Chapter 1: The Theory of Everything
I remember being told, when I was a small child, that in ancient times it was still possible for a very learned person to know everything that was known. I was also told that nowadays so much is known that it is inconceivable that anyone could learn more than a tiny fraction of it in a long lifetime. The latter proposition surprised and disappointed me. In fact, I refused to believe it. I did not know how to justify my disbelief. But I knew that I did not want things to be like that, and I envied the ancient scholars.
It was not that I wanted to memorise all the facts that were listed in the world’s encyclopaedias and telephone directories. On the contrary, I hated memorising facts. That is not the sense in which I expected it to be possible to know everything that was known. It would not have disappointed me to be told that more publications appear every day than anyone could read in a lifetime, or that there are 600,000 known species of beetle. I had no wish to track the fall of every sparrow. Nor did I imagine that an ancient scholar who supposedly knew everything that was known would have known everything of that sort. I had in mind a more discriminating idea of what should count as being known. By “known” I meant understood.
The idea that one person might understand everything that is understood may still seem fantastic, but it is distinctly less so than the idea that one person could memorise every known fact. For example, no one could possibly memorise all known observational data on even so narrow a subject as the motions of the planets, but many astrophysicists understand those motions to the full extent that they are understood. This is possible because understanding does not depend on knowing a lot of facts as such, but on having the right concepts, explanations and theories. One comparatively simple and comprehensible theory can cover an infinity of indigestible facts. Our best theory of planetary motions is Einstein’s general theory of relativity, which, in the early twentieth century, superseded Newton’s theories of gravity and motion. It correctly predicts, in principle, not only all planetary motions but also all other effects of gravity to the limits of accuracy of our best measurements. For a theory to predict something “in principle” means that as a matter of logic the predictions follow from the theory, even if in practice the amount of computation that would be needed to generate some of the predictions is too large to be technologically feasible, or even too large to be physically possible in the universe as we find it.
Being able to predict things, or to describe them, however accurately, is not at all the same thing as understanding them. Predictions and descriptions in physics are often expressed as mathematical formulae. Suppose that I memorise the formula from which I could, if I had the time and inclination, calculate any planetary position that has been recorded in the astronomical archives. What exactly have I gained, compared with memorising those archives directly? The formula is easier to remember – but then, looking a number up in the archives may be even easier than calculating it from the formula. The real advantage of the formula is that it can be used in an infinity of cases beyond the archived data, for instance to predict the results of future observations. It may also state the historical positions of the planets more accurately, because the archives contain observational errors. Yet, even though the formula summarises infinitely more facts than the archives do, it expresses no more understanding of the motions of the planets. Facts cannot be understood just by being summarised in a formula, any more than by being listed on paper or memorised in a brain. They can be understood only by being explained. Fortunately, our best theories contain deep explanations as well as accurate predictions. For example, the general theory of relativity explains gravity in terms of a new, four-dimensional geometry of curved space and time. It explains how, precisely and in complete generality, this geometry affects and is affected by matter. That explanation is the entire content of the theory. Predictions about planetary motions are merely some of the consequences that we can deduce from the explanation.
Moreover, what makes the general theory of relativity so important is not that it can predict planetary motions a shade more accurately than Newton’s theory can. It is that it reveals and explains previously unsuspected aspects of reality, such as the curvature of space and time. This is typical of scientific explanation. Scientific theories explain the objects and phenomena of our experience in terms of an underlying reality which we do not experience directly. But the ability of a theory to explain what we experienceis not its most valuable attribute. Its most valuable attribute is that it explains the fabric of reality itself. As we shall see, one of the most valuable, significant and also usefulattributes of human thought generally, is its ability to reveal and explain the fabric of reality.
Yet some philosophers, and even some scientists, disparage the role of explanation in science. To them, the basic purpose of a scientific theory is not to explain anything, but to predict the outcomes of experiments: its entire content lies in its predictive formulae. They consider any consistent explanation that a theory may give for its predictions to be as good as any other, or as good as no explanation at all, so long as the predictions are true. This view is called instrumentalism (because it says that a theory is no more than an “instrument” for making predictions). To instrumentalists, the idea that science can enable us to understand the underlying reality that accounts for our observations, is a fallacy and a conceit. They do not see how anything that a scientific theory may say beyond predicting the outcomes of experiments can be more than empty words. Explanations, in particular, they regard as mere psychological props: a sort of fiction which we incorporate in theories to make them more memorable and entertaining. The Nobel prize-winning physicist Steven Weinberg was in an instrumentalist mood when he made the following extraordinary comment about Einstein’s explanation of gravity:
“The important thing is to be able to make predictions about images on the astronomers’ photographic plates, frequencies of spectral lines, and so on, and it simply doesn’t matter whether we ascribe these predictions to the physical effects of gravitational fields on the motion of planets and photons [as in pre-Einsteinian physics] or to a curvature of space and time.” (Gravitation and Cosmology p147).
Weinberg and the other instrumentalists are mistaken. It does matter what we ascribe the images on astronomers’ photographic plates to. And it matters not only to theoretical physicists like myself, whose very motivation for formulating and studying theories is the desire to understand the world better. (I am sure that this is Weinberg’s motivation too: he is not really driven by an urge to predict images and spectra!) For even in purely practical applications, the explanatory power of a theory is paramount, and its predictive power only supplementary. If this seems surprising, imagine that an extraterrestrial scientist has visited the Earth and given us an ultra-high-technology “oracle” which can predict the outcome of any possible experiment but provides no explanations. According to the instrumentalists, once we had that oracle we should have no further use for scientific theories, except as a means of entertaining ourselves. But is that true? How would the oracle be used in practice? In some sense it would contain the knowledge necessary to build, say, an interstellar spaceship. But how exactly would that help us to build one? Or to build another oracle of the same kind? Or even a better mousetrap? The oracle only predicts the outcomes of experiments. Therefore, in order to use it at all, we must first know what experiments to ask it about. If we gave it the design of a spaceship, and the details of a proposed test flight, it could tell us how the spaceship would perform on such a flight. But it could not design the spaceship for us in the first place. And if it predicted that the spaceship we had designed would explode on takeoff, it could not tell us how to prevent such an explosion. That would still be for us to work out. And before we could work it out, before we could even begin to improve the design in any way, we should have to understand, among other things, how the spaceship was supposed to work. Only then could we have any chance of discovering what might cause an explosion on takeoff. Prediction – even perfect, universal prediction – is simply no substitute for explanation.
Similarly, in scientific research, the oracle would not provide us with any new theory. Once we already had a theory, and had thought of a possible experimental test, thenwe could ask the oracle what would happen if the theory were subjected to that test. Thus, the oracle would not be replacing theories at all. It would be replacing experiments. It would spare us the expense of running laboratories and particle accelerators. Instead of building prototype spaceships, and risking the lives of test pilots, we could do all the testing on the ground with pilots sitting in flight simulators whose behaviour was controlled by the predictions of the oracle.
The oracle would be very useful in many situations, but its usefulness would always depend on people’s ability to solve scientific problems in just the way they have to now, namely by devising explanatory theories. It would not even replace all experimentation, because its ability to predict the outcome of a particular experiment in practice would depend on how easy it was to describe the experiment accurately enough for the oracle to give a useful answer, compared with doing the experiment in reality. After all, the oracle would have to have some sort of “user interface”. Perhaps a description of the experiment would have to be typed in, in some standard language. In that language, some experiments would be harder to specify than others. In practice, for many experiments, the specification would be too complex to be typed in. Thus the oracle would have the same general advantages and disadvantages as any other source of experimental data, and it would be useful only in cases where consulting it happened to be more convenient than using other sources. To put that another way: there already is one such oracle out there, namely the physical world. It tells us the result of any possible experiment if we ask it in the right language (i.e. if we do the experiment), though in some cases it is impractical for us to “type in” the experimental conditions in the required form (i.e. to build and operate the apparatus). But it provides no explanations.
In a few applications, for instance weather forecasting, we may be almost as satisfied with a purely predictive oracle as with an explanatory theory. But even then, that would be strictly so only if the oracle’s weather forecast were complete and perfect. In practice, weather forecasts are necessarily incomplete and imperfect, and to make up for that, they include explanations of how the forecasters arrived at their predictions. The explanations allow us to judge the reliability of the forecast, and to deduce further predictions relevant to our own location and needs. For instance, it makes a difference to me whether today’s forecast that it will be windy tomorrow is based on an expectation of a nearby high pressure area, or of a more distant hurricane. I should take more precautions in the latter case. Meteorologists themselves also need explanatory theories about weather, so that they can guess what approximations it is safe to incorporate in their computer simulations of the weather, what additional observations would allow the forecast to be more accurate and more timely, and so on.
Thus the instrumentalist ideal that is epitomised by our imaginary oracle, namely a scientific theory stripped of its explanatory content, would be of strictly limited utility. Let us be thankful that real scientific theories do not resemble that ideal, and that scientists in reality do not work towards that ideal.
An extreme form of instrumentalism, called positivism (or “logical positivism”) holds that all statements other than those describing or predicting observations are not only superfluous but meaningless. Although this doctrine is itself meaningless, according to its own criterion, it was nevertheless the prevailing theory of scientific knowledge during the first half of the twentieth century! Even today, instrumentalist and positivist ideas still have currency. One reason why they are superficially plausible is that although prediction is not the purpose of science, it is part of the characteristic method of science. The scientific method involves postulating a new theory to explain some class of phenomena and then performing a crucial experimental test, i.e. an experiment for which the old theory predicts one observable outcome and the new theory another. One rejects the theory whose predictions turned out to be false. Thus the outcome of a crucial experimental test to decide between two theories does depend on the theories’ predictions, and not directly on their explanations. This is the source of the misconception that there is nothing more to a scientific theory than its predictions. But experimental testing is by no means the only process involved in the growth of scientific knowledge. The overwhelming majority of theories are rejected for containing bad explanations, not for failing experimental tests. We reject them without ever bothering to test them. For example, consider the theory that eating a kilogram of grass is a cure for the common cold. That theory makes experimentally testable predictions: if people tried the grass cure and found it ineffective, the theory would be proved false. But it has never been tested and probably never will be, because it contains no explanation, either of how the cure would work, or of anything else. We rightly presume that it is false. There are always infinitely many possible theories of that sort – compatible with existing observations and making new predictions – so we could never have the time or resources to test them all. What we test are theories that seem to show promise of explaining things better than the prevailing theories do.
To say that prediction is the purpose of a scientific theory is to confuse means with ends. It is like saying that the purpose of a spaceship is to burn fuel. In fact, burning fuel is only one among many things that a spaceship has to do to accomplish its real purpose, which is to transport its payload from one point in space to another. Passing experimental tests is only one of many things that a theory has to do to achieve the real purpose of science, which is to explain the world.
As I have said, explanations are inevitably framed partly in terms of things that we do not directly observe: atoms and forces; the interiors of stars and the rotation of galaxies; the past and the future; the laws of nature. The deeper an explanation is, the more remote from immediate experience are the entities that it must refer to. But these entities are not fictional. On the contrary, they are part of the very fabric of reality.
(Buy the book to continue reading.)
Extracts of The Fabric of Reality copyright © David Deutsch, 1996. Reproduced by permission.