This article examines the role existence plays in measurement validity. I argue that existing popular theories of measurement and of validity follow a correspondence framework, which starts by assuming that an entity exists in the real world with certain properties that allow it to be measurable. Drawing on literature from the sociology of measurement, I show that the correspondence framework faces several theoretical and practical challenges. I suggest the validity-first framework of measurement, which starts with a practice-based validation process as the basis for a measurement theory and only posits objective existence when it is scientifically useful to do so.

This paper concerns the recent revival of entity realism. Having been started with the work of Ian Hacking, Nancy Cartwright, and Ronald Giere, the project of entity realism has recently been developed by Matthias Egg, Markus Eronen, and Bence Nanay. The paper opens a dialogue among these recent views on entity realism and integrates them into a more advanced view. The result is an epistemological criterion for reality: the property-tokens of a certain type may be taken as real insofar as only they can be materially inferred from the evidence obtained in a variety of independent ways of detection.

Climate scientists frequently employ heavily idealized models. How should these models be interpreted? Some philosophers have advanced a possibilist interpretation: climate models stand in for possible scenarios that could occur but do not provide information about how probable those scenarios are. This article argues that possibilism is (1) undermotivated, (2) incompatible with successful practices in the science, and (3) unable to correct for known biases. The upshot is that the models should be interpreted probabilistically in at least some cases.

“According to [Bayesian] models” in cognitive neuroscience, says a recent textbook, “the human mind behaves like a capable data scientist.” Do they? That is, do such models show we are rational? I argue that Bayesian models of cognition, perhaps surprisingly, don’t and indeed can’t show that we are Bayes-rational. The key reason is that they appeal to approximations, a fact that carries significant implications. After outlining the argument, I critique two responses, seen in recent cognitive neuroscience. One says that the mind can be seen as approximately Bayes-rational, while the other reconceives norms of rationality.

Participatory modeling in sustainability science allows scientists to take stakeholders’ interests, knowledge, and values into account when designing a model-based solution to a sustainability problem, by incorporating stakeholders in the model-building process. This improves the chance of generating socially robust knowledge and consensus on solutions. Part of what helps in this regard is that scientists, through involving stakeholders, limit their own values from influencing the outcome, thus achieving some level of value-neutrality. We argue that while it might achieve this to some extent, it comes at a cost to the reliability of the outcomes, which is ethically problematic.

Gerhard Schurz claims to have a solution to Hume’s problem of induction based on results from machine learning concerning meta-induction. His argument has two steps. The first is to establish a justification for following a certain meta-inductive strategy based on its predictive optimality. The second step is to show how this justification can be transferred to object-induction. I unpack the second step and fail to find a convincing argument supporting the transfer of justification from meta-induction to object-induction. My conclusion is that the problem of induction has not yet been solved by appeal to meta-induction.

Some philosophers understand natural kinds to be the categories which are constraints on enquiry. In order to elaborate the metaphysics appropriate to such an account, I consider the complicated history of scurvy, citrus, and vitamin C. It may be tempting to understand these categories in a shallow way (as mere property clusters) or in a deep way (as fundamental properties). Neither approach is adequate, and the case instead calls for middle-range ontology: starting from categories which we identify in the world and elaborating their structure, but not pretending to jump ahead to a complete story about fundamental being.

I argue that the classical temperature concept falls apart in special relativity by examining four consilient procedures for establishing classical temperature: Carnot processes, thermometers, kinetic theory, and black-body radiation. I show that their relativistic counterparts demonstrate no such consilience. I suggest two interpretations for this situation: eliminativism akin to simultaneity, or pluralism akin to rotation.

The concept of scaffold is widespread in science and increasingly common in evolutionary biology. While this concept figures in causal explanations, it is not clear what scaffolds are and what role they play in those explanations. Here we present evolutionary scaffolding explanation as a distinct type of explanatory strategy, distinguishing it from other types of evolutionary explanation. By doing so, we clarify the meaning of “scaffold” as a causal concept and its potential contribution to accounts of evolutionary novelty and major transitions.

Absolute and relative outcome measures measure a treatment’s effect size, purporting to inform treatment choices. I argue that absolute measures are at least as good as, if not better than, relative ones for informing rational decisions across choice scenarios. Specifically, this dominance of absolute measures holds for choices between a treatment and a control group treatment from a trial and for ones between treatments tested in different trials. This distinction has hitherto been neglected, just like the role of absolute and baseline risks in rational decision making that my analysis reveals. Recognizing both aspects advances the discussion on reporting outcome measures.

It is widely accepted by physicists and philosophers of physics alike that there are certain contexts in which general relativity will “break down.” In such cases, one expects to need some as-yet-undiscovered successor theory. This article discusses certain pathologies of general relativity that might be taken to signal that the theory is breaking down and considers how one might expect a successor theory to do better. The upshot is an unconventional interpretation of the “strong cosmic censorship hypothesis.”

Like Lewis, many philosophers hold reductionist accounts of chance (on which claims about chance are to be understood as claims that certain patterns of events are instantiated) and maintain that rationality requires that credence should defer to chance (in the sense that under certain circumstances one’s credence in an event must coincide with the chance of that event). It is a shortcoming of an account of chance if it implies that this norm of rationality is unsatisfiable by computable agents. This shortcoming is more common than one might have hoped.

This note scouts a broad but underexplored class of explanations found in contemporary computer science. These explanations, which I call limitative explanations, explain why certain problems cannot be solved computationally. Limitative explanations are philosophically rich, but have not received the attention they deserve. The primary goals of this note are to isolate limitative explanations and provide a preliminary account of what makes them explanatory. On the account I favor, limitative explanations are a kind of non-causal mathematical explanation which depend on highly idealized models of computation.

Like Austin’s “performatives,” some models are used not merely to represent but also to change their targets in various ways. This article argues that Austin’s analysis can inform model evaluation: If models are evaluated with respect to their adequacy-for-purpose, and if performativity can in some cases be regarded as a model purpose (a proposition that is defended, using mechanism design as an example), it follows that these models can be evaluated with respect to their “felicity,” that is whether their use has achieved this purpose. Finally, I respond to epistemic and ethical concerns that might block this conclusion.

Philosophical studies of complex scientific concepts are predominantly “adaptationist,” arguing that conceptual complexity serves important purposes. This is a historical artifact. Having had to defend their views against a monist presumption favoring simpler concepts, pluralists and patchwork theorists felt compelled to show that complexity can be beneficial. This neglects an alternative possibility: Conceptual complexity is largely neutral, persisting simply because it does little harm. This article defends the neutral theory of conceptual complexity in two forms: (a) as a plausible theory in its own right and (b) as a useful foil for adaptationist arguments.

My aim here is (1) to argue that the usual argument for thinking that dysfunction has no place in a dispositionalist approach to functions is deeply flawed and (2) to develop a positive account of the explanatory role dysfunction attributions play in dispositionalist-style functional analysis. I also argue that while my account undermines one common motivation for preferring an etiological over a dispositionalist approach, perhaps more interestingly, it also blurs the boundary between the two and opens a path to unifying them.

The philosophy of historical sciences investigates their distinct objects of study, epistemic challenges, and methodological solutions. Rethinking astronomy in this light offers a contribution. First, the methodology of historical sciences adds to a more adequate description of how astronomers study and utilize token events. Second, astronomy faces a typical difficulty in identifying traces of some past events and has developed a delicate solution. This enriches the idea of trace and suggests a methodology that relies on iterations between data-driven approaches and theory-driven approaches, together with the cross-validation between multiple relevant historical events or datasets.

While philosophers have focused on epistemological and ethical challenges of using artificial intelligence (AI) in science, scientists have focused largely on opportunities. I argue that this disconnect between philosophical pessimism and scientific optimism is driven by failures to critically examine the practice of AI-infused science. To appreciate the epistemic justification for AI-powered breakthroughs, philosophers must analyze the role of AI as part of a wider process of discovery. I demonstrate the importance of this with two cases from the scientific literature, and show that epistemic opacity need not diminish AI’s capacity to lead scientists to significant and justifiable breakthroughs.

Forber and Smead (2014) analyze how increasing the fitness benefits associated with prosocial behavior can increase the fitness of spiteful individuals relative to their prosocial counterparts, so that selection favors spite over prosociality. This poses a problem for the evolution of prosocial behavior: As the benefits of prosocial behavior increase, it becomes more likely that spite, not prosocial behavior, will evolve in any given population. In this article, I develop two game-theoretic models that, taken together, illustrate how synergistic costs and benefits may provide partial solutions to Forber and Smead’s paradox.

Scientism has recently experienced a resurgence of interest in philosophy. One version of scientism often defended is ontological scientism—the view that any kind or property not mentioned in the theories of science has only a subordinate, secondary kind of reality. It is worth noting that a dominant tradition in the history of philosophy of science—classical American pragmatism—undertook decades of critical engagement with contemporaneous scientistic beliefs, many of which resemble those being debated at the present time. This antiscientistic philosophy has multiple points of relevance for contemporary debates and defenses of ontological scientism.