This undergraduate textbook introduces some fundamental issues in philosophy of science for students of philosophy and science students. The book is divided into two parts. Part 1 deals with knowledge and values. Chap. 1 presents the classical conception of knowledge as initiated by the ancient Greeks and elaborated during the development of science, introducing the central concepts of truth, belief and justification. Aspects of the quest for objectivity are taken up in the following two chapters. Moral issues are broached in (...) Chap. 4, which discusses some aspects of the use and abuse of science, taking up the responsibilities of scientists in properly conducting their business and decision-makers in their concerns with the import of science for society. Part 2 contrasts the view of scientific progress as the rejecting of old hypotheses and theories and replacing them with new ones, represented by Karl Popper, with the conception of progress as accumulating knowledge, saving as much as possible from older theories, represented by Pierre Duhem. A concluding chapter defends the natural attitude of taking the theories of modern science to be literally true, i.e. realism, in the face of arguments drawn partly from the history of scientific progress in criticism of this stance. (shrink)

Our purpose in this paper is to contribute to a practice-based characterization of scientific inference. We want to explore whether Brandom’s pragmatist–inferentialist framework can suitably accommodate several types of ampliative inference common in scientific reasoning and explanation (probabilistic reasoning, abduction and idealisation). First, we argue that Brandom’s view of induction in terms of merely permissive inferences is inadequate; in order to overcome the shortcoming of Brandom’s proposal, we put forward an alternative conception of inductive, probabilistic reasoning by appeal to the (...) notion of degrees of commitment. Moreover, we examine the sorts of inferential commitments operative in other types of ampliative inferences, such as abduction or reasoning involving idealizations and assumptions. We suggest that agents engaging in these forms of reasoning often undertake restricted inferential commitments, whose scope and reach are more limited that in the case of the commitments associated with full beliefs. (shrink)

The orthodox view of logic takes for granted the central importance of logical principles. Logic, and thus logical reasoning, is to be understood as a system of rules or principles with universal application. Let us call this orthodox view logical generalism. In this paper we argue that logical generalism, whether monist or pluralist, is wrong. We then outline an account of logical consequence in the absence of general logical principles, which we call logical particularism.

This essay criticizes John Norton's 2010 defense of the thesis that “all induction is local.” Norton's local inductions are bound, if cogent, to involve general principles, and the need to accredit these general principles threatens to lead to all the usual problems associated with the ‘problem of induction’. Norton, in fact, recognizes this threat, but his responses are inadequate. The right response involves not induction but a sophisticated version of hypothetico-deduction. Norton's secondary thesis—that if there is a general account of (...) cogent scientific reasoning, then it is certainly not the one supported by personalist Bayesians—is also criticized. (shrink)

John D. Norton is responsible for a number of influential views in contemporary philosophy of science. This paper will discuss two of them. The material theory of induction claims that inductive arguments are ultimately justified by their material features, not their formal features. Thus, while a deductive argument can be valid irrespective of the content of the propositions that make up the argument, an inductive argument about, say, apples, will be justified (or not) depending on facts about apples. The argument (...) view of thought experiments claims that thought experiments are arguments, and that they function epistemically however arguments do. These two views have generated a great deal of discussion, although there hasn’t been much written about their combination. I argue that despite some interesting harmonies, there is a serious tension between them. I consider several options for easing this tension, before suggesting a set of changes to the argument view that I take to be consistent with Norton’s fundamental philosophical commitments, and which retain what seems intuitively correct about the argument view. These changes require that we move away from a unitary epistemology of thought experiments and towards a more pluralist position. (shrink)

John D. Norton defends an empiricist epistemology of thought experiments, the central thesis of which is that thought experiments are nothing more than arguments. Philosophers have attempted to provide counterexamples to this claim, but they haven’t convinced Norton. I will point out a more fundamental reason for reformulation that criticizes Norton’s claim that a thought experiment is a good one when its underlying logical form possesses certain desirable properties. I argue that by Norton’s empiricist standards, no thought experiment is ever (...) justified in any deep sense due to the properties of its logical form. Instead, empiricists should consider again the merits of evaluating thought experiments more like laboratory experiments, and less like arguments. (shrink)

The no-free-lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning theory, that shows that some algorithms are better than others? Drawing parallels to the philosophy of induction, we point out that the no-free-lunch results presuppose a conception of learning algorithms as purely data-driven. On this conception, every algorithm must have an inherent inductive bias, that wants justification. We argue that many standard learning algorithms should rather (...) be understood as model-dependent: in each application they also require for input a model, representing a bias. Generic algorithms themselves, they can be given a model-relative justification. (shrink)

There are two important traditions in the philosophy of induction. According to one tradition, which has dominated for the last couple of centuries, inductive arguments are warranted by rules. Bayesianism is the most popular view within this tradition. Rules of induction provide functional accounts of inductive support, but no rule is universal; hence, no rule is by itself an accurate model of inductive support. According to another tradition, inductive arguments are not warranted by rules but by matters of fact. Norton’s (...) material theory of induction (MTI) is an influential view within this tradition. Norton’s MTI fails to provide an account of inductive support, but it provides a good account of inductive warrant that can help us define the domain of validity of each rule of induction. Despite their limitations, both approaches to induction are illuminating important aspects of how relations of inductive support work. In this article I present a hybrid theory of induction (HTI), in which I acknowledge and articulate the role of both rules and matters of fact in our understanding of inductive support. According to the HTI, rules of induction accurately describe relations of inductive support when they are warranted, and a rule of induction is warranted if the right facts about the matter of the induction obtain. Crucially, the HTI allows us to address the main challenges that each tradition faces while retaining their strengths, thus obtaining a functional and accurate account of inductive support. The HTI also provides a useful general framework to examine and understand induction, to make sense of how different rules of induction can coexist and to tackle some problems in epistemology. Moreover, the HTI allows us to clarify and resolve some current debates on induction, like the apparent divide between Norton and rule-based theorists. (shrink)

Our purpose in this paper is to contribute to a practice-based characterization of scientific inference. We want to explore whether Brandom’s pragmatist–inferentialist framework can suitably accommodate several types of ampliative inference common in scientific reasoning and explanation (probabilistic reasoning, abduction and idealisation). First, we argue that Brandom’s view of induction in terms of merely permissive inferences is inadequate; in order to overcome the shortcoming of Brandom’s proposal, we put forward an alternative conception of inductive, probabilistic reasoning by appeal to the (...) notion of degrees of commitment. Moreover, we examine the sorts of inferential commitments operative in other types of ampliative inferences, such as abduction or reasoning involving idealizations and assumptions. We suggest that agents engaging in these forms of reasoning often undertake restricted inferential commitments, whose scope and reach are more limited that in the case of the commitments associated with full beliefs. (shrink)

John D. Norton’s “Material Theory of Induction” has been one of the most intriguing recent additions to the philosophy of induction. Norton’s account appears to be a notably natural account of actual inductive practices, although his theory has attracted considerable criticism. I detail several novel issues for his theory but argue that supplementing the Material Theory with a theory of direct inference could address these problems. I argue that if this combination is possible, a stronger theory of inductive reasoning emerges, (...) which has a more propitious answer to the Problem of Induction. (shrink)

According to John D. Norton's Material Theory of Induction, all inductive inferences are justified by local facts, rather than their formal features or some grand principles of nature's uniformity. Recently, Richard Dawid (Found Phys 45(9):1101–1109, 2015) has offered a challenge to this theory: in an adaptation of Norton's own celebrated "Dome" thought experiment, it seems that there are certain inductions that are intuitively reasonable, but which do not have any local facts that could serve to justify them in accordance with (...) Norton's requirements. Dawid's suggestion is that “raw induction” might have a limited but important role for such inferences. I argue that the Material Theory can accommodate such inductions, because there are local facts concerning the combinatoric features of the induction’s target populations that can licence the inferences in an analogous way to existing examples of material induction. Since my arguments are largely independent of the details of the Dome, Norton's theory emerges as surprisingly robust against criticisms of excessive narrowness. (shrink)

The replicability of experiment is routinely offered as the gold standard of evidence. I argue that it is not supported by a universal principle of replicability in inductive logic. A failure of replication may not impugn a credible experimental result; and a successful replication can fail to vindicate an incredible experimental result. Rather, employing a material approach to inductive inference, the evidential import of successful replication of an experiment is determined by the prevailing background facts. Commonly, these background facts do (...) support successful replication as a good evidential guide and this has fostered the illusion of a deeper, exceptionless principle. (shrink)

The use of the material theory of induction to vindicate a scientist's claims of evidential warrant is illustrated with the cases of Einstein's thermodynamic argument for light quanta of 1905 and his recovery of the anomalous motion of Mercury from general relativity in 1915. In a survey of other accounts of inductive inference applied to these examples, I show that, if it is to succeed, each account must presume the same material facts as the material theory and, in addition, some (...) general principle of inductive inference not invoked by the material theory. Hence these principles are superfluous and the material theory superior in being more parsimonious. (shrink)

Curie’s principle asserts that every symmetry of a cause manifests as a symmetry of the effect. It can be formulated as a tautology that is vacuous until it is instantiated. However instantiation requires us to know the correct way to map causal terminology onto the terms of a science. Causal metaphysics has failed to provide a unique, correct way to carry out the mapping. Thus successful or unsuccessful instantiation merely reflects our freedom of choice in the mapping.

In a formal theory of induction, inductive inferences are licensed by universal schemas. In a material theory of induction, inductive inferences are licensed by facts. With this change in the conception of the nature of induction, I argue that the celebrated “problem of induction” can no longer be set up and is thereby dissolved. Attempts to recreate the problem in the material theory of induction fail. They require relations of inductive support to conform to an unsustainable, hierarchical empiricism.

In this paper, I present a new framework supporting the claim that some elements in science play a constitutive function, with the aim of overcoming some limitations of Friedman's (2001) account. More precisely, I focus on what I consider to be the gradualism implicit in Friedman's interpretation of the constitutive a priori, that is, the fact that it seems to allow for degrees of 'constitutivity'. I tease out such gradualism by showing that the constitutive character Friedman aims to track can (...) be captured by three features - namely, quasi-axiomaticity (QA), generative potential (GP), and empirical shielding (ES) - which are exhibited to a maximal degree by the examples Friedman deploys, particularly in his analysis of Newtonian mechanics. I argue that not all varieties of 'constitutivity' can be captured by the kind of gradualism implicit in Friedman's view, although developing the gradualism itself might provide useful insights. To show this, I analyse the function of the Hardy-Weinberg principle (HWP) in population genetics in terms of its QA, GP, and ES. Whereas the HWP does not count as constitutive in classical philosophical interpretations (Sober 1984), nor does it within Friedman's framework, it does nonetheless perform a minimally constitutive function. By means of historical details and considerations on the prospects of replacing the HWP, I show that the HWP is minimally constitutive by being a counterfactual instantiation of a paradigmatically constitutive stability principle, where the latter might itself be regarded as an enabling condition for a variety of modelling practices across the sciences. (shrink)

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...) argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...) argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)

John Norton has proposed a position of “material induction” that denies the existence of a universal inductive inference schema behind scientific reasoning. In this vein, Norton has recently presented a “dome scenario” based on Newtonian physics that, in his understanding, is at variance with Bayesianism. The present note points out that a closer analysis of the dome scenario reveals incompatibilities with material inductivism itself.

Although the inferring of explanations plays an important role in both our everyday lives and in the workings of science, I argue that inference to the best explanation as it is commonly conceived is often not the best way to capture this sort of reasoning. I suggest that a different form of reasoning – so-called immediate explanatory inference – is instead often much better suited to this task. This is a form of inference in which we are justified in believing (...) explanations for the evidence before us purely in virtue of this evidence, and not in virtue of the evidence plus some general principle or rule of non-deductive reasoning. I defend the idea of such a notion of inference, and argue that it plays a central role in both ordinary life and science. (shrink)

The current state of inductive logic is puzzling. Survey presentations are recurrently offered and a very rich and extensive handbook was entirely dedicated to the topic just a few years ago [23]. Among the contributions to this very volume, however, one finds forceful arguments to the effect that inductive logic is not needed and that the belief in its existence is itself a misguided illusion , while other distinguished observers have eventually come to see at least the label as “slightly (...) antiquated” .What seems not to have lost any of its currency is the problem which inductive logic is meant to address. Inference from limited ascertained information to uncertain hypotheses is ubiquitous in learning, prediction and discovery. The logical insight that such kind of inference is fallible m .. (shrink)

This paper discusses recent attempts to solve the problem of induction. Two broad strategies to escape Hume's fork can be distinguished. The first tries to localize the justification of specific inductions in uncontroversial empirical knowledge, e.g.mundane scientific knowledge (J. D. Norton) or perception (M. Lange). I argue that related attempts to (dis)solve the problem fail. The second strategy tries to put forward an argument in favor of induction. As a discussion of work by R. White shows, this argument can barely (...) prove that induction is reliable or at least not unreliable. But D. Steel, F. Huber and G. Schurz could show that enumerative induction is necessary and sufficient for a certain epistemic goal or optimal in a certain sense. These proofs, however, only solve the problem of induction if the goal or a certain standard has priority over the avoidance of error. This suggests that the difficulties to justify induction do not so much derive from Hume's fork, but rather from a plurality of sensible epistemic goals that can conflict with each other. (shrink)

My dissertation explores the ways in which Rudolf Carnap sought to make philosophy scientific by further developing recent interpretive efforts to explain Carnap’s mature philosophical work as a form of engineering. It does this by looking in detail at his philosophical practice in his most sustained mature project, his work on pure and applied inductive logic. I, first, specify the sort of engineering Carnap is engaged in as involving an engineering design problem and then draw out the complications of design (...) problems from current work in history of engineering and technology studies. I then model Carnap’s practice based on those lessons and uncover ways in which Carnap’s technical work in inductive logic takes some of these lessons on board. This shows ways in which Carnap’s philosophical project subtly changes right through his late work on induction, providing an important corrective to interpretations that ignore the work on inductive logic. Specifically, I show that paying attention to the historical details of Carnap’s attempt to apply his work in inductive logic to decision theory and theoretical statistics in the 1950s and 1960s helps us understand how Carnap develops and rearticulates the philosophical point of the practical/theoretical distinction in his late work, offering thus a new interpretation of Carnap’s technical work within the broader context of philosophy of science and analytical philosophy in general. (shrink)

This thesis studies the role of conceptual content in inference and reasoning. The first two chapters offer a theoretical and historical overview of the relation between inference and meaning in philosophy and psychology. In particular, a critical analysis of the formality thesis, i.e., the idea that rational inference is a rule-based and topic-neutral mechanism, is advanced. The origins of this idea in logic and its influence in philosophy and cognitive psychology are discussed. Chapter 3 consists of an analysis of the (...) relationship between inference and representation. It is argued that inference has to be studied from a pluralistic per- spective due to its dependence on different formats of representing information. The following four chapters apply conceptual spaces, a formal theory of concepts within cognitive semantics, to three concept-based inference-types. First, an explication of Sellars notion of material inference is advanced. Later, the model is extended to account for nonmonotonic inference by studying the role expectations in reasoning. Finally, a conceptual space-model of category-based induction is presented. This model predicts most of the empirical properties of this psychological phenomenon and subsumes some of the previous theories in psychology. It is stated that the explanatory fruitfulness of this new approach is evidence for the failure of the formality thesis and calls for a unified model of rational inference that puts semantics at center stage. The last chapter of the thesis discusses how inference and concepts interact in scientific reasoning, which makes constant use of hybrid symbolic structures for representing conceptual information. Stephen Toulmin’s notions of method of representation and inferential technique are developed and applied in a case study about the emergence of the notion of instantaneous speed during the passage from geometrical physics to analytical mechanics. It is claimed that this analysis provides support to the pluralistic perspective for theorizing about reasoning. (shrink)

State of the art paper on the problem of induction: how to justify the conclusion that ‘all Fs are Gs’ from the premise that ‘all observed Fs are Gs’. The most prominent theories of contemporary philosophical literature are discussed and analysed, such as: inductivism, reliabilism, perspective of laws of nature, rationalism, falsificationism, the material theory of induction and probabilistic approaches, according to Carnap, Reichenbach and Bayesianism. In the end, we discuss the new problem of induction of Goodman, raised by the (...) grue predicate. (shrink)

This document collects discussion and commentary on issues raised in the workshop by its participants. Contributors are: Greg Frost-Arnold, David Harker, P. D. Magnus, John Manchak, John D. Norton, J. Brian Pitts, Kyle Stanford, Dana Tulodziecki.

I highlight a lacuna in recent debates concerning analogies in science. Most philosophers focus on analogical inferences as a way to justify accepting hypotheses. I argue that analogies play another important role, namely to justify pursuing hypotheses. In particular, I argue that both Paul Bartha's formal account and John Norton's material view of analogical inference, as they stand, fail to address this issue. I present my own account of justification for pursuit and show how analogies on this account can justify (...) pursuit independently of whether they can also justify acceptance. (shrink)