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Richard Feynman once remarked that unless one is able to make one's ideas understandable to college freshmen, one doesn't really understand them. On the other hand, when asked by a reporter to explain why he was awarded the Nobel Prize, Feynman remarked, "Listen buddy, if I could explain it in fifty words or less, it wouldn't be worth a Nobel Prize."
There are two truths here: (1) Important ideas can be made accessible without dumbing them down; (2) The details of a scientific theory are important and typically inaccessible except to individuals with the requisite training. The hallmark of good science writing—the Feynman Test, let's call it—is the ability to heed both of these truths at the same time.
Yair Guttmann's project centers on a question of fundamental interest, growing out of the increasing use of probabilistic reasoning in physics over the last 150 years: Why do statistical methods work so well at predicting the behavior of huge numbers of particles enclosed in containers even though the physics that describes the individual behaviors of those particles is purely deterministic? Hence, it is an ideal subject for a book that seeks to make important scientific and philosophical issues accessible without garbling essential technical details. Alas, Guttmann fails the Feynman Test, but his failure is instructive.
Guttmann observes that a "philosophical temperament" is needed for a book such as this "on the nature of probabilities in statistical mechanics. … For most physicists, the topic is too 'academic.'" Guttmann himself champions what he calls the "pragmatist" approach to statistical mechanics (statistical mechanics explains the macroscopic properties of a physical system by a probabilistic description of its constituent particles). Essentially, the pragmatist approach says that we use probabilities in statistical mechanics because they work—they give us a successful theory. As Guttmann puts it in chapter 5:
Pragmatists believe that we have a total freedom to choose the concepts that appear in our scientific theories. We do not have to justify the initial choice of concepts at all [here statistical/probabilistic concepts]. All we need is to make sure that the theories we construct will imply correct predictions.
Well, I'm certainly sympathetic to pragmatism in science. "The scientific method, as far as it is a method," the Nobel laureate Percy Bridgman said, "is nothing more than doing one's damndest with one's mind, no holds barred." This account of pragmatism is perfectly fine diet for scientists, whose livelihood depends on getting results. But it's rather thin gruel for philosophers, whose livelihood depends on analyzing conceptual difficulties. Here, then, is Guttmann's challenge.
He does a fine job (once one wades through the technical morass and finds the relevant sections) of showing that an "objectivist" approach to statistical mechanics cannot succeed. That is, he shows that trying to locate probabilities in the purely physical properties of a particle system won't deliver a satisfactory account of why probabilities successfully characterize such a system. But when he considers non-objectivist, or what we might call conceptually based alternatives (the Bayesian or "subjectivist" approach, the "ergodic" approach, and the pragmatist approach), he is less successful at distinguishing these and making a case for his own preferred pragmatist approach.
The problem with subjectivist approaches is that strictly speaking their only criterion for assigning probabilities is internal coherence (probabilities have to be assigned so that a betting scheme based on them cannot always be a loser for one party who bets—heads I win, tails you lose is, for instance, not allowed). But there is a physical tie-in with statistical mechanics, and thus something more than coherence is required.
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