There is a major debate as to whether there are non-causal mathematical explanations of physical facts that show how the facts under question arise from a degree of mathematical necessity considered stronger than that of contingent causal laws. We focus on Marc Lange’s account of distinctively mathematical explanations to argue that purported mathematical explanations are essentially causal explanations in disguise and are no different from ordinary applications of mathematics. This is because these explanations work not by appealing to what the world must be like as a matter of mathematical necessity but by appealing to various contingent causal facts.

This article advances the development of a phylogeny-based psychology in which cognitive ability types are individuated as characters in the evolutionary biological sense. I explain the character concept and its utility in addressing (or dissolving) conceptual problems arising from discoveries of cognitive abilities across a wide range of species. I use the examples of stereopsis in the praying mantis, internal cell-to-cell signaling in plants, and episodic memory in scrub jays to show how anthropocentric cognitive ability types can be reformulated into cognitive characters, thereby promoting the integration of psychology with other sciences of evolved traits.

The developmental properties of organisms play important roles in the generation of variation necessary for evolutionary change. But how can individual development steer the course of evolution? To answer this question, we introduce developmental channeling as a disposition of individual organisms that shapes their possible developmental trajectories and evolutionary dappling as an evolutionary outcome in which the space of possible organismic forms is dappled—it is only partially filled. We then trace out the implications of the channeling-dappling framework for contemporary debates in the philosophy of evolution, including evolvability, reciprocal causation, and the extended evolutionary synthesis.

This paper investigates the question of how subjective probability should relate to binary belief. We propose new distance minimization methods, and develop epistemic decision-theoretic accounts. Both approaches can be shown to get “close” to the truth: the first one by getting “close” to a given probability, and the second by getting expectedly “close” to the truth. More specifically, we study distance minimization with a refined notion of Bregman divergence and expected utility maximization with strict proper scores. Our main results reveal that the two ways to get “close” to the truth can coincide.

This paper proposes a novel view in the the philosophy of race & causation literature known as “causal agnosticism” about race. Causal agnosticism about race implies that it is reasonable to refrain from making judgments about whether race is a cause. The paper’s thesis asserts that certain conditions must be met to infer that something is a cause, according to the fundamental assumptions of causal inference. However, in the case of race, these conditions are often violated. By advocating for causal agnosticism, the paper suggests a more modest approach to understanding the role of race in causal relationships.

In recent debates about the replication crisis, two positions have been dominant: one that focuses on methodological reforms and one that focuses on theory building. This paper takes up the suggestion that there might be a deeper difference in play, concerning the ways the very subject matter of psychology is construed by opposing camps, i.e., in terms of stable effects versus in terms of complexity. I argue that each gets something right, but neither is sufficient. My analysis suggests that the context sensitivity of the psychological subject matter needs to be front and center of methodological and theoretical efforts.

Bayesian agents, argues Belot, are orgulous: they believe in inductive success even when guaranteed to fail on a topologically typical collection of data streams. Here we shed light on how pervasive this phenomenon is. We identify several classes of inductive problems for which Bayesian convergence to the truth is topologically typical. However, we also show that, for all sufficiently complex classes, there are inductive problems for which convergence is topologically atypical. Last, we identify specific topologically typical collections of data streams, observing which guarantees convergence to the truth across all problems from certain natural classes of effective inductive problems.

Imagine that we are on a train, playing with some mechanical systems. Why can’t we detect any differences in their behavior when the train is parked versus when it is moving uniformly? The standard answer is that boosts are symmetries of Newtonian systems. In this article, I use the case of a spring to argue that this answer is problematic because symmetries are neither sufficient nor necessary for preserving its behavior. I also develop a new answer according to which boosts preserve the relational properties on which the behavior of a system depends, even when they are not symmetries.

Confirmation bias has been widely studied for its role in failures of reasoning. Individuals exhibiting confirmation bias fail to engage with information that contradicts their current beliefs, and, as a result, can fail to abandon inaccurate beliefs. But although most investigations of confirmation bias focus on individual learning, human knowledge is typically developed within a social structure. We use network models to show that moderate confirmation bias often improves group learning. However, a downside is that a stronger form of confirmation bias can hurt the knowledge-producing capacity of the community.

Two types of formal models—landscape search tasks and two-armed bandit models—are often used to study the effects that various social factors have on epistemic performance. I argue that they can be understood within a single framework. In this unified framework, I develop a model that may be used to understand the effects of functional and demographic diversity and their interaction. Using the unified model, I find that the benefit of demographic diversity is most pronounced in a functionally homogeneous group, and decreases with increasing functional diversity.

Imitating nature is an ever more popular strategy in many fields of science and engineering research, from ecological engineering to artificial intelligence. But while biomimetics and related fields have recently attracted increased attention from philosophers, there has been relatively little engagement with what I suggest we see as their basic epistemological presupposition: that we may acquire knowledge from nature. I argue that emphasizing and exploring this presupposition opens up a new approach to epistemology, based on a shift from a conventional epistemological relationship to nature as object of knowledge to a biomimetic relationship to nature as source of knowledge.

An influential heuristic for thinking about climate adaptation asserts that “natural” adaptation strategies are the best ones. This heuristic has been roundly criticized but is difficult to dislodge in the absence of an alternative. We introduce a new heuristic that assesses adaptation strategies by looking at their maturity, power, and commitment. Maturity is the extent to which we understand an adaptation strategy’s effects. Power is the size of the effect an adaptation strategy will have. Commitment is the degree to which an adaptation strategy is difficult to test or reverse.

Well-known debates among statistical inferential paradigms emerge from conflicting views on the notion of probability. One dominant view understands probability as a representation of sampling variability; another prominent view understands probability as a measure of belief. The former generally describes model parameters as fixed values, in contrast to the latter. We propose that there are actually two versions of a parameter within both paradigms: a fixed unknown value that generated the data and a random version to describe the uncertainty in estimating the unknown value. An inferential approach based on CDs deciphers seemingly conflicting perspectives on parameters and probabilities.

This paper examines constraints and their role in scientific explanation. Common views in the philosophical literature suggest that constraints are non-causal and that they provide non-causal explanations. While much of this work focuses on examples from physics, this paper explores constraints from other fields, including neuroscience, physiology, and the social sciences. I argue that these cases involve constraints that are causal and that provide a unique type of causal explanation. This paper clarifies what it means for a factor to be a constraint, when such constraints are causal, and how they figure in scientific explanation.

We challenge the standard interpretation of pain asymbolia (PA), a neuropsychiatric condition that causes unusual reactions to pain stimuli. The standard interpretation asserts that PA subjects experience pain but lack important features of the experience. However, we argue that the clinical evidence for PA does not support this interpretation and that the arguments put forward by the defenders of the standard interpretation end up making self-contradicting claims. Finally, we suggest that the best interpretation of the available evidence is to take a deflationist stance toward PA, at least until further evidence becomes available.

Fitness has taken center stage in debates concerning how best to identify evolutionary transitions in individuality (ETIs). An influential framework proposes that an ETI occurs only when fitness is exported from constituent particles to a collective. We reformulate the conceptual structure of this framework as involving three steps. The culminating step compares “counterfactual” fitnesses against a long-run measure of fitness. This comparison assumes that collective-level fitness mereologically supervenes on particle fitness. However, if this assumption is rigorously enforced, the proposed conditions for identifying ETIs prove to be too weak. We here suggest an alternative model of ETIs centered around traits.

We propose a novel account of evolutionary transitions in individuality as life cycle closure: that is, the emergence of a new embedding life cycle. To characterize this process, we show how the life trajectory of lower-level entities (e.g., cells) can be coarse-grained into classes of a higher-level entity. We argue that only higher-level entities displaying two necessary conditions for the existence of a life cycle (e.g., multicellular organisms) have achieved life cycle closure. Throughout, we illustrate our point with stage-structured demographic models that yield a rigorous characterization of the conditions for life cycle closure.

Recent developments in generalized probability theory have renewed a debate about whether regularity (i.e., the constraint that only logical contradictions get assigned probability $0$) should be a necessary feature of both chances and credences. Crucial to this debate, however, are some mathematical facts regarding the interplay between the existence of regular generalized probability measures and various cardinality assumptions. We improve on several known results in the literature regarding the existence of regular generalized probability measures. In particular, we give necessary and sufficient conditions for the existence of regular generalized probability measures defined on the whole powerset of any sample space.

Consensus reporting is valuable for presenting unified scientific evidence to the public. When a consensus does not exist, I argue that scientists ought not to default to majority reporting in its place. Majority reporting has several epistemic drawbacks because it can obscure underlying justifications and lines of evidence, which may be in conflict or contested. I argue that minority reporting, in conjunction with majority reporting, is an epistemically superior mechanism for scientists to report on the full range of reasons and evidence available within a group. This article addresses several objections, including worries over group cohesion, fringe reporting, and elite capture.

There is a mysterious twofold change in Du Châtelet’s position on Newtonian attraction: from acceptance thereof as an explanatory principle in Essai sur l’optique (ca. 1738–39), to rejection in the 1740 Institutions, and returning to acceptance again in her Commentary (1756) to Newton’s Principia. In this article, I suggest that we turn to the 1742 Institutions for answers. There, Du Châtelet introduces physical explanation and maintains that we can appeal to certain physical qualities (such as attraction) for explanatory force. Using this case study, I argue that the scholarship will benefit greatly from turning to the 1742 edition going forward.