This article is about the analogy between inclusive fitness and utility. In behavioral ecology, it is often assumed that individual organisms behave as if they were “striving” to maximize their inclusive fitness—a measure analogue to the kind of utility function that is used to represent the preferences of rational agents. Here, I explore some conceptual puzzles related to this view and question whether the kind of biological utility posited by the advocates of the “maximizing agent analogy” can be adequately interpreted in inclusive fitness terms.

The neural reuse framework developed primarily by Michael Anderson proposes that brain regions are involved in multiple and diverse cognitive tasks and that brain regions flexibly and dynamically interact in different combinations to carry out cognitive functioning. We argue that the evidence cited by Anderson and others falls short of supporting the fundamental principles of neural reuse. We map out this problem and provide solutions by drawing on recent advances in network neuroscience, and we argue that methods employed in network neuroscience provide the means to fully engage in a research program operating under the principles of neural reuse.

This article proposes to explicate theoretical equivalence by supplementing formal equivalence criteria with preservation conditions concerning interpretation. I argue that both the internal structure of models and choices of morphisms are aspects of formalisms that are relevant when it comes to their interpretation. Hence, a formal criterion suitable for being supplemented with preservation conditions concerning interpretation should take these two aspects into account. The two currently most important criteria—generalized definitional equivalence (Morita equivalence) and categorical equivalence—are not optimal in this respect. I put forward a criterion that takes both aspects into account: the criterion of definable categorical equivalence.

Reductionists say things like all mental properties are physical properties; all normative properties are natural properties. I argue that the only way to resist reductionism is to deny that causation is difference making (thus making the epistemology of causation a mystery) or to deny that properties are individuated by their causal powers (thus making properties a mystery); that is to say, unless one is happy to deny supervenience or to trivialize the debate over reductionism. To show this, I argue that if properties are individuated by their causal powers, then, surprisingly, properties are individuated by necessary coexemplification.

The Hong and Page ‘diversity trumps ability’ result has been used to argue for the more general claim that a diverse set of agents is epistemically superior to a comparable group of experts. Here we extend Hong and Page’s model to landscapes of different degrees of randomness and demonstrate the sensitivity of the ‘diversity trumps ability’ result. This analysis offers a more nuanced picture of how diversity, ability, and expertise may relate. Although models of this sort can indeed be suggestive for diversity policies, we advise against interpreting such results overly broadly.

When scientists are asked to give expert advice on risk-related questions, such as the authorization of medical drugs, deliberation often does not eliminate all disagreements. I propose to model these remaining discrepancies as differences in risk assessments and/or in risk acceptability thresholds. The normative question I consider, then, is how the individual expert views should best be aggregated. I discuss what “best” could mean, with an eye to some robustness considerations. I argue that the majority rule, which is currently often used in expert panels, has significant drawbacks.

This article considers the thesis that a more proportional relationship between a cause and its effect yields a more abstract causal explanation of that effect, thereby producing a deeper explanation. This thesis has important implications for choosing the optimal granularity of explanation for a given explanandum. In this article, I argue that this thesis is not generally true of probabilistic causal relationships. In light of this finding, I propose a pragmatic measure of explanatory depth. This measure uses a decision-theoretic model of information pricing to determine the optimal granularity of explanation for a given explanandum, agent, and decision problem.

According to Skow, correct answers to why questions cite only causes or grounds, but not nonaccidental regularities. Accounts that cite nonaccidental regularities typically confuse second-level reasons with first-level reasons. Only causes and grounds are first-level reasons why. Nonaccidental regularities are second-level reasons why. I first show that Skow’s arguments for the accusation of confusion depend on the independent thesis that only citations of first-level reasons why are (parts of) answers to why questions. Then I argue that this thesis is false. Consequently, the claim that correct answers to why questions cite only causes or grounds is refuted as well.

A number of formal models, including a highly influential model from Hong and Page, purport to show that functionally diverse groups often beat groups of individually high-performing agents in solving problems. Thompson argues that in Hong and Page’s model, that the diverse groups are created by a random process explains their success, not the diversity. Here, I defend the diversity interpretation of the Hong and Page result. The failure of Thompson’s argument shows that to understand the value of functional diversity, we should be clearer about how we conceive of and measure that diversity.