In this article, we offer a detailed study of two important episodes in the early history of high-energy physics, namely the development of the Chew and the Nambu-Jona-Lasinio models. Our study reveals that both models resulted from the combination of an old Hamiltonian, which had been introduced by earlier researchers, and two new approximation methods developed by Chew and by Nambu and Jona-Lasinio. These new approximation methods, furthermore, were the key component behind the models’ success. We take this historical investigation (...) to support two philosophical theses about the manner in which scientific modelling operates in high-energy physics. Both of these theses run counter to a view that is commonly accepted among philosophers of science: the view that all approximations can be embedded within an equivalent idealized system, and that whatever role the former might play in scientific modelling is therefore parasitic on the much more substantial work performed by the latter. Our first thesis, which we call “Distinctness,” states that approximation methods constitute an independent category of theoretical output from idealized systems. We thus believe that approximations and idealized systems constitute two independent types of objects, both of which are essential to the practice of modelling. Our second, more radical thesis is called “Content Determination.” Our claim here is that approximation methods can in fact be essential to assigning determinate physical content to the idealized systems with which they jointly operate. As we show, this is due to the fact that quantum field theory allows for a very thin characterization of idealized systems only, making the use of approximations necessary to supply additional content. We conclude the paper with a few reflections about the manner in which our two theses can be used to articulate David Kaiser’s views on the “vanishing of scientific theory” in physics after WWII. (shrink)

Kaplan and Craver claim that all explanations in neuroscience appeal to mechanisms. They extend this view to the use of mathematical models in neuroscience and propose a constraint such models must meet in order to be explanatory. I analyze a mathematical model used to provide explanations in dynamical systems neuroscience and indicate how this explanation cannot be accommodated by the mechanist framework. I argue that this explanation is well characterized by Batterman’s account of minimal model explanations and that it demonstrates (...) how relationships between explanatory models in neuroscience and the systems they represent is more complex than has been appreciated. (shrink)

Conflicting accounts of the role of mathematics in our physical theories can be traced to two principles. Mathematics appears to be both (1) theoretically indispensable, as we have no acceptable non-mathematical versions of our theories, and (2) metaphysically dispensable, as mathematical entities, if they existed, would lack a relevant causal role in the physical world. I offer a new account of a role for mathematics in the physical sciences that emphasizes the epistemic benefits of having mathematics around when we do (...) science. This account successfully reconciles theoretical indispensability and metaphysical dispensability and has important consequences for both advocates and critics of indispensability arguments for platonism about mathematics. (shrink)

In this paper, we develop and refine the idea that understanding is a species of explanatory knowledge. Specifically, we defend the idea that S understands why p if and only if S knows that p, and, for some q, S’s true belief that q correctly explains p is produced/maintained by reliable explanatory evaluation. We then show how this model explains the reception of James Bjorken’s explanation of scaling by the broader physics community in the late 1960s and early 1970s. The (...) historical episode is interesting because Bjorken’s explanation initially did not provide understanding to other physicists, but was subsequently deemed intelligible when Feynman provided a physical interpretation that led to experimental tests that vindicated Bjorken’s model. Finally, we argue that other philosophical models of scientific understanding are best construed as limiting cases of our more general model. (shrink)

In epistemology and philosophy of science, there has been substantial debate about truth’s relation to understanding. “Non-factivists” hold that radical departures from the truth are not always barriers to understanding; “quasi-factivists” demur. The most discussed example concerns scientists’ use of idealizations in certain derivations of the ideal gas law from statistical mechanics. Yet, these discussions have suffered from confusions about the relevant science, as well as conceptual confusions. Addressing this example, we shall argue that the ideal gas law is best (...) interpreted as favoring non-factivism about understanding, but only after delving a bit deeper into the statistical mechanics that has informed these arguments and stating more precisely what non-factivism entails. Along the way, we indicate where earlier discussions have gone astray, and highlight how a naturalistic approach furnishes more nuanced normative theses about the interaction of rationality, understanding, and epistemic value. (shrink)

There is a widely shared belief that the higher-level sciences can provide better explanations than lower-level sciences. But there is little agreement about exactly why this is so. It is often suggested that higher-level explanations are better because they omit details. I will argue instead that the preference for higher-level explanations is just a special case of our general preference for informative, logically strong, beliefs. I argue that our preference for informative beliefs entirely accounts for why higher-level explanations are sometimes (...) better—and sometimes worse—than lower-level explanations. The result is a step in the direction of the unity of science hypothesis. 1Introduction2Background: Is Omitting Details an Explanatory Virtue? 2.1Anti-reductionist arguments2.2Reductionist argument2.3Logical strength3Bases, Links and Logical Strength4Functionalism and Fodor’s Argument5Two Generalizations6Should the Base Really Be Maximally Strong?7Anti-reductionist Arguments Regarding the Base8Should the Antecedent of the Link Really Be Maximally Weak? (shrink)

We analyze the effects of the introduction of new mathematical tools on an old branch of physics by focusing on lattice fluids, which are cellular automata -based hydrodynamical models. We examine the nature of these discrete models, the type of novelty they bring about within scientific practice and the role they play in the field of fluid dynamics. We critically analyze Rohrlich's, Fox Keller's and Hughes' claims about CA-based models. We distinguish between different senses of the predicates “phenomenological” and “theoretical” (...) for scientific models and argue that it is erroneous to conclude, as they do, that CA-based models are necessarily phenomenological in any sense of the term. We conversely claim that CA-based models of fluids, though at first sight blatantly misrepresenting fluids, are in fact conservative as far as the basic laws of statistical physics are concerned and not less theoretical than more traditional models in the field. Based on our case-study, we propose a general discussion of the prospect of CA for modeling in physics. We finally emphasize that lattice fluids are not just exotic oddities but do bring about new advantages in the investigation of fluids' behavior. (shrink)