If ontic dependence is the basis of explanation, there cannot be mathematical explanations. Accounting for the explanatory dependency between mathematical properties and empirical phenomena poses insurmountable metaphysical and epistemic difficulties, and the proposed amendments to the counterfactual theory of explanation invariably violate core commitments of the theory. Instead, mathematical explanations are either abstract mechanistic constitutive explanations or reconceptualizations of the explanandum phenomenon in which mathematics as such does not have an explanatory role. Explanation-like reasoning within mathematics, distinction between explanatory and nonexplanatory proofs, and comparative judgments of mathematical depth can be fully accounted for by a concept of formal understanding.

Kinds that share historical properties are dubbed “historical kinds” or “etiological kinds,” and they have some distinctive features. I will try to characterize etiological kinds in general terms and briefly survey some previous philosophical discussions of these kinds. Then I will take a closer look at a few case studies involving different types of etiological kinds. Finally, I will try to understand the rationale for classifying on the basis of etiology, putting forward reasons for classifying phenomena on the basis of diachronic features, thereby making a provisional case for considering at least some etiological kinds to be natural kinds.

According to the probabilistic relevance account of confirmation, E confirms H relative to background knowledge K just in case $\mathrm{P}(\mathrm{H}/\mathrm{K}\&\mathrm{E})>\mathrm{P}(\mathrm{H}/\mathrm{K})$. This requires an inequality between the rational degree of belief in H determined relative to two bodies of total knowledge that are such that one (K&E) includes the other (K) as a proper part. In this article, I argue that it is quite plausible that there are no two possible bodies of total knowledge for ideally rational agents meeting this requirement. Hence, the positive relevance account may have to be rejected.

Gender bias is a recalcitrant problem in academia and society. However, dissent has been created on this issue. We focus on dissenting studies by Stephen J. Ceci and Wendy M. Williams, arguing that they reach conclusions that are unwarranted on the basis of the available evidence and that they ignore fundamental objections to their methodological decisions. Drawing on discussions from other contexts, particularly on manufactured dissent concerning anthropogenic climate change, we conclude that dissent on gender bias substantially contributes to (a) the exacerbation of biases in society and (b) an increasing number of attacks on researchers, making it both epistemically and socially problematic.

Confirmation is a graded notion: evidence can confirm a hypothesis to a greater or lesser degree. There has been debate about how to measure degree of confirmation. Starting from the observation that we would like evidence to be a discriminating indicator of truth, we conduct computer simulations to determine how well the various known measures of confirmation predict the extent to which a given piece of evidence fulfills that role, given a hypothesis of interest. The outcomes show that some measures are markedly better indicators of truth than others.

For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many times over, and because computing resources are finite, uncertainty assessment is more feasible using models that demand less computer processor time. Such models are generally simpler in the sense of being more idealized, or less realistic. So modelers face a trade-off between realism and uncertainty quantification. Seeing this trade-off for the important epistemic issue that it is requires a shift in perspective from the established simplicity literature in philosophy of science.

Philosophers of science are increasingly interested in engaging with scientific communities, policy makers, and members of the public; however, the nature of this engagement has not been systematically examined. Instead of delineating a specific kind of engaged philosophy of science, as previous accounts have done, this article draws on literature from outside the discipline to develop a framework for analyzing different forms of broadly engaged philosophy of science according to two key dimensions: social interaction and epistemic integration. Clarifying the many forms of engagement available to philosophers of science can advance future scholarship on engagement and promote more strategic engagement efforts.

An increasing number of arguments for causal pluralism invoke empirical psychological data. Different aspects of causal cognition—specifically, causal perception and causal inference—are thought to involve distinct cognitive processes and representations, and they thereby distinctively support transference and dependency theories of causation, respectively. We argue that this dualistic picture of causal concepts arises from methodological differences, rather than from an actual plurality of concepts. Hence, philosophical causal pluralism is not particularly supported by the empirical data. Serious engagement with cognitive science reveals that the connection between psychological concepts of causation and philosophical notions is substantially more complicated than is traditionally presumed.

Daniel Dennett distinguishes real patterns from bogus patterns by appeal to compressibility. As information theorists have shown, data are compressible if and only if those data exhibit a pattern. Noting that high-level models are much simpler than their low-level counterparts, Dennett interprets high-level models as compressed representations of the fine-grained behavior of their target system. As such, he argues that high-level models depend on patterns in this behavior. Unfortunately, data scientific practice complicates Dennett’s interpretation, undermining the traditional justification for real patterns and suggesting a revised research program for its defenders.

Humans are often altruistic in a variety of contexts, even toward strangers they may never meet again. What explains this behavior? Many argue that kin selection cannot explain it but group selection can. Contra this common line of reasoning, I provide two ways that kin selection might help explain the evolution of broad-scope human altruism: in gene-culture coevolution and in a ‘cultural’ version of kin selection.

This article presents a new argument for incorporating a distinction between default and deviant values into the formalism of causal models. The argument is based on considerations about how causal reasoners should represent disagreement over causes, and it is defended against an objection that has been raised against earlier arguments for defaults.

Sometimes retracted or refuted scientific information is used and propagated long after it is understood to be misleading. Likewise, retracted news items may spread and persist, despite being publicly established as false. In this article, we explore the dynamics of retraction using agent-based models of epistemic networks, to see why false beliefs might persist despite retraction. We find that, often, those who received false information simply fail to receive retractions because of social dynamics. Surprisingly, delaying retraction can increase its impact. Further, retractions are most successful when issued by the original source of misinformation, rather than a separate source.

Early philosophical accounts of explanation mistook the function of boundary conditions for that of contingent facts. I diagnose where this misunderstanding arose and establish that it persists. I disambiguate between two uses of the term “boundary conditions”? and argue that boundary conditions are explanatory via their roles as components of models. Using case studies from fluid mechanics and the physics of waves, I articulate four explanatory functions for boundary conditions in physics: specifying the scope of a model, enabling stable descriptions of phenomena in a model, generating descriptions of novel phenomena, and connecting models from differing theoretical backgrounds.

In cases in which many causes together bring about an effect, it is common to select some as particularly important. Philosophers since Mill have been pessimistic about analyzing this reasoning because of its variability and the multifarious causal and pragmatic details of how it works. I argue Mill was right to think these details matter but wrong that they preclude philosophical analysis of causal selection. I show that analyzing the pragmatic details of scientific debates about the important causes of the Bhopal Gas Tragedy can illuminate causal reasoning about disasters and shed new light on causality and causal selection.

Researchers often pursue proof of concept research, but criteria for evaluating such research remain poorly specified. This article proposes a general framework for proof of concept research that knits together and augments earlier discussions. The framework includes prototypes, proof of concept demonstrations, and post facto demonstrations. With a case from theoretical evolutionary genetics, the article illustrates the general framework and articulates some of the reasoning strategies used within that field. This article provides both specific tools with which to understand how researchers evaluate models in theoretical evolutionary genetics and general tools that apply to proof of concept research more generally.

In the causal modeling literature, it is well known that ill-defined variables may give rise to ambiguous manipulations. Here, we illustrate how ill-defined variables may also induce mistakes in causal inference when standard causal search methods are applied. To address the problem, we introduce a representation framework, which exploits an independent component representation of the data, and demonstrate its potential for detecting ill-defined variables and avoiding mistaken causal inferences.

In “Resurrecting Biological Essentialism,” I went against the consensus in the philosophy of biology by arguing that a Linnaean taxon, including a species, has an essence that is, at least partly, an underlying intrinsic, mostly genetic, property: this intrinsic nature explains both the truth of generalizations about the phenotypic properties of the taxon and why being in the taxon is explanatory. The current article is a response to two criticisms: that this intrinsic essentialism is at odds with certain biological variations and that this talk of intrinsic essences is an uncalled for metaphysical addition to biology.

Many policy decisions take input from collections of scientific models. Such decisions face significant and often poorly understood uncertainty. We rework the so-called confidence approach to tackle decision-making under severe uncertainty with multiple models, and we illustrate the approach with a case study: insurance pricing using hurricane models. The confidence approach has important consequences for this case and offers a powerful framework for a wide class of problems. We end by discussing different ways in which model ensembles can feed information into the approach, appropriate to different collections of models.

We investigate the fate of determinism in general relativity (GR), comparing the philosopher’s account with the physicist’s well-posed initial value formulations. The fate of determinism is interwoven with the question of what it is for a spacetime to be ‘physically reasonable’. A central concern is the status of global hyperbolicity, a putatively necessary condition for determinism in GR. While global hyperbolicity may fail to be true of all physically reasonable models, we analyze whether global hyperbolicity should be (i) imposed by fiat; (ii) established from weaker assumptions, as in cosmic censorship theorems; or (iii) justified by beyond-GR physics.