The paper discusses methodological guidelines for evaluating mechanistic explanations. According to current accounts, a satisfactory mechanistic explanation should include all of the relevant features of the mechanism, its component entities and activities, and their properties and organization, as well as exhibit productive continuity. It is not specified, however, how this kind of mechanistic completeness can be demonstrated. I argue that parameter sufficiency inferences based on mathematical model simulations provide a way of determining whether a mechanism capable of producing the phenomenon of interest can be constructed from mechanistic components organized, acting, and having the properties described in the mechanistic explanation.

The aim of this article is to develop an argument against metaphysical debates about the existence of human races. I argue that the ontology of race is underdetermined by both empirical and nonempirical evidence owing to a plurality of equally permissible candidate meanings of “race.” Furthermore, I argue that this underdetermination leads to a deflationist diagnosis according to which disputes about the existence of human races are nonsubstantive verbal disputes. While this diagnosis resembles general deflationist strategies in contemporary metaphysics, I show that my argument does not presuppose controversial metametaphysical assumptions.

The discovery of the Higgs particle required a signal of 5σ significance. The rigid application of that condition is a convention that disregards more specific aspects of the given experiment. In particular, it does not account for the characteristics of the look elsewhere effect in the individual experimental context. The paper relates this aspect of data analysis to the question as to what extent theoretical reasoning should be admitted to play a role in the assessment of the significance of empirical data.

In this article, I consider what sorts of chance credence norms can be justified by appeal to the idea that ideal credences should line up with the chances. I argue that the Principal Principle cannot be so justified but that an alternative norm, the Temporal Principle—which maintains that an agent’s credence in a proposition ϕ, conditional on the temporal proposition that says that the chance of ϕ is x, should be x—can be so justified.

This article examines two questions about scientists’ search for knowledge. First, which search strategies generate discoveries effectively? Second, is it advantageous to diversify search strategies? We argue pace Weisberg and Muldoon, “Epistemic Landscapes and the Division of Cognitive Labor” (this journal, 2009), that, on the first question, a search strategy that deliberately seeks novel research approaches need not be optimal. On the second question, we argue they have not shown epistemic reasons exist for the division of cognitive labor, identifying the errors that led to their conclusions. Furthermore, we generalize the epistemic landscape model, showing that one should be skeptical about the benefits of social learning in epistemically complex environments.

Jonah N. Schupbach and Jan Sprenger have proposed conditions of adequacy for measures of explanatory power. They derive and defend a measure of explanatory power satisfying their conditions of adequacy. This article furthers the development of their measure. The requirement that the measure be multidimensional analytic (in the sense of the mathematics of several complex variables) is avoided. Several proofs are simplified, and gaps in proofs are filled.

One of the major puzzles in evolutionary theory is how communication and information transfer are possible when the interests of those involved conflict. Perfect information transfer seems inevitable if there are physical constraints, which limit the signal repertoire of an individual, effectively making bluffing an impossibility. This, I argue, is incorrect. Unfakeable signals by no means guarantee information transfer. I demonstrate the existence of a so-called pooling equilibrium and discuss why the traditional argument for perfect information transfer (Frank’s full disclosure principle) does not hold in all cases. Additionally, I demonstrate that deception is possible at equilibrium despite the fact that signals are impossible to fake.

On the received view, the Representational Theory of Measurement reduces measurement to the numerical representation of empirical relations. This account of measurement has been widely criticized. In this article, I provide a new interpretation of the Representational Theory of Measurement that sidesteps these debates. I propose to view the Representational Theory of Measurement as a library of theorems that investigate the numerical representability of qualitative relations. Such theorems are useful tools for concept formation that, in turn, is one crucial aspect of measurement for a broad range of cases in linguistics, rational choice, metaphysics, and the social sciences.

Some scientists are happy to follow in the footsteps of others; some like to explore novel approaches. It is tempting to think that herein lies an epistemic division of labor conducive to overall scientific progress: the latter point the way to fruitful areas of research, and the former more fully explore those areas. Weisberg and Muldoon’s model, however, suggests that it would be best if all scientists explored novel approaches. I argue that this is due to implausible modeling choices, and I present an alternative ‘epistemic landscape’ model that demonstrates the alleged benefits from division of labor, with one restriction.

Here I explore a novel no-collapse interpretation of quantum mechanics that combines aspects of two familiar and well-developed alternatives, Bohmian mechanics and the many-worlds interpretation. Despite reproducing the empirical predictions of quantum mechanics, the theory looks surprisingly classical. All there is at the fundamental level are particles interacting via Newtonian forces. There is no wave function. However, there are many worlds.

There is currently a consensus among comparative psychologists that nonhuman animals are capable of some forms of mindreading. Several philosophers and psychologists have criticized this consensus, however, arguing that there is a “logical problem” with the experimental approach used to test for mindreading in nonhuman animals. I argue that the logical problem is no more than a version of the general skeptical problem known as the theoretician’s dilemma. As such, it is not a problem that comparative psychologists must solve before providing evidence for mindreading.

This article examines the issue of whether consideration of so-called minimal models can prompt learning about real-world targets. Using a widely cited example as a test case, it argues against the increasingly popular view that consideration of minimal models can prompt learning about such targets. The article criticizes influential defenses of this view for failing to explicate by virtue of what properties or features minimal models supposedly prompt learning. It then argues that consideration of minimal models cannot prompt learning about real-world targets unless one supplements these models with additional information or presuppositions concerning such targets.

Epistemic accounts of scientific collaboration usually assume that, one way or another, two heads really are more than twice better than one. We show that this hypothesis is unduly strong. We present a deliberately crude model with unfavorable hypotheses. We show that, even then, when the priority rule is applied, large differences in successfulness can emerge from small differences in efficiency, with sometimes increasing marginal returns. We emphasize that success is sensitive to the structure of competing communities. Our results suggest that purely epistemic explanations of the efficiency of collaborations are less plausible but have much more powerful socioepistemic versions.

In a recent article, Carlos Santana shows that in common interest signaling games when signals are costly and when receivers can observe contextual environmental cues, ambiguous signaling strategies outperform precise ones and can, as a result, evolve. I show that if one assumes a realistic structure on the state space of a common interest signaling game, ambiguous strategies can be explained without appeal to contextual cues. I conclude by arguing that there are multiple types of cases of payoff-beneficial ambiguity, some of which are better explained by Santana’s models and some of which are better explained by models presented here.

Batterman and Rice offer an account of “minimal model explanations” and argue against “common features accounts” of those explanations. This paper offers some objections to their proposals and arguments. It argues that their proposal cannot account for the apparent explanatory asymmetry of minimal model explanations. It argues that their account threatens ultimately to collapse into a “common features account.” Finally, it argues against their motivation for thinking that an explanation appealing to “common features” would have to explain the common features’ own prevalence.

Theoretical accounts of health attempt to ground the concept in the relevant underlying biological facts. Discussions of such accounts have largely focused on whether they successfully identify necessary and sufficient conditions for a state to count as pathological. Correctly accounting for examples of pathology, however, is not the only basis for evaluating an understanding of disease. Here I argue that we should expect any understanding of health and disease to be consistent with the view that medicine’s central aim is health promotion. I argue that the theoretical account of health offered by Christopher Boorse faces particular difficulties in this regard.

In a recent paper, Sahotra Sarkar compares the Standard Dynamical interpretation of natural selection with the Information-Theoretic interpretation from Steven A. Frank. I address Sarkar’s three arguments against Frank’s interpretation. I show that Sarkar’s major argument that a key component of Frank’s account “does not have any natural biological interpretation” is premised on a contradiction stemming from a mathematical error. Consequently, Sarkar’s major argument is unsound. I also address Sarkar’s claim that a central equation in Frank’s interpretation is dynamically insufficient and his claim that Frank’s interpretation uses nonintuitive parameters to simply rewrite familiar results.

Macroeconomists overwhelmingly believe that macroeconomics requires microfoundations, typically understood as a strong eliminativist reductionism. Microfoundations aims to recover intentionality. In the face of technical and data constraints macroeconomists typically employ a representative-agent model, in which a single agent solves the microeconomic optimization problem for the whole economy, and take it to be microfoundationally adequate. The characteristic argument for the representative-agent model holds that the possibility of the sequential elaboration of the model to cover any number of individual agents justifies treating the policy conclusions of the single-agent model as practically relevant. This eschatological justification is examined and rejected.

This paper proposes an account of scientific data that makes sense of recent debates on data-driven and ‘big data’ research, while also building on the history of data production and use particularly within biology. In this view, ‘data’ is a relational category applied to research outputs that are taken, at specific moments of inquiry, to provide evidence for knowledge claims of interest to the researchers involved. They do not have truth-value in and of themselves, nor can they be seen as straightforward representations of given phenomena. Rather, they are fungible objects defined by their portability and prospective usefulness as evidence.