Recent science funding policy scholars and practitioners have advocated for the use of lotteries, or elements of random chance, as supplementations of traditional peer review for evaluating grant applications. One of the primary motivations for lotteries is their purported openness to innovative research. The purpose of this article is to argue that current proponents of funding science by lottery overestimate the viability of peer review and thus unduly restrict the scope of lotteries in science funding practice. I further show how this analysis suggests a different way of introducing lotteries into science funding policy.

Our thinking often uses rich memories of particular past events. Yet frequently we would do better to use other forms of memory. I show that existing accounts of the function of episodic memory cannot account for such cases, then develop an account which can. Roughly: rich representations of particular past events are required for Unrestricted Learning, learning which is not limited in how much of the world’s complexity it can capture; and episodic memory’s selection for Unrestricted Learning could explain its ubiquitous (and often inappropriate) use for other tasks. This proposal suggests many avenues for further empirical and computational research.

Livengood and Sytsma (2020) challenge the compositionality constraint of actual causation (CCAC), according to which each intermediary of a causal chain is an effect of its predecessor and a cause of its successor link. In several studies, they find support for their hypothesis that the CCAC is not in accordance with the ordinary causal attributions of laypeople. We argue that there are three interrelated problems in their studies’ design that we call the causality-responsibility confusion (CRC), the intermediary-ontology confusion (IOC), and the cause-end questioning (CEQ). Avoiding the CRC, the IOC, and the CEQ leads to strong empirical support for the CCAC.

Leibnizian metaphysics underpins the universally held view that spacetime must be inextendible—that it must be “as large as it can be” in a sense. But here we demonstrate a surprising fact within the context of general relativity: the property of inextendibility turns out to be unstable when attention is restricted to certain collections of “physically reasonable” spacetimes.

Adaptationism is often taken to be the thesis that most traits are adaptations. To assess this thesis, it seems we must be able to establish either an exhaustive set of all traits or a representative sample of this set. Either task requires a more systematic and principled way of individuating traits than is currently available. Moreover, different trait individuation criteria can make adaptationism turn out true or false. For instance, individuation based on natural selection may render adaptationism true, but may do so by presupposing adaptationism. In this article, we show how adaptationism depends on trait individuation and that the latter is an open and unsolved problem.

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.

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.

Recent accounts of multiscale modeling investigate ontic and epistemic constraints imposed by relations between component models at varying relative scales (macro, meso, micro). These accounts often focus especially on the role of the meso, or intermediate, relative scale in a multiscale model. We aid this effort by highlighting a novel role for mesoscale models: functioning as a focal point, and explanation, for disagreement between researchers who otherwise share theoretical commitments. We present a case study in multiscale modeling of insect behavior to illustrate, arguing that the cognitive map debate in neuroethology research is best understood as a mesoscale disagreement.

I consider a problem for functional reductionism based on the following tension. Say that b is functionally reduced to a. On one hand, a and b turn out to be identical, and identity is a symmetric relation. On the other hand, functional reductionism implies that a and b are asymmetrically related: if b is functionally reduced to a, then a is not functionally reduced to b. Thus we ask, how can a and b be asymmetrically related if they are the same thing? I propose a solution to this tension by distinguishing between ontological levels and levels of description.

It has been suggested that particle physics has reached the “dawn of the post-naturalness era.” I explain the current shift in particle physicists’ attitude toward naturalness. I argue that the naturalness principle was perceived to be supported by theories it has inspired. The potential coherence between major beyond the standard model (BSM) proposals and the naturalness principle led to an increasing degree of credibility of the principle among particle physicists. The absence of new physics at the Large Hadron Collider (LHC) has undermined the potential coherence and has led to the principle’s loss of significance.

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.

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.

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.

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.

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.

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.

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.

“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.

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.