Much of the literature on values in science is limited in its perspective because it focuses on the role of values in individual scientists’ decision making, thereby ignoring the context of scientific collaboration. I examine the epistemic structure of scientific collaboration and argue that it gives rise to two arguments showing that moral and social values can legitimately play a role in scientists’ decision to accept something as scientific knowledge. In the case of scientific collaboration some moral and social values are properly understood to be extrinsic epistemic values, that is, values that promote the attainment of scientific knowledge.

Several authors have argued that causes differ in the degree to which they are ‘specific’ to their effects. Woodward has used this idea to enrich his influential interventionist theory of causal explanation. Here we propose a way to measure causal specificity using tools from information theory. We show that the specificity of a causal variable is not well defined without a probability distribution over the states of that variable. We demonstrate the tractability and interest of our proposed measure by measuring the specificity of coding DNA and other factors in a simple model of the production of mRNA.

Much of Stephen Jay Gould’s legacy is dominated by his views on the contingency of evolutionary history expressed in his classic Wonderful Life. However, Gould also campaigned relentlessly for a “nomothetic” paleontology. How do these commitments hang together? I argue that Gould’s conception of science and natural law combined with his commitment to contingency to produce an evolutionary science centered around the formulation of higher-level evolutionary laws.

Two approaches to evidential reasoning compete in the biomedical and social sciences: the experimental and the pragmatist. Whereas experimentalism has received considerable philosophical analysis and support since the times of Bacon and Mill (and continues to enjoy attention and support in very recent work on causation and evidence), pragmatism about evidence has been neither articulated nor defended. The overall aim is to fill this gap and develop a theory that articulates the latter. The main ideas of the theory will be illustrated and supported by a case study on the smoking/lung cancer controversy in the 1950s.

This article presents an account of the biological populations that can undergo paradigmatic natural selection. I argue for, and develop Peter Godfrey-Smith’s claim that reproductive competition is a core attribute of such populations. However, as Godfrey-Smith notes, it is not the only important attribute. I suggest what the missing element is, co-opting elements of Alan Templeton’s notion of exchangeability. The final framework is then compared to two recent discussions regarding biological populations proposed by Roberta Millstein and Jacob Stegenga.

Using Gebharter’s representation, we consider aspects of the problem of discovering the structure of unmeasured submechanisms when the variables in those submechanisms have not been measured. Exploiting an early insight of Sober’s, we provide a correct algorithm for identifying latent, endogenous structure—submechanisms—for a restricted class of structures. The algorithm can be merged with other methods for discovering causal relations among unmeasured variables, and feedback relations between measured variables and unobserved causes can sometimes be learned.

A long-running dispute concerns which adaptation-related explananda natural selection can be said to explain. (The issue is conceptual—not empirical—and orthogonal adaptationism.) At issue are explananda of the form: why a given individual organism has a given adaptation rather than that same individual having another trait. It is broadly agreed that one must be ready to back up a “no” answer with an appropriate theory of trans-world identity for individuals. I argue, against the conventional wisdom, that the same is true for a “yes” answer. My conclusion recasts the landscape and opens the door to a potential resolution.

Analogies between Newtonian mechanics and evolutionary processes are powerful but not infinitely versatile tools for generating explanations of particular biological phenomena. Their explanatory range is sensitive to a preliminary decision about which processes count as background conditions and which as special forces. Here I argue that the defenders of the zero-force evolutionary law are mistaken in defending their decision as the only appropriate one. The Hardy–Weinberg principle remains a viable option that is consistent with the epistemic role of Newton’s own first law, and the strengths and weaknesses of each analogy are sufficiently distinct to justify their continued coexistence.

A primary goal of quantum computer science is to find an explanation for the fact that quantum computers are more powerful than classical computers. In this paper I argue that to answer this question is to compare algorithmic processes of various kinds and to describe the possibility spaces associated with these processes. By doing this, we explain how it is possible for one process to outperform its rival. Further, in this and similar examples little is gained in subsequently asking a how-actually question. Once one has explained how-possibly, there is little left to do.

Kaplan and Craver claim that all explanations in neuroscience appeal to mechanisms. They extend this view to the use of mathematical models in neuroscience and propose a constraint such models must meet in order to be explanatory. I analyze a mathematical model used to provide explanations in dynamical systems neuroscience and indicate how this explanation cannot be accommodated by the mechanist framework. I argue that this explanation is well characterized by Batterman’s account of minimal model explanations and that it demonstrates how relationships between explanatory models in neuroscience and the systems they represent is more complex than has been appreciated.

One of the most troubling and persistent challenges for Bayesian Confirmation Theory is the Problem of Old Evidence (POE). The problem arises for anyone who models scientific reasoning by means of Bayesian Conditionalization. This article addresses the problem as follows: First, I clarify the nature and varieties of the POE and analyze various solution proposals in the literature. Second, I present a novel solution that combines previous attempts while making weaker and more plausible assumptions. Third and last, I summarize my findings and put them into the context of the general debate about POE and Bayesian reasoning.

This article develops and defends an account of inference to the best explanation according to which it first and foremost justifies pursuing hypotheses rather than accepting them as true. This sidesteps the issue of why better explanations should be more likely to be true. I defend an account of justification for pursuit, inspired by Peirce’s mature account of abduction, and develop it as a formal decision-theoretic model. This account provides a straightforward connection between explanatoriness and justification for pursuit.

Recently, biologists have argued that data-driven biology fosters a new scientific methodology, namely, one that is irreducible to traditional methodologies of molecular biology defined as the discovery strategies elucidated by mechanistic philosophy. Here I show how data-driven studies can be included in the traditional mechanistic approach in two respects. On the one hand, some studies provide eliminative inferential procedures to prioritize and develop mechanistic hypotheses. On the other, different studies play an exploratory role in providing useful generalizations to complement the procedure of prioritization. Overall, this article aims to shed light on the structure of contemporary research in molecular biology.

In a recent paper, Belot argues that Bayesians are epistemologically flawed because they believe with probability 1 that they will learn the truth about observational propositions in the limit. While Belot’s considerations suggest that this result should be interpreted with some care, the concerns he raises can largely be defused by putting convergence to the truth in the context of learning from an arbitrarily large but finite number of observations.

The concordance of results that are “robust” across multiple scientific modalities is widely considered to play a critical role in the epistemology of science. But what should we make of those cases where such multimodal evidence is discordant? Jacob Stegenga has recently argued that robustness is “worse than useless” in these cases, suggesting that “different kinds of evidence cannot be combined in a coherent way.” In this article I respond to this critique and illustrate the critical methodological role that robustness plays as an aim of scientific inquiry.

Causal relations among components and activities are intentionally misrepresented in mechanistic explanations found routinely across the life sciences. Since several mechanists explicitly advocate accurately representing factors that make a difference to the outcome, these idealizations conflict with the stated rationale for mechanistic explanation. We argue that these idealizations signal an overlooked feature of reasoning in molecular and cell biology—mechanistic explanations do not occur in isolation—and suggest that explanatory practices within the mechanistic tradition share commonalities with model-based approaches prevalent in population biology.

Chance Neutrality is the thesis that, conditional on some proposition being true (or being false), its chance of being true should be a matter of practical indifference. The aim of this article is to examine whether Chance Neutrality is a requirement of rationality. We prove that given Chance Neutrality, the Principal Principle entails a thesis called Linearity; the centerpiece of von Neumann and Morgenstern’s expected utility theory. With this in mind, we argue that the Principal Principle is a requirement of practical rationality but that Linearity is not and, hence, that Chance Neutrality is not rationally required.

Understanding the dynamics of information is crucial to many areas of research, both inside and outside of philosophy. Using computer simulations of three kinds of information, germs, genes, and memes, we show that the mechanism of information transfer often swamps network structure in terms of its effects on both the dynamics and the fitness of the information. This insight has both obvious and subtle implications for a number of questions in philosophy, including questions about the nature of information, whether there is genetic information, and how to arrange scientific communities.

This article has both substantive and methodological goals. Methodologically, it shows that rational choice theory (in its behavioral ecology form) is an especially important tool for guiding research in contexts in which agents appear to be acting against their best interests. The Neolithic transition is one such case, and the article develops a substantive conception of that transition, illustrating the heuristic power of behavioral ecology.