The article examines the process of niche standardization in medical research as an example of democratizing strategies implemented in industry-funded science. I argue that niche standardization can lead to undesirable epistemic and ethical consequences, if the various goals of research are not properly aligned. I examine two examples: the case of Sarafem, approved for the treatment of premenstrual dysphoric disorder in women, and the case of BiDil, approved for exclusive use in African Americans for the treatment of congestive heart failure. I conclude by cautioning against the unqualified support of democratizing strategies in industry-funded research.

This article examines the concept of wishful thinking in philosophical literature on science and values. It suggests that this term tends to be used in an overly broad manner that fails to distinguish between separate types of bias, mechanisms that generate biases, and general theories that might explain those mechanisms. I explain how confirmation bias is distinct from wishful thinking and why it is more useful for examining the relationship between cognitive bias and beliefs about the existence of injustices.

Thought experiments play an important epistemic, rhetorical, and didactic function in Galileo’s dialogues. In some cases, Salviati, Sagredo, and Simplicio agree about what would happen in an imaginary scenario and try to understand whether the predicted outcome is compatible with their respective theoretical assumptions. There are, however, also situations in which the predictions of the three interlocutors turn out to be theory laden. Salviati, Sagredo, and Simplicio not only disagree about what would happen, but they reject one another’s solutions as question begging and sometimes even dismiss one another’s thought experiments as misleading or nonsensical.

Galileo proposed what has been called a proto-inertial principle, according to which a body in horizontal motion will conserve its motion. This statement is only true in counterfactual circumstances where no impediments are present. This article analyzes how Galileo could have been justified in ascribing definite properties to this idealized motion. This analysis is then used to better understand the relation of Galileo’s proto-inertial principle to the classical inertial principle.

Philosophy of science is expanding via the introduction of new digital data and tools for their analysis. The data comprise digitized published books and journal articles, as well as heretofore unpublished material such as images, archival text, notebooks, meeting notes, and programs. The growth in available data is matched by the extensive development of automated analysis tools. The variety of data sources and tools can be overwhelming. In this article, we survey the state of digital work in the philosophy of science, showing what kinds of questions can be answered and how one can go about answering them.

In this article, we argue, first, that there are very different research projects that fall under the heading of “systems biology of cancer.” While they share some general features, they differ in their aims and theoretical commitments. Second, we argue that some explanations in systems biology of cancer are concerned with properties of signaling networks (such as robustness or fragility) and how they may play an important causal role in patterns of vulnerability to cancer. Further, some systems biological explanations are compelling illustrations of how “top-down” and “bottom-up” approaches to the same phenomena may be integrated.

I consider three explanatory strategies from recent systems biology that are driven by mathematics as much as mechanistic detail. Analysis of differential equations drives the first strategy; topological analysis of network motifs drives the second; mathematical theorems from control engineering drive the third. I also distinguish three abstraction types: aggregations, which simplify by condensing details; generalizations, which simplify by generalizing details; and structurations, which simplify by contextualizing details. Using a common explanandum as a reference point—namely, the robust perfect adaptation of chemotaxis in Escherichia coli—I argue that each strategy targets various abstraction types to different mechanistic details.

I consider recent uses of the notion of neutrality in evolutionary biology and ecology, questioning their relevance to the kind of explanation recently labeled ‘topological explanation’. Focusing on fitness landscapes and genotype-phenotype maps, I explore the explanatory uses of neutral subspaces, as modeled in two perspectives: hyperdimensional fitness landscapes and RNA sequence-structure maps. I argue that topological properties of such spaces account for features of evolutionary systems: respectively, capacity for adaptive evolution toward global optima and mutational robustness of genotypes. Thus many models appealing to “neutral” manifolds provide topological alternatives to hypothetical mechanisms.

Philosophical discussions of systems biology have enriched the notion of mechanistic explanation by pointing to the role of mathematical modeling. However, such accounts still focus on explanation in terms of tracking a mechanism’s operation across time (by means of mental or computational simulation). My contention is that there are explanations of molecular systems in which the explanatory understanding does not consist in tracking a mechanism’s operation and productive continuity. I make this case by a discussion of bifurcation analysis in dynamical systems, articulating the distinctive way in which explanatory understanding is provided, especially about the reversibility or irreversibility of molecular processes.

This article argues that scale dependence of physical and biological processes offers resistance to reductionism and has implications that support a specific kind of downward causation. I demonstrate how insights from multiscale modeling can provide a concrete mathematical interpretation of downward causation as boundary conditions for models used to represent processes at lower scales. The autonomy and role of macroscale parameters and higher-level constraints are illustrated through examples of multiscale modeling in physics, developmental biology, and systems biology. Drawing on these examples, I defend the explanatory importance of constraining relations for understanding the behavior of biological systems.

A causal approach to biological information is outlined. There are two aspects to this approach: information as determining a choice between alternative objects and information as determining the construction of a single object. The first aspect has been developed in earlier work to yield a quantitative measure of biological information that can be used to analyze biological networks. This article explores the prospects for a measure based on the second aspect and suggests some applications for such a measure. These two aspects are not suggested to exhaust all the facets of biological information.

Animal welfare scientists face an acute version of the problem of inductive risk since they must choose whether to affirm attributions of mental states to animals in advisory contexts, knowing their decisions hold consequences for animal welfare. In such contexts, the burden of proof should be sensitive to the consequences of error, but a framework for setting appropriate burdens of proof is lacking. Through reflection on two cases—pain and cognitive enrichment—I arrive at a tentative framework based on the principle of expected welfare maximization. I then discuss the limitations of this framework and the questions it leaves open.

In this article I articulate the Theory of Affective Pragmatics, which combines insights from the Basic Emotion View and the Behavioral Ecology View of emotional expressions. My core thesis is that emotional expressions are ways of manifesting one’s emotions but also of representing states of affairs, directing other people’s behaviors, and committing to future courses of actions. Since these are some of the main things we can do with language, my article’s take home message is that, from a communicative point of view, much of what we can do with language we can also do with nonverbal emotional expressions.

We argue that there are two different kinds of altruistic motivation: classical psychological altruism, which generates ultimate desires to help other organisms at least partly for those organisms’ sake, and nonclassical psychological altruism, which generates ultimate desires to help other organisms for the sake of the organism providing the help. We then argue that classical psychological altruism is adaptive if the desire to help others is intergenerationally reliable and, thus, need not be learned. Nonclassical psychological altruism is adaptive when the desire to help others is adaptively learnable. This theory opens new avenues for the interdisciplinary study of psychological altruism.

I introduce two new tools in experimental neurobiology, optogenetics and DREADDs (designer receptors exclusively activated by designer drugs). These tools permit unprecedented control over activity in specific neurons in behaving animals. In addition to their inherent scientific interest, these tools make an important contribution to philosophy of science. They illustrate the very premises of Ian Hacking’s “microscope” argument for the relative independence of experiment from theory. This new example is important for generalizing Hacking’s argument because the background sciences (optics for microscopes, molecular biology for optogenetics, and DREADDs) and the fields of engineering producing these tools differ significantly.

Optogenetics makes possible the control of neural activity with light. In this article, I explore how the development of this experimental tool has brought about methodological and theoretical advances in the neurobiological study of memory. I begin with Semon’s distinction between the engram and the ecphory, explaining how these concepts present a methodological challenge to investigating memory. Optogenetics provides a way to intervene into the engram without the ecphory that, in turn, opens up new means for testing theories of memory error. I focus on a series of experiments where optogenetics is used to study false memory and forgetting.

Optogenetic techniques are described as “revolutionary” for the unprecedented causal control they allow neuroscientists to exert over neural activity in awake-behaving animals. In this article, I demonstrate by means of a case study that optogenetic techniques will only illuminate causal links between the brain and behavior to the extent that their error characteristics are known and, further, that determining these error characteristics requires (1) comparison of optogenetic techniques with techniques having well-known error characteristics (methodological pluralism) and (2) consideration of the broader neural and behavioral context in which the targets of optogenetic interventions are situated (perspectival pluralism).

A hallmark of ecological research is dealing with complexity in the systems under investigation. One strategy is to diminish this complexity by constructing models and theories that are general. Alternatively, ecologists can constrain the scope of their generalizations to particular phenomena or types of systems. However, research employing the second strategy is often met with scathing criticism. I offer a theoretical argument in support of moderate generalizations in ecological research, based on the notions of interdependence and causal heterogeneity and their effect on the trade-off between generality and realism.