Many philosophers have challenged the ideal of value-free science on the grounds that social or moral values are relevant to inferential thresholds. But given this view, how precisely and to what extent should scientists adjust their inferential thresholds in light of nonepistemic values? We suggest that signal detection theory provides a useful framework for addressing this question. Moreover, this approach opens up further avenues for philosophical inquiry and has important implications for philosophical debates concerning inductive risk. For example, the signal detection theory framework entails that considerations of inductive risk and inferential-threshold placement cannot be conducted in isolation from base-rate information.

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 (especially his answer to the Problem of Induction) 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.

To systematically answer the generalized Kretschmann objection, I propose a mean to make operational a criterion widely recognized as allowing one to decide whether the gauge symmetry of a theory is artificial or substantial. My proposition is based on the dressing field method of gauge symmetry reduction, a new simple tool from mathematical physics. This general scheme allows one in particular to straightforwardly argue that the notion of spontaneous symmetry breaking is superfluous to the empirical success of the electroweak theory. Important questions regarding the context of justification of the theory then arise.

There is a fairly long-standing distinction between what are called the ideographic as opposed to nomothetic sciences. The nomothetic sciences, such as physics, offer explanations in terms of the laws and regular operations of nature. The ideographic sciences, such as natural history (or, more controversially, evolutionary biology), cast explanations in terms of narratives. This article offers an account of what is involved in offering an explanatory narrative in the historical (ideographic) sciences. I argue that narrative explanations involve two chief components: a possibility space and an explanatory causal mechanism.

Reductionists say things like all mental properties are physical properties; all normative properties are natural properties. I argue that the only way to resist reductionism is to deny that causation is difference making (thus making the epistemology of causation a mystery) or to deny that properties are individuated by their causal powers (thus making properties a mystery); that is to say, unless one is happy to deny supervenience or to trivialize the debate over reductionism. To show this, I argue that if properties are individuated by their causal powers, then, surprisingly, properties are individuated by necessary coexemplification.

Scientists often study hypotheses that they know to be false. This creates an interpretive problem for Bayesians because the probability assigned to a hypothesis is typically interpreted as the probability that the hypothesis is true. I argue that solving the interpretive problem requires coming up with a new semantics for Bayesian inference. I present and contrast two new semantic frameworks, and I argue that both of them support the claim that there is pervasive pragmatic encroachment on whether a given Bayesian probability assignment is rational.

I analyze the exploratory function of two main modeling practices: targetless fictional models and hypothetical perspectival models. In both cases, I argue, modelers invite us to imagine or conceive something about the target system, which is known to be either nonexistent (fictional models) or just hypothetical (in perspectival models). I clarify the kind of imagining or conceiving involved in each modeling practice, and I show how each—in its own right—delivers important modal knowledge. I illustrate these two kinds of exploratory models with Maxwell’s ether model and supersymmetric particle models at the Large Hadron Collider.

The replicator dynamics and Moran process are the main deterministic and stochastic models of evolutionary game theory. The models are connected by a mean-field relationship—the former describes the expected behavior of the latter. However, there are conditions under which their predictions diverge. I demonstrate that the divergence between their predictions is a function of standard techniques used in their analysis and of differences in the idealizations involved in each. My analysis reveals problems for stochastic stability analysis in a broad class of games, demonstrates a novel domain of agreement between the dynamics, and indicates a broader moral for evolutionary modeling.

The Hong and Page ‘diversity trumps ability’ result has been used to argue for the more general claim that a diverse set of agents is epistemically superior to a comparable group of experts. Here we extend Hong and Page’s model to landscapes of different degrees of randomness and demonstrate the sensitivity of the ‘diversity trumps ability’ result. This analysis offers a more nuanced picture of how diversity, ability, and expertise may relate. Although models of this sort can indeed be suggestive for diversity policies, we advise against interpreting such results overly broadly.

Shuford, Albert, and Massengill proved, a half century ago, that the logarithmic scoring rule is the only proper measure of inaccuracy determined by a differentiable function of probability assigned the actual cell of a scored partition. In spite of this, the log rule has gained less traction in applied disciplines and among formal epistemologists that one might expect. In this article we show that the differentiability criterion in the Shuford et al. result is unnecessary and use the resulting simplified characterization of the logarithmic rule to give novel arguments in favor of it.

In the current article and contrary to a widespread assumption, I argue that Humeanism and ontological emergence can peacefully coexist. Such a coexistence can be established by reviving elements of John Stuart Mill’s philosophy of science, in which an idiosyncratic account of diachronic emergence is associated with extensions of the Humean mosaic and the correlative coming into being of new best system laws, which have the peculiarity of being temporally indexed. Incidentally, this reconciliation of Humeanism and emergence allows for conceiving the autonomy of the special sciences in an interesting way, consistently with the reductionist ideal of a unified science.

I propose a general alethic theory of epistemic risk according to which the riskiness of an agent’s credence function encodes her relative sensitivity to different types of graded error. After motivating and mathematically developing this approach, I show that the epistemic risk function is a scaled reflection of expected inaccuracy (a quantity also known as generalized information entropy). This duality between risk and information enables us to explore the relationship between attitudes to epistemic risk, the choice of scoring rules in epistemic utility theory, and the selection of priors in Bayesian epistemology more generally (including the Laplacean principle of indifference).

When scientists are asked to give expert advice on risk-related questions, such as the authorization of medical drugs, deliberation often does not eliminate all disagreements. I propose to model these remaining discrepancies as differences in risk assessments and/or in risk acceptability thresholds. The normative question I consider, then, is how the individual expert views should best be aggregated. I discuss what “best” could mean, with an eye to some robustness considerations. I argue that the majority rule, which is currently often used in expert panels, has significant drawbacks.

According to mainstream linguistic phonetics, speech can be modeled as a string of discrete sound segments or “phones” drawn from a universal phonetic inventory. Recent work has argued that a mature phonetics should refrain from theorizing about speech and speech processing using sound segments and that the phone concept should be eliminated from linguistic theory. The article lays out the tenets of the phone methodology and evaluates its prospects in light of the eliminativist arguments. I claim that the eliminativist arguments fail to show that the phone concept should be eliminated from linguistic theory.

This essay considers a mixed-effects modeling practice and its implications for the philosophical debate surrounding reductive explanation. Mixed-effects modeling is a species of the multilevel modeling practice, where a single model incorporates simultaneously two (or even more) levels of explanatory variables to explain a phenomenon of interest. I argue that this practice makes the position of explanatory reductionism held by many philosophers untenable because it violates two central tenets of explanatory reductionism: single-level preference and lower-level obsession.

Philosophers of science discuss whether theory selection depends on aesthetic judgments or criteria and whether these putatively aesthetic features are genuinely extra-epistemic. As examples, judgments involving criteria such as simplicity and symmetry are often cited. However, other theory selection criteria, such as fecundity, coherence, internal consistency, and fertility, more closely match those criteria used in art contexts and by scholars working in aesthetics. Paying closer attention to the way these criteria are used in art contexts allows us to understand some evaluative and developmental practices in scientific theory selection as genuinely aesthetic, enlarging the scope of the goals of science.

This article considers the thesis that a more proportional relationship between a cause and its effect yields a more abstract causal explanation of that effect, thereby producing a deeper explanation. This thesis has important implications for choosing the optimal granularity of explanation for a given explanandum. In this article, I argue that this thesis is not generally true of probabilistic causal relationships. In light of this finding, I propose a pragmatic measure of explanatory depth. This measure uses a decision-theoretic model of information pricing to determine the optimal granularity of explanation for a given explanandum, agent, and decision problem.

This article develops and defends a new functional approach to scientific progress. I begin with a review of the problems of the traditional functional approach. Then I propose a new functional account of scientific progress, in which scientific progress is defined in terms of usefulness of problem defining and problem solving. I illustrate and defend my account by applying it to the history of genetics. Finally, I highlight the advantages of my new functional approach over the epistemic and semantic approaches and dismiss some potential objections to my approach.

Current debates about the replication crisis in psychology take it for granted that direct replication is valuable, largely focusing on its role in uncovering questionable statistical practices. This article takes a broader look at the notion of replication in psychological experiments. It is argued that all experimentation/replication involves individuation judgments and that research in experimental psychology frequently turns on probing the adequacy of such judgments. In this vein, I highlight the ubiquity of conceptual and material questions in research, arguing that replication has its place but is not as central to psychological research as it is sometimes taken to be.

This article contributes to the underdeveloped field of experimental philosophy of science. We examine variability in the philosophical views of scientists. Using data from Toolbox Dialogue Initiative, we analyze scientists’ responses to prompts on philosophical issues (methodology, confirmation, values, reality, reductionism, and motivation for scientific research) to assess variance in the philosophical views of physical scientists, life scientists, and social and behavioral scientists. We find six prompts about which differences arose, with several more that look promising for future research. We then evaluate the difference between the natural and social sciences and the challenge of interdisciplinary integration across scientific branches.