There are two senses of ‘what scientists know’: An individual sense (the separate opinions of individual scientists) and a collective sense (the state of the discipline). The latter is what matters for policy and planning, but it is not something that can be directly observed or reported. A function can be defined to map individual judgments onto an aggregate judgment. I argue that such a function cannot effectively capture community opinion, especially in cases that matter to us.

Many scientific models lack an established representation relation to actual targets and instead refer to merely possible processes, background conditions, and results. This article shows how such models can be appraised. On the basis of the discussion of how-possibly explanations, five types of learning opportunities are distinguished. For each of these types, an example—from economics, biology, psychology, and sociology—is discussed. Contexts and purposes are identified in which the use of a model offers a genuine opportunity to learn. These learning opportunities offer novel justifications for modeling practices that fall between the cracks of standard representationalist appraisals of models.

The multiple-models approach, which has its origins in Levins’s work, is gaining broader acceptance among philosophers. Levins asserted that there is a trade-off between modeling desiderata, which justified the multiple-models approach through two separate justificatory paths. Some attention has been paid to the trade-off thesis, culminating in a paper by Matthewson and Weisberg. However, no attention has been paid to how the trade-off is supposed to justify the multiple-models approach. I argue that a trade-off between generality and precision cannot support one of Levins’s justificatory paths, and I consider what that might mean for the multiple-models approach.

Recently, Bechtel and Abrahamsen have argued that mathematical models study the dynamics of mechanisms by recomposing the components and their operations into an appropriately organized system. We will study this claim through the practice of combinational modeling in circadian clock research. In combinational modeling, experiments on model organisms and mathematical/computational models are combined with a new type of model—a synthetic model. We argue that the strategy of recomposition is more complicated than what Bechtel and Abrahamsen indicate. Moreover, synthetic modeling as a kind of material recomposition strategy also points beyond the mechanistic paradigm.

The United Kingdom Climate Impacts Program’s UKCP09 project makes high-resolution forecasts of climate during the twenty-first century using state of the art global climate models. The aim of this article is to introduce and analyze the methodology used and then urge some caution. Given the acknowledged systematic errors in all current climate models, treating model outputs as decision-relevant probabilistic forecasts can be seriously misleading. This casts doubt on our ability, today, to make trustworthy, high-resolution predictions out to the end of this century.

Are laws of nature necessary, and if so, are all laws of nature necessary in the same way? This question has played an important role in recent discussion of laws of nature. I argue that not all laws of nature are necessary in the same way: conservation laws are perhaps to be regarded as metaphysically necessary. This sheds light on both the modal character of conservation laws and the relationship between different varieties of necessity.

The current antireductionist consensus rests in part on the indefensibility of the deductive-nomological model of explanation, on which classical reductionism depends. I argue that the DN model is inessential to the reductionist program and that mechanism provides a better framework for thinking about reductionism. This runs counter to the contemporary mechanists’ claim that mechanism is an alternative to reductionism. I demonstrate that mechanists are committed to reductionism, as evidenced by the historical roots of the contemporary mechanist program. This view shares certain core commitments with reductionism. It is these shared commitments that constitute the essential elements of the reductionist program.

The contingentist/inevitabilist debate contests whether the results of successful science are contingent or inevitable. This article addresses lingering ambiguity in the way contingency is defined in this debate. I argue that contingency in science can be understood as a collection of distinct concepts, distinguished by how they hold science contingent, by what elements of science they hold contingent, and by what those elements are contingent upon. I present a preliminary taxonomy designed to characterize the full-range positions available and illustrate that these constitute a diverse array rather than a spectrum.

Diagrams have distinctive characteristics that make them an effective medium for communicating research findings, but they are even more impressive as tools for scientific reasoning. To explore this role, we examine diagrammatic formats that have been devised by biologists to (a) identify and illuminate phenomena involving circadian rhythms and (b) develop and modify mechanistic explanations of these phenomena.

Stem cells are defined as having capacities for both self-renewal and differentiation. Many different entities satisfy this working definition. I show that this general stem cell concept is relative to a cell lineage, temporal duration, and characters of interest. Experiments specify values for these variables. So claims about stem cells must be understood in terms of experimental methods used to identify them. Furthermore, the stem cell concept imposes evidential constraints on interpretation of experimental results. From these constraints, it follows that claims about stem cell capacities are inherently uncertain. This result has important implications for stem cell research.

Several articles have recently appeared arguing that there really are no viable alternatives to mechanistic explanation in the biological sciences (Kaplan and Bechtel; Kaplan and Craver). We argue that mechanistic explanation is defined by localization and decomposition. We argue further that systems neuroscience contains explanations that violate both localization and decomposition. We conclude that the mechanistic model of explanation needs to either stretch to now include explanations wherein localization or decomposition fail or acknowledge that there are counterexamples to mechanistic explanation in the biological sciences.

We reject a widespread objectivism about kinds of evolutionary groups in favor of a new conventionalism. Surprisingly, being any one kind of evolutionary group typically depends on which of many incompatible values are taken by suppressed variables. This novel pluralism underlies almost any single evolutionary group concept, unlike familiar pluralisms claiming that multiple concepts of certain sorts are legitimate. Consequently, we must help objective facts determine which candidate evolutionary groups satisfy the definition of a given evolutionary group concept, regardless of whether we also help determine the legitimacy of that concept’s applications.

In this essay I examine a well-known articulation of human nature skepticism, a paper by Hull. I then review a recent reply to Hull by Machery, which argues for an account of human nature that he claims is both useful and scientifically robust. I challenge Machery’s account and introduce an alternative account—the “life-history trait cluster” conception of human nature—that I hold is scientifically sound and makes sense of (at least some of) our intuitions about—and desiderata for—human nature.

Francisco Gil-White argues that the ubiquity of racialism—the view that so-called races have biological essences—can be explained as a by-product of a shared mental module dedicated to ethnic cognition. Gil-White’s theory has been endorsed, with some revisions, by Edouard Machery and Luc Faucher. In this skeptical response I argue that our developmental environments contain a wealth, rather than a poverty of racialist stimulus, rendering a nativist explanation of racialism redundant. I also argue that we should not theorize racialism in isolation from racism, as value judgments may play a role in essentialist thinking about the ‘other’.

The theoretical construct of functional reference is the main tool used by animal communication researchers to explore how animals refer to the world in the absence of a language. Functionally referential signals are commonly defined as signals elicited by a specific class of stimuli and capable of causing behaviors adaptive to such stimuli in the absence of contextual cues. I will argue that this definition is conceptually flawed and propose an alternative definition according to which signals can functionally refer to things that rarely cause them while relying on the essential contribution of contextual cues.

A common way of testing the inner sense theory of introspection exploits the possibility of damage to inner sense. Such damage is expected to lead to first-personal deficits/impairments of one kind or another. I raise various problems for this way of testing the theory. The main difficulty, I argue, stems from the existence of the method subserving confabulation.

There are two distinct but related threads in this article. The first is methodological and is aimed at exploring the relative merits and faults of different operational definitions of consciousness. The second is conceptual and is aimed at understanding the prior commitments regarding the nature of conscious content that motivate these positions. I consider two distinct operationalizations: one defines consciousness in terms of dichotomous subjective reports, the other in terms of graded subjective reports. I ultimately argue that both approaches are inadequate and that a third alternative that involves integration of subjective report and task performance should be adopted.

I show that the recent account of levels in neuroscience proposed by Craver and Bechtel is unsatisfactory since it fails to provide a plausible criterion for being at the same level and is incompatible with Craver and Bechtel’s account of downward causation. Furthermore, I argue that no distinct notion of levels is needed for analyzing explanations and causal issues in neuroscience: it is better to rely on more well-defined notions such as composition and scale. One outcome of this is that apparent cases of downward causation can be analyzed away.

Randomized controlled trials are currently the gold standard within evidence-based medicine. Usually they are monitored for early signs of effectiveness or harm. However, evidence from trials stopped early is often charged with bias toward implausibly large effects. To our mind, this skeptical attitude is unfounded and caused by the failure to perform appropriate conditioning in the statistical analysis of the evidence. We contend that conditional hypothesis tests give a superior appreciation of the obtained evidence and significantly improve the practice of sequential medical trials, while staying firmly rooted in frequentist methodology.

The case of Semmelweis has been well known since Hempel. More recently, it has been revived by Peter Lipton, Donald Gillies, Alexander Bird, Alex Broadbent, and Raphael Scholl. While these accounts differ on what exactly the case of Semmelweis shows, they all agree that Semmelweis was an excellent reasoner. This widespread agreement has also given rise to a puzzle: why Semmelweis’s views were rejected for so long. I aim to dissolve both this puzzle and the standard view of Semmelweis by showing that, contrary to prevailing opinion, Semmelweis was not the excellent reasoner he has been assumed to be.