I argue against Matthew Slater’s rejection of what he calls the grounding claim in his stable property cluster (SPC) account of natural kinds. This claim states that the epistemic value of natural kinds depends on the existence of some ground to bind together a kind’s properties. Using two test cases from academic medicine, I show that grounds are genuinely explanatory of scientific epistemic practices and that the SPC account should not do without them in its philosophical analysis of natural kinds.

Counternomics—counterfactuals whose antecedents run contrary to the laws of nature—are commonplace in science but have enjoyed relatively little philosophical attention. This article discusses a puzzle about our counternomic epistemology, focusing on cases in which experimental observations are used as evidence for counternomic claims. I show that these cases resist being characterized in familiar interventionist lines, and I suggest a characterization of my own.

Callender and Cohen argue that there is no need for a special account of the constitution of scientific representation. I argue that scientific representation is communal and therefore deeply tied to the practice in which it is embedded. The communal nature is accounted for by licensing, the activities of scientific practice by which scientists establish a representation. A case study of the Lotka-Volterra model reveals how licensure is a constitutive element of the representational relationship. Thus, any account of the constitution of scientific representation must account for licensing, meaning that there is a special problem of scientific representation.

In this article I connect two debates in the philosophy of science: the questions of scientific representation and both model and theoretical equivalence. I argue that by paying attention to how a model is used to draw inferences about its target system, we can define a notion of theoretical equivalence that turns on whether models license the same claims about the same target systems. I briefly consider the implications of this for two questions that have recently been discussed in the context of the formal philosophy of science.

It has recently been suggested that realist responses to historical cases featured in pessimistic meta-inductions are not as successful as previously thought. In response, selective realists have updated the basic divide et impera strategy specifically to take such cases into account and to argue that more modern realist accounts are immune to the historical challenge. Using a case study—that of the nineteenth-century zymotic theory of disease—I argue that these updated proposals fail and that even the most sophisticated recent realist accounts remain vulnerable to the challenge from history.

Industry is a major source of funding for scientific research. There is also a growing concern for how it corrupts researchers faced with conflicts of interest. As such, the debate has focused on whether researchers have maintained their integrity. In this article we draw on both the history of medicine and formal modeling to argue that given methodological diversity and a merit-based system, industry funding can bias a community without corrupting any particular individual. We close by considering a policy solution (i.e., independent funding) that may seem to promote unbiased inquiry but that actually exacerbates the problem without additional restrictions.

The aim of this article is to critically build on Justin Biddle and Anna Leuschner’s characterization of epistemologically detrimental dissent (EDD) in the context of science. We argue that the presence of nonepistemic agendas and severe nonepistemic consequences offers neither necessary nor sufficient conditions for EDD to obtain. We clarify their role by arguing that they are contingent enabling factors, not stable difference-makers, in the production of EDD. We maintain that two stable difference-makers are core to the production of EDD: production of skewed science and effective public dissemination.

The reward system of science is the priority rule. The first scientist making a new discovery is rewarded with prestige, while second runners get little or nothing. Michael Strevens, following Philip Kitcher, defends this reward system, arguing that it incentivizes an efficient division of cognitive labor. I argue that this assessment depends on strong implicit assumptions about the replicability of findings. I question these assumptions on the basis of metascientific evidence and argue that the priority rule systematically discourages replication. My analysis leads us to qualify Kitcher and Strevens’s contention that a priority-based reward system is normatively desirable for science.

Many philosophers of science have argued that social and ethical values have a significant role to play in core parts of the scientific process. This naturally suggests the following question: when such value choices need to be made, which or whose values should be used? A common answer to this question turns to democratic values—the values of the public or its representatives. I argue that this imposes a morally significant burden on certain scientists, effectively requiring them to advocate for policy positions they strongly disagree with. I conclude by discussing under what conditions this burden might be justified.

Heritability estimates obtained from genome-wide association studies (GWAS) are much lower than those of traditional quantitative methods. This phenomenon has been called the “missing heritability problem.” By analyzing and comparing GWAS and traditional quantitative methods, we first show that the estimates obtained from the latter involve some terms other than additive genetic variance, while the estimates from the former do not. Second, GWAS, when used to estimate heritability, do not take into account additive epigenetic factors transmitted across generations, while traditional quantitative methods do. Given these two points we show that the missing heritability problem can largely be dissolved.

We consider modifications to the standard David Lewis signaling game and relax a number of unrealistic implicit assumptions that are often built into the framework. In particular, we motivate and explore various asymmetries that exist between the sender and receiver roles. We find that endowing receivers with a more realistic set of responses significantly decreases the likelihood of signaling, while allowing for unequal selection pressure often has the opposite effect. We argue that the results of this article can also help make sense of a well-known evolutionary puzzle regarding the absence of an evolutionary arms race between sender and receiver in conflict-of-interest signaling games.

What is natural selection? I address this question by exploring the relation between two debates: Is natural selection a mechanism? Is natural selection a causal or a statistical theory? I argue that the first can be assessed only relative to a model and that, following the second, there are two fundamentally different and independent kinds of models, Modern-Synthesis and Darwinian models. MS-models, I argue, are not mechanistic even if they are causal. D-models, in contrast, are mechanistic. A causal-mechanistic interpretation of D-models is thus compatible with a statistical interpretation of MS-models. Natural selection, I conclude, lacks a single, unifying nature.

Over the last 20 years, several philosophers have developed a new approach to biological functions, the organizational (or systems-theoretic) approach. This is not a single theory but a family of theories based on the idea that a trait token can acquire a function by virtue of the way it contributes to a complex, organized system and thereby to its own continued persistence as a token. I argue that the organizational approach faces a serious liberality objection. I examine three different ways organizational theorists have tried to avoid that objection and show how they fail.

Recently, philosophers have sought to determine the nature of individuals relevant to evolution by natural selection or evolutionary individuals. The Evolutionary Contingency Thesis is a claim about evolution that emphasizes the role of contingency or dependency relations and chance-based factors in how evolution unfolds. In this article, I argue that if we take evolutionary contingency seriously, then we should be pluralists about the types of individuals in selection.

Genidentity coupled with material continuity is proposed as a minimum conception of biological individuality, and then theoretical individuation is employed to identify multiple kinds of biological individuals in a single example from mycology, a patch of chanterelle mushrooms. Of the many candidate materially continuous genidenticals found in a mushroom patch, only those with functional roles in biological theory are notable as biological individuals. Evolutionary and physiological theories pick out multiple kinds of functional individuals in mushrooms, so a pluralistic account of mushroom individuality is warranted.

I propose a new account of homology, according to which homology is a correspondence of developmental mechanisms due to common ancestry, formally defined as an isomorphism of causal graphs over lineages. The semiformal definition highlights the role of homology as a higher-order principle unifying evolutionary models and also provides definite meanings to concepts like constraints, evolvability, and novelty. The novel interpretation of homology suggests a broad perspective that accommodates evolutionary developmental biology and traditional population genetics as distinct but complementary approaches to understand evolution, prompting further empirical and theoretical research.

In this article, I discuss the concept of robustness in neuroscience. Various mechanisms for making systems robust have been discussed across biology and neuroscience (e.g., redundancy and fail-safes). Many of these notions originate from engineering. I argue that concepts borrowed from engineering aid neuroscientists in (1) operationalizing robustness, (2) formulating hypotheses about mechanisms for robustness, and (3) quantifying robustness. Furthermore, I argue that the significant disanalogies between brains and engineered artifacts raise important questions about the applicability of the engineering framework. I argue that the use of such concepts should be understood as a kind of simplifying idealization.