms of classical underpinnings would be like me trying to write this book in Italian. Anything I can say in Italian I can say in English, but because of my limited Italian vocabulary the reverse is far from true.

Still, with or without agreement on philosophical import, all physicists agree on how to apply quantum mechanics. The wacky naysayers are just that. Quantum mechanical predictions are trustworthy and have been tested many times. Even without them, we still have alternative experimental evidence (in the form of the Earth and Sun and neutron stars and white dwarfs) that the LHC is safe.

LHC alarmists also objected to the purported use of string theory. Indeed, using quantum mechanics was just fine but relying on string theory would not have been. But the conclusions about black holes never needed string theory anyway. People do try to use string theory to understand the interior of black holes—the geometry of the apparent singularity at the center where according to general relativity energy becomes infinitely dense. And people have done string-theory-based calculations of black hole evaporation in nonphysical situations that support Hawking’s result. But the computation of black hole decay relies on quantum mechanics and not on a complete theory of quantum gravity. Even without string theory, Hawking could do his calculations. The very questions some bloggers posed reflected the absence of sufficient scientific understanding to weigh the facts.

A more generous interpretation of this objection is as resistance not to the science itself but to scientists with “faith-based” beliefs in their theories. After all, string theory is beyond the experimentally verifiable regime of energies. Yet many physicists think it’s right and continue to work on it. However, the variety of opinions about string theory—even within the scientific community—nicely illustrates just the opposite point. No one would base any safety assessment on string theory. Some physicists support string theory and some do not. Yet everyone knows it is not yet proven or fully fleshed out. Until everyone agreed on string theory’s validity and reliability, trusting string theory for risky situations would be foolhardy. As concerns our safety, the inaccessibility of string theory’s experimental consequences is not the only reason that we don’t yet know if it’s correct—it’s also the reason it isn’t required to predict most real-world phenomena we will encounter in our lifetimes.

Yet despite my confidence that it was okay to rely on experts when evaluating potential risks from the LHC, I recognize the potential limitations of this strategy and don’t quite know how to address them. After all, “experts” told us that derivatives were a way of minimizing risk, not creating potential crises. “Expert” economists told us that deregulation was essential to the competitiveness of American business, not to the potential downfall of the American economy. And “experts” tell us only those in the banking sector understand their transactions sufficiently well to address its woes. How do we know when experts are thinking broadly enough?

Clearly experts can be shortsighted. And experts can have conflicts of interest. Are there any lessons from science here?

I don’t think it is my bias that leads me to say that in the case of LHC black holes, we examined the full range of potential risks that we could logically envision. We thought about both the theoretical arguments and also the experimental evidence. We thought about situations in the cosmos where the same physical conditions applied, yet did not destroy any nearby structure.

It would be nice to be so sanguine that economists do similar comparisons to existing data. But the title of Carmen Reinhart and Kenneth Rogoff’s book
This Time Is Different
suggests otherwise. Although economic conditions are never identical, some broad measures do indeed repeat themselves in economic bubbles.

The argument made by many today that no one could anticipate the dangers of deregulation also doesn’t stand up. Brooksley Born, the former chairperson of the Commodity Futures Trading Commission, which oversees futures and commodity options markets, did point out the dangers of deregulation—actually she rather reasonably suggested that potential risks be explored—but she was shouted down. There was no solid analysis of whether caution was justified (as it clearly turned out to be) but only a partisan view that moving slowly would be bad for business (as it would have been for Wall Street in the short term).

Economists speaking out about regulation and policy might have a political as well as a financial agenda and that can interfere with doing the right thing. Ideally, scientists pay more attention to the merits of arguments, including those regarding risk, than politics. LHC physicists made serious scientific inquiries to ensure no disasters would occur.

Although perhaps only financial experts understand the details of a particular financial instrument, anyone can consider some basic structural issues. Most people can understand why an overly leveraged economy is unstable, even without predicting or even understanding the precise trigger that might cause a collapse. And most anyone can understand that giving the banks hundreds of billions of dollars with few or no constraints is probably not the best way to spend taxpayers’ money. And even a faucet is built with a reliable means of turning it off—or at least a mop and plan in place to clean up any mess. It’s hard to see why the same shouldn’t apply to deep-sea oil rigs.

Psychological factors enter when we count on experts, as the
New York Times
economics columnist David Leonhardt explained in 2010 when attributing Mr. Greenspan’s and Mr. Bernanke’s errors to factors that were “more psychological than economic.” He explained, “They got trapped in an echo chamber of conventional wisdom” and “fell victim to the same weakness that bedeviled the engineers of the Challenger space shuttle, the planners of the Vietnam and Iraq wars, and the airline pilots who have made tragic cockpit errors. They didn’t adequately question their own assumptions. It’s an entirely human mistake.”
⁴⁸

The only way to address complicated issues is to listen broadly, even to the outliers. Despite their ability to predict that the economy could collapse into a black hole, self-interested bankers were content to ignore warnings so long as they could. Science is not democratic in the sense that we all get together and vote on the right answer. But if anyone has a valid scientific point, it will ultimately be heard. People will often pay attention to the discoveries and insights from more prominent scientists first. Nonetheless, an unknown who makes a good point will eventually gain an audience.

With the ear of a well-known scientist, an unknown might even be listened to right away. That is how Einstein could present a theory that shook scientific foundations almost immediately. The German physicist Max Planck understood the implications of Einstein’s relativistic insights and was fortuitously in charge of the most important physics journal at the time.

Today we benefit from the rapid spread of ideas over the Internet. Any physicist can write a paper and have it sent out through the physics archive the next day. When Luboš Motl was an undergraduate in the Czech Republic, he solved a scientific problem that a prominent scientist at Rutgers was working on. Tom Banks paid attention to good ideas, even if they came from an institution he had never heard of before. Not everyone is so receptive. But so long as a few people pay attention, an idea, if good and correct, will ultimately enter scientific discourse.

LHC engineers and physicists sacrificed time and money for safety. They wanted to economize as much as possible, but not at the expense of danger or inaccuracy. Everyone’s interests were aligned. No one benefits from a result that doesn’t stand the test of time.

The currency in science is reputation. There are no golden parachutes.

FORECASTING

I hope we all now agree that we shouldn‘t be worrying about black holes—though we do have much else to worry about. In the case of the LHC, we are and should be thinking about all the good things it can do. The particles created there will help us answer deep and fundamental questions about the underlying structure of matter.

To briefly return to my conversation with Nate Silver, I realized how special our situation is. In particle physics, we can restrict ourselves to simple enough systems to exploit the methodical manner in which new results build on old ones. Our predictions sometimes originate in models we know to be correct based on existing evidence. In other cases, we make predictions based on models we have reasons to believe might exist and use experiments to winnow down the possibilities. Even then—without yet knowing if these models will prove correct—we can anticipate what the experimental evidence would be, should the idea turn out to be realized in the world.

Particle physicists exploit our ability to separate according to scale. We know small-scale interactions can be very different from those that occur on large scales, but they nonetheless feed into large-scale interactions in a well-defined way, giving consistency with what we already know.

Forecasting is very different in almost all other cases. For complex systems, we often have to simultaneously address a range of scales. That can be true not only for social organizations, such as a bank in which an irresponsible trader could destabilize AIG and the economy, but even in other sciences. Predictions in those cases can have a great deal of variability.

For example, the goals of biology include predicting biological patterns and even animal and human behavior. But we don’t yet fully understand all the basic functional units or the higher-level organization by which elementary elements produce complex effects. We also don’t know all the feedback loops that threaten to make separating interactions by scale impossible. Scientists can make models, but without better understanding the critical underlying elements or how they contribute to emergent behavior, modelers face a quagmire of data and competing possibilities.

A further challenge is that biological models are designed to match preexisting data, but we don’t yet know the rules. We haven’t identified all the simple independent systems, so it is difficult to know which—if any—model is right. When I spoke with my neuroscientist colleagues, they described the same problem. Without qualitatively new measurements, the best that the models can do is to match all existing data. Since all the surviving models must agree with the data, it is difficult to decisively determine which underlying hypothesis is correct.

It was interesting to talk to Nate about the kind of things he tries to predict. A lot of recent popular books present shaky hypotheses that give predictions that work—except when they don’t. Nate is a lot more scientific. He first became famous for his accurate predictions of baseball games and elections. His analysis was based on careful statistical evaluations of similar situations in the past, where he included as many variables that he could manage to apply historical lessons to as precisely as he could.

He now has to choose wisely where to apply his methods. But he realizes that the kinds of correlations he focuses on can be tricky to interpret. You can say an engine on fire caused a plane crash, but it’s not a surprise to find an engine on fire in a plane going down. What really was the initial cause? You have the same issue when you connect a mutated gene to cancer. It doesn’t necessarily cause the disease even if it is correlated with it.

He is aware of other potential pifalls too. Even with large amounts of data, randomness and noise may enhance or suppress the interesting underlying signals. So Nate won’t work on financial markets or earthquakes or climate. Although in all likelihood he could predict overall trends, the short-term predictions would be inherently uncertain. Nate now studies other places where his methods shed light such as how best to distribute music and movies, as well as questions such as the value of NBA superstars. But he acknowledges that only very few systems can be so accurately quantified.