Christopher Pincock

Explanatory pluralism is the view that explanations come in two or more different types. This chapter clarifies two versions of explanatory pluralism and considers two very different attempts to make sense of it. On the one hand, an ontic approach isolates genuine explanations only by appeal to facts that obtain in the world. The most promising way for an ontic approach to accommodate explanatory pluralism is to posit different sorts of objective dependence relations. On the other hand, an epistemic approach requires that features of agents appear in any analysis of what a genuine explanation is. This chapter argues that there is a version of an ontic account and a version of an epistemic account that do make sense of explanatory pluralism in their own terms. At the same time, pluralism raises problems for each approach that require further consideration.

This paper argues that investigations into how to best make something often provide researchers with an objectual understanding of their target phenomena. This argument starts with an extended investigation into the non-representational uses of models. In particular, we identify a special sort of “design model” whose aim is to guide the production of phenomena. Clarifying how these design models are evaluated shows that they are evaluated in different ways than representational models. Once the character of design models has been fixed, we argue that grasping design models can provide objectual understanding of phenomena. This argument proceeds through a critical engagement with Dellsén’s (2020) position that a grasp of a good representational model of dependencies provides objectual understanding of a phenomenon. We agree with Dellsén that this is one way to achieve understanding, but maintain that grasping a good design model is another way to achieve understanding. The paper concludes by considering some important objections to our proposal and also by noting some of the broader questions about understanding and knowledge in both science and engineering.

This paper considers a range of proofs of Morley’s theorem, and uses this variety to argue for pluralism about explanatory proof. The main alternative to pluralism is Lange’s proposal that an explanatory proof obtains the salient feature of some theorem by exploiting the same kind of feature of the theorem’s setup. This proposal can be amended in a wide variety of different ways to deal with the proofs of Morley’s theorem that I consider. However, it is not clear how to revise Lange’s proposal to reach the right verdict in all of these cases.

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.

This Element answers four questions. Can any traditional theory of scientific explanation make sense of the place of mathematics in explanation? If traditional monist theories are inadequate, is there some way to develop a more flexible, but still monist, approach that will clarify how mathematics can help to explain? What sort of pluralism about explanation is best equipped to clarify how mathematics can help to explain in science and in mathematics itself? Finally, how can the mathematical elements of an explanation be integrated into the physical world? Some of the evidence for a novel scientific posit may be traced to the explanatory power that this posit would afford, were it to exist. Can a similar kind of explanatory evidence be provided for the existence of mathematical objects, and if not, why not?

This paper argues that investigations into how to best make something often provide researchers with an objectual understanding of their target phenomena. This argument starts with an extended investigation into the non-representational uses of models. In particular, we identify a special sort of “design model” whose aim is to guide the production of phenomena. Clarifying how these design models are evaluated shows that they are evaluated in different ways than representational models. Once the character of design models has been fixed, we argue that grasping design models can provide objectual understanding of phenomena. This argument proceeds through a critical engagement with Dellsén’s ( 2020 ) position that a grasp of a good representational model of dependencies provides objectual understanding of a phenomenon. We agree with Dellsén that this is one way to achieve understanding, but maintain that grasping a good design model is another way to achieve understanding. The paper concludes by considering some important objections to our proposal and also by noting some of the broader questions about understanding and knowledge in both science and engineering.

At different times logical empiricists engaged one another in debates about the proper problems and methods for philosophy or its successor discipline. The most pressing problem focused on how to coordinate the abstract statements of the sciences with what can be experienced and tested. While the new logic was the main tool for coordination for Moritz Schlick, Hans Reichenbach, and Rudolf Carnap, there was no agreement on the nature of logic or its role in coordination. Otto Neurath and Philipp Frank countered with a sophisticated alternative that emphasized the social and political context within which science is done. All told, one finds in logical empiricism a high level of methodological awareness as well as a healthy skepticism about the appropriate aims and methods of philosophy.

Should the scientific realist admit that their realism involves what Chakravartty has called an epistemic stance? I argue that the realist should accept the need for a realist stance that licenses the use of inference to the best explanation. However, unlike Chakravartty, I maintain that the realist should insist that their realist stance is rationally obligatory. This requires an anti-voluntarism about stances that involves theoretical reasons for adopting one stance rather than another. I present one account of what these reasons might be that is tied to principles that identify some strong connections between evidence, knowledge and genuine explanation.

This paper illustrates how an experimental discovery can prompt the search for a theoretical explanation and also how obtaining such an explanation can provide heuristic benefits for further experimental discoveries. The case considered begins with the discovery of Poiseuille’s law for steady fluid flow through pipes. The law was originally supported by careful experiments, and was only later explained through a derivation from the more basic Navier–Stokes equations. However, this derivation employed a controversial boundary condition and also relied on a contentious approach to viscosity. By comparing two editions of Lamb’s famous Hydrodynamics textbook, I argue that explanatory considerations were central to Lamb’s claims about this sort of fluid flow. In addition, I argue that this treatment of Poiseuille’s law played a heuristic role in Reynolds’ treatment of turbulent flows, where Poiseuille’s law fails to apply.

This paper considers how to best relate the competing accounts of scientific knowledge that Russell and Reichenbach proposed in the 1930s and 1940s. At the heart of their disagreements are two different accounts of how to best combine a theory of knowledge with scientific realism. Reichenbach argued that a broadly empiricist epistemology should be based on decisions. These decisions or “posits” informed Reichenbach’s defense of induction and a corresponding conception of what knowledge required. Russell maintained that a scientific realist must abandon empiricism in favor of knowledge of some non-demonstrative principles with a non-empirical basis. After identifying the best versions of realism offered by Reichenbach and Russell, the paper concludes with a brief discussion of the limitations of these two approaches.

How can a reflective scientist put forward an explanation using a model when they are aware that many of the assumptions used to specify that model are false? This paper addresses this challenge by making two substantial assumptions about explanatory practice. First, many of the propositions deployed in the course of explaining have a non-representational function. In particular, a proposition that a scientist uses and also believes to be false, i.e. an “idealization”, typically has some non-representational function in the practice, such as the interpretation of some model or the specification of the target of the explanation. Second, when an agent puts forward an explanation using a model, they usually aim to remain agnostic about various features of the phenomenon being explained. In this sense, explanations are intended to be autonomous from many of the more fundamental features of such systems. I support these two assumptions by showing how they allow one to address a number of recent concerns raised by Bokulich, Potochnik and Rice. In addition, these assumptions lead to a defense of the view that explanations are wholly true that improves on the accounts developed by Craver, Mäki and Strevens.

This paper defends three claims about concrete or physical models: these models remain important in science and engineering, they are often essentially idealized, in a sense to be made precise, and despite these essential idealizations, some of these models may be reliably used for the purpose of causal explanation. This discussion of concrete models is pursued using a detailed case study of some recent models of landslide generated impulse waves. Practitioners show a clear awareness of the idealized character of these models, and yet address these concerns through a number of methods. This paper focuses on experimental arguments that show how certain failures to accurately represent feature X are consistent with accurately representing some causes of feature Y, even when X is causally relevant to Y. To analyse these arguments, the claims generated by a model must be carefully examined and grouped into types. Only some of these types can be endorsed by practitioners, but I argue that these endorsed claims are sufficient for limited forms of causal explanation.

A pluralist about explanation posits many explanatory relevance relations, while an invariantist denies any substantial role for context in fixing genuine explanation. This article summarizes one approach to combining pluralism and invariantism that emphasizes the contrastive nature of explanation. If explanations always take contrasts as their objects and contrasts come in types, then the role for the context in which an explanation is given can be minimized. This approach is illustrated using a classic debate between natural theology and natural selection about the structure of bees’ honeycombs.

My goal in reviewing Soames’ book was to help readers of this journal evaluate his contribution to the history of analytic philosophy, with a special focus on his discussion of Russell. Soames charges both that I misrepresent the contents of his book and that I make mistakes in the interpretation of various aspects of Russell’s philosophy. If I had committed errors of the former sort, I would certainly apologize and thank Soames for bringing such mistakes to my attention. After explaining why, I do not believe I have misrepresented the contents of his book, I will turn to the one substantive issue that he raises in his reply, namely the need for unperceived sense-data in Russell’s external world program. Nothing Soames says in his book or in his reply has led me to revise my original remarks.

Russell’s study of the biologist and psychologist Richard Semon is traced to contact with the experimental psychologist Adolf Wohlgemuth and dated to the summer of 1919. This allows a new interpretation of when Russell embraced neutral monism and presents a case-study in Russell’s use of scientific results for philosophical purposes. Semon’s distinctive notion of mnemic causation was used by Russell to clarify both how images referred to things and how the existence of images could be reconciled with a neutral monist metaphysics