Eliminative reasoning seems to play an important role in the sciences, but should it be part of our best theory of science? Statistical evidence, prevalent across the sciences, causes problems for eliminative inference, supporting the view that probabilistic theories of confirmation provide a better framework for reasoning about evidence. Here I argue that deductive elimination has an important inferential role to play in science, one that is compatible with probabilistic approaches to evidence. Eliminative inferences help frame testing problems, an essential step that determines the context for evaluating statistical evidence. I illustrate this process with examples from molecular evolutionary biology.

This article argues that the basic account of mechanism and mechanistic explanation, involving sequential execution of qualitatively characterized operations, is itself insufficient to explain biological phenomena such as the capacity of living organisms to maintain themselves as systems distinct from their environment. This capacity depends on cyclic organization, including positive and negative feedback loops, which can generate complex dynamics. Understanding cyclically organized mechanisms with complex dynamics requires coordinating research directed at decomposing mechanisms into parts (entities) and operations (activities) with research using computational models to recompose mechanisms and determine their dynamic behavior. This coordinated endeavor yields dynamic mechanistic explanations.

Michael Friedman has recently developed a neo-Kantian conception of science as a rival to holistic accounts. According to Friedman, the main virtue of his neo-Kantian philosophy is that it resolves the problem of incommensurability. A close reading of Friedman's work, however, indicates that his account surprisingly accomplishes the opposite of what he believes to have defended. This article will argue that the real virtue of Friedman's neo-Kantian account of scientific theories is not that it resolves the problem of incommensurability but that it provides an elegant philosophical account much needed to substantiate Thomas Kuhn's historical thesis of incommensurability.

A conservation law in physics can be either a constraint on the kinds of interaction there could be or a coincidence of the kinds of interactions there actually are. This is an important, unjustly neglected distinction. Only if a conservation law constrains the possible kinds of interaction can a derivation from it constitute a scientific explanation despite failing to describe the causal/mechanical details behind the result derived. This conception of the relation between “bottom-up” scientific explanations and one kind of “top-down” scientific explanation is motivated by several examples from classical and modern physics.

We critically examine Denis Walsh's latest attack on the causalist view of fitness. Relying on Judea Pearl's Sure-Thing Principle and geneticist John Gillespie's model for fitness, Walsh has argued that the causal interpretation of fitness results in a reductio. We show that his conclusion only follows from misuse of the models, that is, (1) the disregard of the real biological bearing of the population-size parameter in Gillespie's model and (2) the confusion of the distinction between ordinary probability and Pearl's causal probability. Properly understood, the models used by Walsh do not threaten the causalist view of fitness.

Sober has reconstructed the biological design argument in the framework of likelihoodism, purporting to demonstrate that it is defective for intrinsic reasons. We argue that Sober’s restriction on the introduction of auxiliary hypotheses is too restrictive, as it commits him to rejecting types of everyday reasoning that are clearly valid. Our account shows that the design argument fails, not because it is intrinsically untestable but because it clashes with the empirical evidence and fails to satisfy certain theoretical desiderata (in particular, unification). Likewise, Sober’s critique of the arguments from imperfections and from evil against design is off the mark.

The purpose of this article is to update and defend syntax-based (conserved DNA-sequence motifs) gene concepts. I show how syntax-based concepts have been and can be extended to accommodate complex cases of genome expression, regulation, and processing. In response to difficult cases and causal parity objections, I argue that the syntax-based approach fleshes out a deflationary concept that defines genes in terms of sequences and organizational features of the genome that contribute to a phenotype.

Describing a physical system in idealized terms involves making claims about the system that we know to be literally false. Because of this, it is not clear how calculations involving idealizations can generate justified belief and explain facts about the world. I argue that this puzzling aspect of idealizations cannot be explained away by talking about approximations, as is often supposed. I develop a different account of how justified beliefs and explanations can be generated from idealized descriptions of physical systems. My account involves a type of contextualism about the truth of mathematical descriptions of physical systems.

Nagel's official model of theory reduction and the way it is represented in the literature are shown to be incompatible with the careful remarks on the notion of reduction Nagel gave while developing his model. Based on these remarks, an alternative model is outlined, which does not face some of the problems the official model faces. Taking the context in which Nagel developed his model into account, it is shown that the way Nagel shaped his model and, thus, its well-known deficiencies are best conceived of as a mere by-product of his philosophical background.

In a recent paper, Brandon and Nijhout argue against genic selectionism—the thesis, roughly, that evolutionary processes are best understood from the gene's-eye point of view—by presenting a case in which genic models of selection allegedly make predictions that conflict with the (correct) predictions of higher-level genotypic selection models. Their argument, if successful, would refute the widely held belief that genic models and higher-level models are predictively equivalent. Here, I argue that Brandon and Nijhout fail to demonstrate that the models make incompatible predictions.

Focused correlation compares the degree of association within an evidence set to the degree of association in that evidence set given that some hypothesis is true. Wheeler and Scheines have shown that a difference in incremental confirmation of two evidence sets is robustly tracked by a difference in their focus correlation. In this essay, we generalize that tracking result by allowing for evidence having unequal relevance to the hypothesis. Our result is robust as well, and we retain conditions for bidirectional tracking between incremental confirmation measures and focused correlation.

Physical theories continue to be interpreted in terms of particles. The idea of a particle has required modification for successive theories but remains central to scientific explanation. Particle ontologies also have the virtue of explaining basic epistemic features of the world and so remain appealing for the scientific realist. However, particle ontologies are untenable when coupled with the empirically necessary postulate of permutation invariance—the claim that permuting the roles of particles in a representation of a physical state results in a representation of the same state. I demonstrate that any permutation invariant theory is incompatible with a particle ontology.

This article identifies conditions under which robust predictive modeling results have special epistemic significance—related to truth, confidence, and security—and considers whether those conditions hold in the context of present-day climate modeling. The findings are disappointing. When today’s climate models agree that an interesting hypothesis about future climate change is true, it cannot be inferred—via the arguments considered here anyway—that the hypothesis is likely to be true or that scientists’ confidence in the hypothesis should be significantly increased or that a claim to have evidence for the hypothesis is now more secure.

This essay examines the claim “path dependence entails irreversibility” from the point of view of evolutionary biology. I argue that evolutionary irreversibility possesses many faces, sometimes conflicting with path dependence. I propose an account of path dependence that does not rely on irreversibility and explain why it more naturally coexists with the notion of (contingent) irreversibility developed by the Belgian paleontologist Louis Dollo. However, I argue that we should not conceive of this relationship as necessary.

A probability space is common cause closed if it contains a Reichenbachian common cause of every correlation in it and common cause incomplete otherwise. It is shown that a probability space is common cause incomplete if and only if it contains more than one atom and that every space is common cause completable. The implications of these results for Reichenbach's Common Cause Principle are discussed, and it is argued that the principle is only falsifiable if conditions on the common cause are imposed that go beyond the requirements formulated by Reichenbach in the definition of common cause.

The received view of dynamical explanation is that dynamical cognitive science seeks to provide covering-law explanations of cognitive phenomena. By analyzing three prominent examples of dynamicist research, I show that the received view is misleading: some dynamical explanations are mechanistic explanations and in this way resemble computational and connectionist explanations. Interestingly, these dynamical explanations invoke the mathematical framework of dynamical systems theory to describe mechanisms far more complex and distributed than the ones typically considered by philosophers. Therefore, contemporary dynamicist research reveals the need for a more sophisticated account of mechanistic explanation.

We argue that dynamical and mathematical models in systems and cognitive neuroscience explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phenomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual receptive fields are explored.

Developmental systems theory (DST) is often dismissed on the basis that the causal indispensability of nongenetic factors in evolution and development has long been appreciated. A reformulation makes a more substantive claim: that the special role played by genes is also played by some (but not all) nongenetic resources. That special role can be captured by Shea's ‘inherited representation’. Formulating DST as the claim that there are nongenetic inherited representations turns it into a striking, empirically testable hypothesis. DST's characteristic rejection of a gene versus environment dichotomy is preserved but without dissolving into an interactionist casual soup, as some have alleged.

There is still a great deal of debate over what counts as a constraint and about how to assess experimentally the relative importance of constraints and selection in evolutionary history. I will argue that the notion of a constraint on variation, and thus the selection-constraint distinction, depends on two specifications: (1) what counts as a variant—constraints limit or bias the production of what? and (2) range of assessment—over what range of times or conditions is the variation assessed? Specifications 1 and 2 help us to understand empirical work on the relative importance of constraint and selection in evolution.