This paper articulates a conceptual framework for examining philosophical issues such as the role of values in science at an organizational level. It distinguishes between three dimensions of organizations – organizational aims, organizational structure, and organizational culture – and it examines how these dimensions relate to values in research and development, with a focus on machine learning systems for predictive policing. This framework can be fruitful in identifying interesting and understudied philosophical problems – including those involving inter-organizational divisions of labor – that might otherwise be difficult to conceptualize.

Grant Ramsey and Michael Deem argue that appreciating the role that empathy plays in posttransgression guilt leads to a more promising account of the emotion’s evolutionary origins. But because their proposal fails to adequately distinguish guilt from shame, we cannot say which of the two emotions we are actually getting an evolutionary account of. Moreover, a closer look at the details suggests both that empathy may be more relevant for our understanding of shame’s evolutionary origins than guilt’s and that guilt is unlikely to be an adaptation.

We focus on a neglected aspect of scientific theory choice: how the selection of theories affects epistemic values. Building on Kuhn, we provide a general characterization of the feedback-loop dynamic between theories and values in theory choice as analogous to the relationship between organisms and the environment in niche construction. We argue that understanding theory choice as niche construction can explain how certain values acquire more weight and a specific application over time, and how resistance to scientific change can, therefore, arise. We illustrate our picture by looking at the Mendelian–biometrician controversy.

Recent work on the status of astrophysical modeling in the wake of quantum gravity indicates that a “fauxrizon” (a portmanteau of faux horizon), such as is relevant to understanding astrophysical black holes according to the fuzzball proposal within string theory, might ultimately solve the familiar black hole evaporation paradox. I clarify, with general upshots for the foundations of quantum gravity research, some of what this suggestion would amount to: identification of intertheoretic constraints on global spacetime structure in (observer-relative) semiclassical models of fuzzballs.

I discuss a contemporary solution to the dynamic problem of old evidence (POE), as proposed by Sprenger. Sprenger’s solution combines the Garber–Jeffrey–Niiniluoto (GJN) approach with Howson’s suggestion of counterfactually removing the old evidence from scientists’ belief systems. I argue that in the dynamic POE, the challenge is to explain how an insight under beliefs in which the old evidence E is known increased the credence of a scientific hypothesis. Therefore, Sprenger’s counterfactual solution, in which E has been artificially removed, does not resolve the problem. I consider several potential responses.

This paper investigates the question of how subjective probability should relate to binary belief. We propose new distance minimization methods, and develop epistemic decision-theoretic accounts. Both approaches can be shown to get “close” to the truth: the first one by getting “close” to a given probability, and the second by getting expectedly “close” to the truth. More specifically, we study distance minimization with a refined notion of Bregman divergence and expected utility maximization with strict proper scores. Our main results reveal that the two ways to get “close” to the truth can coincide.

Consensus reporting is valuable for presenting unified scientific evidence to the public. When a consensus does not exist, I argue that scientists ought not to default to majority reporting in its place. Majority reporting has several epistemic drawbacks because it can obscure underlying justifications and lines of evidence, which may be in conflict or contested. I argue that minority reporting, in conjunction with majority reporting, is an epistemically superior mechanism for scientists to report on the full range of reasons and evidence available within a group. This article addresses several objections, including worries over group cohesion, fringe reporting, and elite capture.

This note presents a short reply to Henderson’s critical discussion of Schurz’s approach to the problem of induction based on the optimality of meta-induction. Henderson objects that the meta-inductive a posteriori justification of object-induction rests on a certain premise—namely, an approximation condition—that she reveals as untenable. I reply that Henderson’s approximation condition is indeed too strong to be plausible, but it is not needed by the meta-inductive approach; a much weaker and highly plausible approximation condition is sufficient.

We consider the long-term evolution of science and show how a “contagion of disrespect”—an increasing dismissal of research in subfields associated with marginalized groups—can arise due to the dynamics of collaboration and reputation (versus, e.g., preconceived notions of the field’s worth). This has implications both for how we understand the history of science and for how we attempt to promote diverse scientific inquiry.

When solving a complex problem in a group, should group members always choose the best available solution that they are aware of? In this paper, I build simulation models to show that, perhaps surprisingly, a group of agents who individually randomly follow a better available solution than their own can end up outperforming a group of agents who individually always follow the best available solution. This result has implications for the feminist philosophy of science and social epistemology.