According to a widely shared generic conception of inferential justification—‘the standard conception’—an agent is inferentially justified in believing that p only if she has antecedently justified beliefs in all the non-redundant premises of a good argument for p. This conception tends to serve as the starting-point in contemporary debates about the nature and scope of inferential justification: as neutral common ground between various competing, more specific, conceptions. But it’s a deeply problematic starting-point. This paper explores three questions that haven’t been (...) given the attention they deserve, that complicate the application of the standard conception to cases, and that reveal it to be underspecified at the core—in ways that aren’t resolved but inherited by more specific (extant) versions of it. The goal isn’t to answer the questions, but to articulate them, explain what turns on them, and invite a critical re-examination of the standard conception. (shrink)

There are two senses of ‘what scientists know’: An individual sense (the separate opinions of individual scientists) and a collective sense (the state of the discipline). The latter is what matters for policy and planning, but it is not something that can be directly observed or reported. A function can be defined to map individual judgments onto an aggregate judgment. I argue that such a function cannot effectively capture community opinion, especially in cases that matter to us.

Some philosophers think that there is a gap between is and ought which necessarily makes normative enquiry a different kind of thing than empirical science. This position gains support from our ability to explicate our inferential practices in a way that makes it impermissible to move from descriptive premises to a normative conclusion. But we can also explicate them in a way that allows such moves. So there is no categorical answer as to whether there is or is not a (...) gap. The question of an is-ought gap is a practical and strategic matter rather than a logical one, and it may properly be answered in different ways for different questions or at different times. (shrink)

Some scientific categories seem to correspond to genuine features of the world and are indispensable for successful science in some domain; in short, they are natural kinds. This book gives a general account of what it is to be a natural kind and puts the account to work illuminating numerous specific examples.

This paper offers a general characterization of underdetermination and gives a prima facie case for the underdetermination of the topology of the universe. A survey of several philosophical approaches to the problem fails to resolve the issue: the case involves the possibility of massive reduplication, but Strawson on massive reduplication provides no help here; it is not obvious that any of the rival theories are to be preferred on grounds of simplicity; and the usual talk of empirically equivalent theories misses (...) the point entirely. (If the choice is underdetermined, then the theories are not empirically equivalent!) Yet the thought experiment is analogous to a live scientific possibility, and actual astronomy faces underdetermination of this kind. This paper concludes by suggesting how the matter can be resolved, either by localizing the underdetermination or by defeating it entirely. Introduction A brief preliminary Around the universe in 80 days Some attempts at resolving the problem 4.1 Indexicality 4.2 Simplicity 4.3 Empirical equivalence 4.4 Is this just a philosophers' fantasy? Move along... ...nothing to see here 6.1 Rules of repetition 6.2 Some possible replies Conclusion. (shrink)

It has been common wisdom for centuries that scientific inference cannot be deductive; if it is inference at all, it must be a distinctive kind of inductive inference. According to demonstrative theories of induction, however, important scientific inferences are not inductive in the sense of requiring ampliative inference rules at all. Rather, they are deductive inferences with sufficiently strong premises. General considerations about inferences suffice to show that there is no difference in justification between an inference construed demonstratively or ampliatively. (...) The inductive risk may be shouldered by premises or rules, but it cannot be shirked. Demonstrative theories of induction might, nevertheless, better describe scientific practice. And there may be good methodological reasons for constructing our inferences one way rather than the other. By exploring the limits of these possible advantages, I argue that scientific inference is neither of essence deductive nor of essence inductive. (shrink)

Background theories in science are used both to prove and to disprove that theory choice is underdetermined by data. The alleged proof appeals to the fact that experiments to decide between theories typically require auxiliary assumptions from other theories. If this generates a kind of underdetermination, it shows that standards of scientific inference are fallible and must be appropriately contextualized. The alleged disproof appeals to the possibility of suitable background theories to show that no theory choice can be timelessly or (...) noncontextually underdetermined: Foreground theories might be distinguished against different backgrounds. Philosophers have often replied to such a disproof by focussing their attention not on theories but on Total Sciences. If empirically equivalent Total Sciences were at stake, then there would be no background against which they could be differentiated. I offer several reasons to think that Total Science is a philosophers' fiction. No respectable underdetermination can be based on it. (shrink)

Using the context of controversies surrounding evolutionary developmental biology (EvoDevo) and the possibility of an Extended Evolutionary Synthesis, I provide an account of theory structure as idealized theory presentations that are always incomplete (partial) and shaped by their conceptual content (material rather than formal organization). These two characteristics are salient because the goals that organize and regulate scientific practice, including the activity of using a theory, are heterogeneous. This means that the same theory can be structured differently, in part because (...) theory presentations (as idealizations) intentionally depart from different features known to be present in a theory. Since there are diverse and potentially incompatible theory structures derived from heterogeneous goals found in scientific practices, a question arises about the absence of a unifying theory structure in the background. The notion of a “theory façade” offers a fruitful perspective on this potentially unsettling result. (shrink)

John Norton’s argument that all formal theories of induction fail raises substantive questions about the philosophical analysis of scientific reasoning. What are the criteria of adequacy for philosophical theories of induction, explanation, or theory structure? Is more than one adequate theory possible? Using a generalized version of Norton’s argument, I demonstrate that the competition between formal and material theories in philosophy of science results from adhering to different criteria of adequacy. This situation encourages an interpretation of “formal” and “material” as (...) indicators of divergent criteria that accompany different philosophical methodologies. I characterize another criterion of adequacy associated with material theories, the avoidance of imported problems, and conclude that one way to negotiate between conflicting criteria is to adopt a pluralist stance toward philosophical theories of scientific reasoning. (shrink)

Scientific explanation is a perennial topic in philosophy of science, but the literature has fragmented into specialized discussions in different scientific disciplines. An increasing attention to scientific practice by philosophers is (in part) responsible for this fragmentation and has put pressure on criteria of adequacy for philosophical accounts of explanation, usually demanding some form of pluralism. This commentary examines the arguments offered by Fagan and Woody with respect to explanation and understanding in scientific practice. I begin by scrutinizing Fagan's concept (...) of collaborative explanation, highlighting its distinctive advantages and expressing concern about several of its assumptions. Then I analyze Woody's attempt to reorient discussions of scientific explanation around functional considerations, elaborating on the wider implications of this methodological recommendation. I conclude with reflections on synergies and tensions that emerge when the two papers are juxtaposed and how these draw attention to critical issues that confront ongoing philosophical analyses of scientific explanation. (shrink)

We propose a new account of calibration according to which calibrating a technique shows that the technique does what it is supposed to do. To motivate our account, we examine an early twentieth-century debate about chlorophyll chemistry and Mikhail Tswett’s use of chromatographic adsorption analysis to study it. We argue that Tswett’s experiments established that his technique was reliable in the special case of chlorophyll without relying on either a theory or a standard calibration experiment. We suggest that Tswett broke (...) the experimenters’ regress by appealing to material facts in the common ground for chemists at the time. (shrink)

Otto Neurath’s empiricist methodology of economics and his contributions to politi- cal economy have gained increasing attention in recent years. We connect this research with contemporary debates regarding the epistemological status of thought experiments by reconstructing Neurath’s utopias as linchpins of thought experiments. In our three reconstructed examples of different uses of utopias/dystopias in thought experiments we employ a reformulation of Häggqvist’s model for thought experiments and we argue that: (1) Our reformulation of Häggqvist’s model more adequately complies with many (...) uses of thought experiments, especially with the open-ended discussions of utopias and dys- topias in thought experiments. (2) As a strict logical empiricist, Neurath is committed to a strictly empiricist account of thought experiments. John Norton’s empiricist argument view can indeed account for the justifications of empirical beliefs and genuine discoveries targeted by scientific utopianism in three distinct (yet connected) ways, all of which Neur- ath already contemplated: (2.I) Dealing with utopias and thought experiments on a regular basis increases creativity and inventiveness. (2.II) Particular ways of presenting knowl- edge facilitate scientific discovery and social progress. (2.III) The use of utopias in thought experiments can prompt conceptual change and allow access to new phenomena. We con- clude by highlighting that, even though thought experiments support a positive attitude for exploring new social possibilities, Neurath points out that active decisions are unavoidable. The exploration of alternatives and the awareness of a need for decisions in policy discus- sion avert a technocratic outlook in social science. (shrink)

As scientists start to adopt machine learning (ML) as one research tool, the security of ML and the knowledge generated become a concern. In this paper, I explain how supervised ML can be improved with better data ontology, or the way we make categories and turn information into data. More specifically, we should design data ontology in such a way that is consistent with the knowledge that we have about the target phenomenon so that such ontology can help us make (...) the inductive leap. I do so by thinking through a thought experiment, Goodman’s New Riddle of Induction (Fact, fiction, and forecast, Harvard University Press, 1955). Goodman’s riddle helps flesh out three problems of induction: (1) the problem of equal goodies, that there are often too many equally good inductive results given the same data; (2) the problem of diverging performance, that these equally good results can give opposite predictions in the future; and (3) the problem of mediocrity, that when averaged across all equally possible datasets and tasks, no inductive algorithm outperforms any other. I show that all these three problems are manifested as real obstacles in ML practice, namely, the Rashomon effect (Breiman in Stat Sci 16(3):199–231, 2001), the problem of underspecification (D’Amour et al. in J Mach Learn Res, 2020, https://doi.org/10.48550/arXiv.2011.03395 ), and the No Free Lunch theorem (Wolpert in Neural Comput 8(7):1341–90, 1996, https://doi.org/10.1162/neco.1996.8.7.1341 ). Lastly, I argue that proper data ontology can help mitigate these problems and I demonstrate how using concrete examples from climate science. This research highlights the links between philosophers’ discussions of induction and implications in ML practice. (shrink)

This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in (...) making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research. (shrink)

In this paper I offer a coherentist justification of induction along the lines of a Sellarsian coherence theory. On this coherence theory, a proposition is justified if we can answer all objections raised against it in our social practice of demanding justification and responding to such demands. On the basis of this theory of justification, I argue that we are justified in accepting the uniformity of nature partly because we have no alternative but to accept it for rationally pursuing our (...) epistemic goal. In addition, my coherentist view explains which inductive inferences it is rational to accept. Furthermore, my coherentist view also explains why we can hardly determine a particular numerical value x such that we can draw the conclusion ‘it is rational to accept that p’ from the premise ‘the numerical probability of p is greater than or equal to x’. (shrink)

It is common knowledge that the Aristotelian idea of an unmoved mover was abandoned definitively with the advent of modern science and, in particular, Newton’s precise formulation of mechanics. Here I show that the essential attribute of an unmoved mover is not incompatible with such mechanics; quite the contrary, it makes this possible. The unmoved mover model proposed does not involve supertasks, and leads both to an outrageous form of indeterminism and a new, accountable form of interaction. The process presents (...) a more precise characterization of the crucial going-to-the-limit operation. It has long been acknowledged in the existing literature that, theoretically, in infinite Newtonian systems, masses can move from rest to motion through supertasks. Numerous minor variations on the original schemes have already been published. Against this backdrop, this paper introduces three significant additions: 1) It formulates for the first time a limit postulate for systematically addressing infinite systems; 2) It shows that an Aristotelian unmoved mover is possible in systems of infinitely many particles that interact with each other solely by contact collision; 3) It shows how interaction at a distance can emerge in systems of infinitely many particles that interact with each other solely by contact. (shrink)

Das Dissertationsprojekt soll eine Verbindung zwischen zwei augenscheinlich separaten Debattensträngen in der kontemporären Wissenschaftstheorie erforschen. Das ist zum einen die Debatte um die humesche Doktrin, der zufolge es keine de-re notwendigen Verbindungen respektive modale Fakten in der natürlichen Welt gibt. Diese Debatte betrifft die Grundlagen der Naturwissenschaften sowie der Natur selbst. Und das ist zum anderen die Debatte um die materiale Theorie der Induktion, welche auf John D. Norton zurückgeht und die Grundlagen der Rationalität und Logik betrifft. Nach dieser Theorie (...) werden induktive Schlüsse qua relevanter, lokaler Hintergrundfakten zuverlässig. Norton bringt viele - in meinen Augen überzeugende - Beispiele für diese Theorie, lässt uns aber im Unklaren darüber, wie diese Hintergrundfakten induktive Schlüsse lizensieren können. Ich werde argumentieren, dass es sich bei diesen relevanten Fakten zumindest in einigen paradigmatischen Fällen um irreduzibel-modale oder modal grundierte Fakten handeln muss, ansonsten können diese die ihnen zugedachte Funktion nicht erfüllen. Infolgedessen steht der Humeanismus vor einer materialen Form des Induktionsproblems. -/- Meine Forschungshypothese lautet, dass es sich bei dem materialen Induktionsproblem um das eigentliche und zentrale Problem des Humeanismus handelt. Zum einen werden viele altbekannte Probleme des Humeanismus durch das materiale Induktionsproblem motiviert und bestärkt. Zum anderen begründet das materiale Induktionsproblem ein neues und tiefgreifendes Problem des Humeanismus. Es stellt sich darin, dass der Humeanismus den empirischen Erfolg unserer verfügbaren wissenschaftlichen und alltäglichen Gesetzesannahmen nicht zirkelfrei erklären kann. Darauf aufbauend formuliere ich ein Super-Wunderargument, das im Kern in einer Inferenz von den bisherigen empirischen Erfolgen unserer verfügbaren Gesetzesannahmen auf die Wahrheit des Anti-Humeanismus besteht. Dieses Argument steht im Zentrum meiner Arbeit. Alles Weitere wird im Kontext dieses Argumentes diskutiert und illustriert. Zu diesen Untersuchungen gehören die schrittweise Motivation des Argumentes durch das materiale Induktionsproblem, die Unterscheidung und Diskussion verschiedener logischer Formen des Argumentes, das Herausarbeiten der Vorteile sowie die Antizipation von möglichen Einwänden gegen das Argument. (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)

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)

Reasoning from inconclusive evidence, or ‘induction’, is central to science and any applications we make of it. For that reason alone it demands the attention of philosophers of science. This Element explores the prospects of using probability theory to provide an inductive logic, a framework for representing evidential support. Constraints on the ideal evaluation of hypotheses suggest that overall support for a hypothesis is represented by its probability in light of the total evidence, and incremental support, or confirmation, indicated by (...) an increased probability given the evidence than otherwise. This proposal is shown to have the capacity to reconstruct many canons of the scientific method and inductive inference. Along the way, significant objections are discussed, such as the challenge from inductive scepticism and the objection that the probabilistic approach makes evidential support arbitrary. (shrink)

With respect to inductive reasoning, there are at least two broad projects that have been of interest to philosophers. The first project is that of accurately describing paradigmatic instances of inductive reasoning in the sciences and in everyday life. Thus, we might ask, of some particular historical episode, how exactly Newton, or Darwin, or Einstein arrived at some conclusion on the basis of the evidence that was before him. The second project is one of justification. The task here is that (...) of showing why paradigmatic inductive reasoning is good reasoning, or why skeptical challenges which purport to undermine the legitimacy of such reasoning fail to do so. As I understand it, this second, justificatory project prominently includes, but is not limited to, the task of either solving or dissolving Hume’s skeptical critique of induction.[1] Neither of these projects is trivial. Indeed, many think that we are currently far from successfully completing either one. But in any case, it would seem that the descriptive project has a certain priority to the justification project. After all, how could one justify our inductive practices, or defend them against skeptical challenge, in the absence of some reasonably detailed and accurate description of those practices? Given this apparent priority, it is a striking fact that the justification project has often been pursued in the absence of any significant attention to the project of accurate description. In particular, a great deal of the traditional discussion of Hume’s problem of induction has proceeded against the background of hopelessly crude models of our actual inductive practice—a fact sometimes acknowledged by participants in such discussions themselves. Plausibly, the thought which underlies this procedure is the following: whatever force Hume’s critique ultimately possesses, that force is not tied to the specific content of the general rules or principles that we follow in reasoning inductively. Rather, the force of Hume’s critique depends on our following any general rules or principles at all.. (shrink)

This paper analyzes what it means for philosophy of science to be normative. It argues that normativity is a multifaceted phenomenon rather than a general feature that a philosophical theory either has or lacks. It analyzes the normativity of philosophy of science by articulating three ways in which a philosophical theory can be normative. Methodological normativity arises from normative assumptions that philosophers make when they select, interpret, evaluate, and mutually adjust relevant empirical information, on which they base their philosophical theories. (...) Object normativity emerges from the fact that the object of philosophical theorizing can itself be normative, such as when philosophers discuss epistemic norms in science. Metanormativity arises from the kind of claims that a philosophical theory contains, such as normative claims about science as it should be. Distinguishing these three kinds of normativity gives rise to a nuanced and illuminating view of how philosophy of science can be normative. (shrink)

Convincing disputes about explanatory reductionism in the philosophy of biology require a clear and precise understanding of what a reductive explanation in biology is. The central aim of this book is to provide such an account by revealing the features that determine the reductive character of a biological explanation. Chapters I-IV provide the ground, on which I can then, in Chapter V, develop my own account of explanatory reduction in biology: Chapter I reveals the meta-philosophical assumptions that underlie my analysis (...) of reduction, Chapter II introduces the previous debate about reduction in the philosophy of biology by pointing out the most crucial lessons one should learn from this debate, Chapter III critically discusses the two perspectives on explanatory reduction that have been proposed in the philosophy of biology so far, and Chapter IV clarifies in what ways the issue of reduction is entangled with the issue of explanation. In Chapter V I present my own account of explanatory reduction. My main claim is that reductive explanations in the biological sciences possess three main characteristics: they explain the behavior of a biological system S, first, exclusively in terms of factors that are located on a lower level of organization than S, second, by focusing on factors that are internal to S , and third, by describing the genuine parts of S only as “parts in isolation”. This account is ontic because it traces the reductivity of an explanation back to certain relations that exist in the world. (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)

The inaugural title in the new, Open Access series BSPS Open, The Material Theory of Induction will initiate a new tradition in the analysis of inductive inference. The fundamental burden of a theory of inductive inference is to determine which are the good inductive inferences or relations of inductive support and why it is that they are so. The traditional approach is modeled on that taken in accounts of deductive inference. It seeks universally applicable schemas or rules or a single (...) formal device, such as the probability calculus. After millennia of halting efforts, none of these approaches has been unequivocally successful and debates between approaches persist. The Material Theory of Induction identifies the source of these enduring problems in the assumption taken at the outset: that inductive inference can be accommodated by a single formal account with universal applicability. Instead, it argues that that there is no single, universally applicable formal account. Rather, each domain has an inductive logic native to it. Which that is, and its extent, is determined by the facts prevailing in that domain. Paying close attention to how inductive inference is conducted in science and copiously illustrated with real-world examples, The Material Theory of Induction will initiate a new tradition in the analysis of inductive inference. (shrink)

As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. One strategy for overcoming these challenges is guided by a presumption in philosophy of science that inductive inferences can and should be value-free. Applied to machine learning programs, the strategy assumes that the influence of values is restricted to data and decision outcomes, thereby omitting internal value-laden design choice points. In this paper, I apply arguments from feminist philosophy of (...) science to machine learning programs to make the case that the resources required to respond to these inductive challenges render critical aspects of their design constitutively value-laden. I demonstrate these points specifically in the case of recidivism algorithms, arguing that contemporary debates concerning fairness in criminal justice risk-assessment programs are best understood as iterations of traditional arguments from inductive risk and demarcation, and thereby establish the value-laden nature of automated decision-making programs. Finally, in light of these points, I address opportunities for relocating the value-free ideal in machine learning and the limitations that accompany them. (shrink)

European Journal of Philosophy, Volume 29, Issue 4, Page 864-886, December 2021.

While the term “reactivity” has come to be associated with specific phenomena in the social sciences, having to do with subjects’ awareness of being studied, this paper takes a broader stance on this concept. I argue that reactivity is a ubiquitous feature of the psychological subject matter and that this fact is a precondition of experimental research, while also posing potential problems for the experimenter. The latter are connected to the worry about distorted data and experimental artifacts. But what are (...) experimental artifacts and what is the most productive way of dealing with them? In this paper, I approach these questions by exploring the ways in which experimenters in psychology simultaneously exploit and suppress the reactivity of their subject matter in order to produce experimental data that speak to the question or subject matter at hand. Highlighting the artificiality of experimental data, I raise the question of what distinguishes a genuine experimental result from an experimental artifact. My analysis construes experimental results as the outcomes of inferences from the data that take material background assumptions as auxiliary premises. Artifacts occur when one or more of these background assumptions are false, such that the data do not reliably serve the purposes they were generated for. I conclude by laying out the ways in which my analysis of data quality is relevant to, and informed by, recent debates about the replicability of experimental results. (shrink)

The interventionist theory of causation has been advertised as an empirically informed and more nuanced approach to causality than the competing theories. However, previous literature has not yet analyzed the regression discontinuity (hereafter, RD) and the difference-in-differences (hereafter, DD) within an interventionist framework. In this paper, I point out several drawbacks of using the interventionist methodology for justifying the DD and RD designs. However, I argue that the first step towards enhancing our understanding of the DD and RD designs from (...) an interventionist perspective is to take advantage of the assumptions of common trend and continuity. (shrink)

This paper explains what’s wrong with a Hume-inspired argument for skepticism about induction. Hume’s argument takes as a premise that inductive reasoning presupposes that the future will resemble the past. I explain why that claim is not plausible. The most plausible premise in the vicinity is that inductive reasoning from E to H presupposes that if E then H. I formulate and then refute a skeptical argument based on that premise. Central to my response is a psychological explanation for how (...) people judge that if E then H without realizing that they thereby settled the matter rationally. (shrink)

Despite wide recognition that speculation is critical for successful science, philosophers have attended little to it. When they have, speculation has been characterized in narrowly epistemic terms: a hypothesis is speculative due to its (lack of) evidential support. These ‘evidence-first’ accounts provide little guidance for what makes speculation productive or egregious, nor how to foster the former while avoiding the latter. I examine how scientists discuss speculation and identify various functions speculations play. On this basis, I develop a ‘function-first’ account (...) of speculation. This analysis grounds a richer discussion of when speculation is egregious and when it is productive, based in both fine-grained analysis of the speculation’s purpose, and what I call the ‘epistemic situation’ scientists face. (shrink)

As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet (...) the latter part of the contract depends on human inductive predictions or generalisations, which infer a uniformity between the trained ML model and the targets. The paper asks how we justify the contract between human and machine learning. It is argued that the justification becomes a pressing issue when we use ML to reach ‘elsewheres’ in space and time or deploy ML models in non-benign environments. The paper argues that the only viable version of the contract can be based on optimality (instead of on reliability which cannot be justified without circularity) and aligns this position with Schurz’s optimality justification. It is shown that when dealing with inaccessible/unstable ground-truths (‘elsewheres’ and non-benign targets), the optimality justification undergoes a slight change, which should reflect critically on our epistemic ambitions. Therefore, the study of ML robustness should involve not only heuristics that lead to acceptable accuracies on testing sets. The justification of human inductive predictions or generalisations about the uniformity between ML models and targets should be included as well. Without it, the assumptions about inductive risk minimisation in ML are not addressed in full. (shrink)

David Armstrong argues that necessitation relations among universals are the best explanation of some of our observations. If we consequently accept them into our ontologies, then we can justify induction, because these necessitation relations also have implications for the unobserved. By embracing Armstrongian universals, we can vindicate some of our strongest epistemological intuitions and answer the Problem of Induction. However, Armstrong’s reasoning has recently been challenged on a variety of grounds. Critics argue against both Armstrong’s usage of inference to the (...) best explanation and even whether, by Armstrong’s own standards, necessitation relations offer a potential explanation of this explanandum, let alone the best explanation. I defend Armstrong against these particular criticisms. Firstly, even though there are reasons to think that Armstrong’s justification fails as a self-contained defence of induction, it can usefully complement several other answers to Hume. Secondly, I argue that Armstrong’s reasoning is consistent with his own standards for explanation. (shrink)

To demarcate the limits of experimental knowledge, we probe the limits of what might be called an experiment. By appeal to examples of scientific practice from astrophysics and analogue gravity, we demonstrate that the reliability of knowledge regarding certain phenomena gained from an experiment is not circumscribed by the manipulability or accessibility of the target phenomena. Rather, the limits of experimental knowledge are set by the extent to which strategies for what we call ‘inductive triangulation’ are available: that is, the (...) validation of the mode of inductive reasoning involved in the source-target inference via appeal to one or more distinct and independent modes of inductive reasoning. When such strategies are able to partially mitigate reasonable doubt, we can take a theory regarding the phenomena to be well supported by experiment. When such strategies are able to fully mitigate reasonable doubt, we can take a theory regarding the phenomena to be established by experiment. There are good reasons to expect the next generation of analogue experiments to provide genuine knowledge of unmanipulable and inaccessible phenomena such that the relevant theories can be understood as well supported. This article is part of a discussion meeting issue ‘The next generation of analogue gravity experiments’. (shrink)

Philosophical theories of evidence have been on offer, but they are mostly evaluated in terms of all-or-none desiderata — if they fail to meet one of the desiderata, they are not a satisfactory theory. In this thesis, I aim to accomplish three missions. Firstly, I construct a new way of evaluating theories of evidence, which I call a tool-based view. Secondly, I analyse the nature of what I will call the various relevance-mediating vehicles that each theory of evidence employs. Thirdly, (...) I articulate the comparative core of evidential reasoning in the historical sciences, one which is overlooked in major theories of evidence. On the first mission, I endorse a meta-thesis of pluralism on theories of evidence, namely a tool-based view. I regard a theory of evidence as a purpose-specific and setting-sensitive tool which has its own strengths, difficulties and limitations. Among the major theories of evidence I have reviewed, I focus on Achinstein’s explanationist theory, Cartwright’s argument theory and Reiss’s inferentialist account, scrutinising and evaluating them against the purposes they set out and the scope of their applications. On the second mission, I note that there is no such thing as intrinsically ‘being evidence’. Rather, I hold that relevance-mediating vehicles configure data, materials or claims in such ways that some of them are labelled evidence. I identify the relevance-mediating vehicles that the theories of evidence employ. On the final mission, I argue that the likelihoodist account is an appropriate tool for explaining the evidential reasoning in poorly specified settings where likelihoods can be only imprecisely compared. Such settings, I believe, are typical in the historical sciences. Using the reconstruction of proto-sounds in historical linguistics as a case study, I formalise the rationale behind it by means of the law of likelihood. (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)

This paper considers Norton’s Material Theory of Induction. The material theory aims inter alia to neutralize Hume’s Problem of Induction. The purpose of the paper is to evaluate the material theory's capacity to achieve this end. After pulling apart two versions of the theory, I argue that neither version satisfactorily neutralizes the problem.

This paper studies the use of hypotheses schemes in generatinginductive predictions. After discussing Carnap–Hintikka inductive logic,hypotheses schemes are defined and illustrated with two partitions. Onepartition results in the Carnapian continuum of inductive methods, the otherresults in predictions typical for hasty generalization. Following theseexamples I argue that choosing a partition comes down to making inductiveassumptions on patterns in the data, and that by choosing appropriately anyinductive assumption can be made. Further considerations on partitions makeclear that they do not suggest any solution (...) to the problem of induction.Hypotheses schemes provide the tools for making inductive assumptions, but theyalso reveal the need for such assumptions. (shrink)

Pierre Duhem’s influential argument for holism relies on a view of the role that background theory plays in testing: according to this still common account of “auxiliary hypotheses,” elements of background theory serve as truth-apt premises in arguments for or against a hypothesis. I argue that this view is mistaken. Rather than serving as truth-apt premises in arguments, auxiliary hypotheses are employed as “epistemic tools”: instruments that perform specific tasks in connecting our theoretical questions with the world but that are (...) not premises in arguments. On the resulting picture, the acceptability of an auxiliary hypothesis depends not on its truth but on contextual factors such as the task or purpose it is put to and the other tools employed alongside it. (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.

These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018.

We evaluate a common reasoning strategy used in community ecology and comparative psychology for selecting between competing hypotheses. This strategy labels one hypothesis as a “null” on the grounds of its simplicity and epistemically privileges it as accepted until rejected. We argue that this strategy is unjustified. The asymmetrical treatment of statistical null hypotheses is justified through the experimental and mathematical contexts in which they are used, but these contexts are missing in the case of the “pseudo-null hypotheses” found in (...) our case studies. Moreover, statistical nulls are often not epistemically privileged in practice over their alternatives because failing to reject the null is usually a negative result about the alternative, experimental hypothesis. Scientists should eschew the appeal to pseudo-nulls. It is a rhetorical strategy that glosses over a commitment to valuing simplicity over other epistemic virtues in the name of good scientific and statistical methodology. (shrink)

I’m often asked why, as a philosopher of science, I study archaeology. Philosophy is so abstract and intellectual, and archaeology is such an earth-bound, data-driven enterprise, what could the connection possibly be? This puzzlement takes a number of different forms. In one memorable exchange in the late 1970s when I was visiting Oxford as a graduate student an elderly don, having inquired politely about my research interests, tartly observed that archaeology isn’t a science, so I couldn’t possibly be writing a (...) dissertation in philosophy of science on archaeology. At job interviews a few years later a standard opening gambit was to ask: “so what’s philosophical about the work you do on archaeology” or, more querulously, “what could possibly be of philosophical interest in archaeology”? And from archaeologists, weary of the “theory wars” that raged through 1980s and 1990s, the standard question was, “What’s the point?! Better to step away from the wrangling about philosophical ‘isms’ and get on with the empirical work that needs doing.”. (shrink)

Archaeological data are shadowy in a number of senses. Not only are they notoriously fragmentary but the conceptual and technical scaffolding on which archaeologists rely to constitute these data as evidence can be as constraining as it is enabling. A recurrent theme in internal archaeological debate is that reliance on sedimented layers of interpretative scaffolding carries the risk that “preunderstandings” configure what archaeologists recognize and record as primary data, and how they interpret it as evidence. The selective and destructive nature (...) of data capture in archeology further suggests that there may be little scope for putting “legacy” data to work in new ways. And yet archaeologists have been strikingly successful in mining old datasets for new insights. I situate these concerns in the broader context of debate about the epistemic standing of the historical sciences, and then consider three strategies by which archaeologists address the challenges posed by legacy data. The first two – secondary retrieval and recontextualization – are a matter of reconfiguring the scaffolding that underpins evidential reasoning. The third turns on redeploying old data in the context of computational models that support the experimental simulation of the cultural systems and contexts under study. (shrink)

Should philosophers prefer simpler theories? Huemer (Philos Q 59:216–236, 2009) argues that the reasons to prefer simpler theories in science do not apply in philosophy. I will argue that Huemer is mistaken—the arguments he marshals for preferring simpler theories in science can also be applied in philosophy. Like Huemer, I will focus on the philosophy of mind and the nominalism/Platonism debate. But I want to engage with the broader issue of whether simplicity is relevant to philosophy.

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)

One of the most lively debates on scientific understanding is standardly presented as a controversy between the so-called factivists, who argue that understanding implies truth, and the non-factivists whose position is that truth is neither necessary nor sufficient for understanding. A closer look at the debate, however, reveals that the borderline between factivism and non-factivism is not as clear-cut as it looks at first glance. Some of those who claim to be quasi-factivists come suspiciously close to the position of their (...) opponents, the non-factivist, from whom they pretend to differ. The non-factivist, in turn, acknowledges that some sort of ‘answering to the facts’ is indispensable for understanding. This paper discusses an example of convergence of the initially rival positions in the debate on understanding and truth: the use of the same substitute for truth by the quasi-factivist Kareem Khalifa and the non-factivists Henk de Regt and Victor Gijsbers. It is argued that the use of ‘effectiveness’ as a substitute for truth by both parties is not an occasional coincidence of terms, it rather speaks about a deeper similarity which have important implications for understanding the essential features of scientific understanding. (shrink)