Most attempts to justify the use of idealized models to explain appeal to the accuracy of the model with respect to difference-making causes. In this article, I argue for an alternative way to justify using idealized models to explain that appeals to universality classes. In support of this view, I show that scientific modelers seeking to explain stable limiting behaviors often explicitly appeal to universality classes in order to justify their use of idealized models to explain.

This article examines the multiple realizability thesis within a causal framework. The beginnings of this framework are found in Elliott Sober’s “Multiple Realizability Argument against Reduction,” which argues that the multiple realizability thesis poses no challenge to reductive explanation. While Sober’s causal approach has the potential to reveal new insights, I argue that his setup fails to capture important aspects of the multiple realizability thesis. After correcting for these issues, I argue that this causal framework reveals something quite different. It reveals how multiple realizability relates to a common type of causal complexity in biology that poses problems for reductive explanation.

In statistics, there are two main paradigms: classical and Bayesian statistics. The purpose of this article is to investigate the extent to which classicists and Bayesians can (in some suitable sense of the word) agree. My conclusion is that, in certain situations, they cannot. The upshot is that, if we assume that the classicist is not allowed to have a higher degree of belief (credence) in a null hypothesis after he has rejected it than before, then (in certain situations) he has to either have trivial or incoherent credences to begin with or fail to update his credences by conditionalization.

Wright’s “adaptive landscape” has been influential in evolutionary thinking but controversial, especially because the landscape that organisms encounter is altered by the evolutionary process itself and the effects organisms have on their environments. Lewontin offered a mathematical heuristic describing the coupling of niche construction and adaptive evolution. Here, we propose a “dual landscape” model to view these relationships. Our model represents change as simultaneous movement on two landscapes, each a function of phenotype and environment. This model clarifies the evolutionary feedback generated by niche construction. We relate our model to Lewontin’s niche construction equations and illustrate it with three examples.

Binding specificity is a centrally important concept in molecular biology, yet it has received little philosophical attention. Here I aim to remedy this by analyzing binding specificity as a causal property. I focus on the concept’s role in drug design, where it is highly prized and hence directly studied. From a causal perspective, understanding why binding specificity is a valuable property of drugs contributes to an understanding of causal selection—of how and why scientists distinguish between causes, not just causes from noncauses. In particular, the specificity of drugs is precisely what underwrites their value as experimental interventions on biological processes.

History is littered with scientifically ill-founded claims about human nature. They frequently appear in normative contexts, projecting ideology or values onto nature (what we call the naturalizing error). In considering a remedy, we adopt a naturalized epistemology approach to how we think about human nature. The “nature” in “human nature” fosters unproductive essentialist thinking, epitomized in the adage “a tiger cannot change its stripes.” Universalist, fixist, and teleological perspectives each erode epistemic reasoning and blur the distinction between normative and descriptive justification. We articulate strategies to guide more responsible claims about human nature in science and science communication.

Thomas Polger and Lawrence Shapiro claim that unlike human-made artifacts cases of multiple realization in naturally occurring systems are uncommon. Drawing on cases from systems biology, I argue that multiple realization in naturally occurring systems is not as uncommon as Polger and Shapiro initially thought. The relevant cases, which I draw from systems biology, involve generalizable design principles called network motifs, which recur in different organisms and species and perform specific functions. I show that network motifs with entirely different underlying causal structures can perform the same function of interest. The article also considers the scope problem of multiple realization.

A common strategy for simplifying complex systems involves partitioning them into subsystems whose behaviors are roughly independent of one another at shorter timescales. Dynamic causal models clarify how doing so reveals a system’s nonequilibrium causal relationships. Here I use these models to elucidate the idealizations and abstractions involved in representing a system at a timescale. The models reveal that key features of causal representations—such as which variables are exogenous—may vary with the timescale at which a system is considered. This has implications for debates regarding which systems can be represented causally.

I evaluate the plausibility of explanatory elitism, the view that a good scientific explanation of an outcome will show that it was highly probable. I consider an argument from Michael Strevens that elitism is the only view that can account for the historical acceptance of probabilistic theories in physics. I argue that biology provides better test cases for evaluating elitism and conclude that theories in that domain were favored in virtue of conferring correct, and not necessarily high, probabilities on outcomes.

Why are conditional degrees of belief in an observation E, given a statistical hypothesis H, aligned with the objective probabilities expressed by H? After showing that standard replies (ratio analysis of conditional probability, chance-credence coordination) are not satisfactory, I develop a suppositional analysis of conditional degree of belief, transferring Ramsey’s classical proposal to statistical inference. The analysis saves the alignment, explains the role of chance-credence coordination, and rebuts the charge of arbitrary assessment of evidence in Bayesian inference. Finally, I explore the implications of this analysis for Bayesian reasoning with idealized models in science.

Substantive counterlegal discourse poses a problem for those according to whom the laws of nature are metaphysically necessary. I discern two types of necessitarianism about laws: dispositional essentialism and modal necessitarianism. I argue that Toby Handfield’s response to the problem of counterlegals cannot help the modal necessitarian, according to whom all possible worlds are identical with respect to the laws. I thus propose a fictionalist treatment of counterlegals. Fictions are not limited by metaphysical possibility; hence, fictionalism affords the modal necessitarian the means to account for the apparent substance of counterlegals even granting the metaphysical necessity of the laws.

I argue that the human mind includes an innate domain-specific system for representing precise small numerical quantities. This theory contrasts with object-tracking theories and with domain-general theories that only make use of mental models. I argue that there is a good amount of evidence for innate representations of small numerical quantities and that such a domain-specific system has explanatory advantages when infants’ poor working memory is taken into account. I also show that the mental models approach requires previously unnoticed domain-specific structure and consequently that there is no domain-general alternative to an innate domain-specific small number system.

This article defends the notion of downward causation, relating it to a notion of conditional independence.

We report the results of a study that investigated the views of researchers working in seven scientific disciplines and in history and philosophy of science in regard to four hypothesized dimensions of scientific realism. Among other things, we found (i) that natural scientists tended to express more strongly realist views than social scientists, (ii) that history and philosophy of science scholars tended to express more antirealist views than natural scientists, (iii) that van Fraassen’s characterization of scientific realism failed to cluster with more standard characterizations, and (iv) that those who endorsed the pessimistic induction were no more or less likely to endorse antirealism.

Computer scientists have provided a distinct justification of Occam’s Razor. Using the probably approximately correct framework, they provide a theorem that they claim demonstrates that we should favor simpler hypotheses. The argument relies on a philosophical interpretation of the theorem. I argue that the standard interpretation of the result in the literature is misguided and that a better reading does not, in fact, support Occam’s Razor at all. To this end, I state and prove a very similar theorem that, if interpreted the same way, would justify the contradictory Anti-Occam’s Razor—the principle that we should favor more complex hypotheses.

Kitcher has proposed an ideal-theory account—well-ordered science (WOS)—of the collective good that science’s research agenda should promote. Against criticism regarding WOS’s action guidance, Kitcher has advised critics not to confuse substantive ideals and the ways to arrive at them, and he has defended WOS as a necessary and useful ideal for science policy. I provide a distinction between two types of ideal theories that helps clarifying WOS’s elusive nature. I use this distinction to argue that the action-guidance problem that WOS faces remains even under the aims/means distinction because the WOS’s failure is more basic than critics have suggested.

Children acquire complex concepts like dog earlier than simple concepts like brown, even though our best neuroscientific theories suggest that learning the former is harder than learning the latter and, thus, should take more time (Markus Werning). This is the complex-first paradox. We present a novel solution to the complex-first paradox. Our solution builds on a generalization of Fei Xu and Joshua B. Tenenbaum’s Bayesian model of word learning. By focusing on a rational theory of concept learning, we show that it is easier to infer the meaning of complex concepts than that of simple concepts.

I propose a framework for pragmatic reliability in-the-limit criteria, extending the epistemic reliability framework. I identify some common scientific contexts that complicate the application or interpretation of epistemic reliability criteria, drawing heavily from economics for illustrative examples. I then propose an extension of the standard framework, where inquiry is constrained by both epistemic and nonepistemic factors. This provides analogous notions of pragmatic underdetermination and pragmatic reliability with respect to a particular goal, as well as a principled method for extracting solvable problems from unsolvable ones.

Reflection and martingale principles are central to models of rational learning. They can be justified in a variety of ways. In what follows we study martingale and reflection principles in the context of measure theory. We give special attention to two approaches for justifying these principles that have not been studied in that context before: diachronic coherence and the value of information. Together with an extant argument based on expected accuracy, these arguments lend support to the thesis that reflection and martingale principles govern rational learning.