Measuring the climates of the deep past requires the use of paleoclimate proxies. I describe two proxy data and measurement practices, regarding proxy calibration and proxy data infrastructure. I document how at least some data and measurement practices in paleoclimatology are disunified: these practices do not involve intercalibration or otherwise statistical combination of multiple proxy records, and metadata necessary for proxy data to be reused or intercompared is often not provided. I argue that, perhaps counterintuitively, this lack of standardization and unification of proxy data and measurements has several benefits, especially related to the management of error and uncertainty.

This article tackles the objection that revealed preferences cannot causally explain. I mount a causal explanatory defense by drawing out three conditions under which such preferences can explain well, using an example of a successful explanation employing behavioral preferences. When behavioral preferences are multiple realizable, they can causally explain behavior well. Behavioral preferences also explain when agential preferences cannot be analytically separated from the environment that produces the relevant behavior (Condition 2) and when the environment is a significant causal factor (Condition 3). Thus, there are not causal explanatory grounds to completely bar revealed preference explanations from social science.

This article concerns the diachronic rationality norms for comparative confidence judgments, that is, judgments of the form “I am at least as confident in $p$ as I am in $q$.” Specifically, it identifies, characterizes, and evaluates an intuitively compelling learning rule called comparative conditionalization that specifies how agents should revise their comparative confidence judgments in the face of novel evidence.

The modern Everett interpretation of quantum mechanics describes an emergent multiverse. The goal of this paper is to provide a perspicuous characterisation of how the multiverse emerges making use of a recent account of (weak) ontological emergence. This will be cashed out with a case study that identifies decoherence as the mechanism for emergence. The greater metaphysical clarity enables the rebuttal of critiques due to Baker (2007) and Dawid and Thébault (2015) that cast the emergent multiverse ontology as incoherent; responses are also offered to challenges to the Everettian approach from Maudlin (2010) and Monton (2013).

While the investigation of non-human economic decision-making is increasingly popular, it is not clear exactly what role it can play in settling debates in economics. This paper argues that—contrary to recent claims otherwise—data on animal decision-making do not help in (dis–)confirming economic theories of choice. Rather, such data help in spelling out the representationally proper domains of models of choice. To play this role, though, these data must be placed into phylogenetic comparative analyses: correlations with specific environmental features need to be assessed, and these correlations need to be corrected for the presence of phylogenetic signals.

There is a Dutch Book argument for the axiom of countable additivity for subjective probability functions, but de Finetti famously rejected the axiom, arguing that it wrongly renders a uniform distribution impermissible over a countably infinite lottery. Dubins however showed that rejecting countable additivity has a strongly paradoxical consequence that a much weaker rule than countable additivity blocks. I argue that this rule, which also prohibits the de Finetti lottery, has powerful independent support in a desirable closure principle. I leave it as an open question whether countable additivity should be adopted.

This paper recapitulates earlier work in which I argued for the disunity of science, the plurality of partly incommensurable ways in which the world can be conceptualised for scientific purposes. It then aims to show how this plurality is intelligible, even to be expected, from the perspective of a process philosophy that sees the world as largely disorganised, but as allowing the emergence of pockets of stability, most notably the stability provided by biological organisms. It incidentally aims to demonstrate the importance to one another of science and metaphysics.

I develop a novel account of how nonepistemic aims and values can appropriately influence scientific investigation. At its heart is a process of epistemic projection in which a nonepistemic aim or value is mapped to an epistemic research problem that aligns with that aim or value. Choices in research are then justified as a means of solving that research problem. This epistemic projection approach makes research responsive to nonepistemic aims and values yet remains consistent with the value-free ideal; it could be acceptable to parties on both sides of the values-in-science debate. It also promises to be useful in practice.

Imagine that we are on a train, playing with some mechanical systems. Why can’t we detect any differences in their behavior when the train is parked versus when it is moving uniformly? The standard answer is that boosts are symmetries of Newtonian systems. In this article, I use the case of a spring to argue that this answer is problematic because symmetries are neither sufficient nor necessary for preserving its behavior. I also develop a new answer according to which boosts preserve the relational properties on which the behavior of a system depends, even when they are not symmetries.

I defend a novel account of scientific progress centered around justification. Science progresses, on this account, where there is a change in justification. I consider three options for explicating this notion of change-in-justification. This account of scientific progress dispels with a condition for scientific progress that requires accumulation of truth or truth-likeness, and it emphasizes the social nature of scientific justification.

Sexes are normally taken to be fundamental categories in biology—many sexually reproducing organisms fall under the categories of female/male. Much research aims at explaining differences between sexes. Sexual selection forms a central framework for explaining “typical” distributions of traits among sexes and explicating circumstances leading to “reversal.” I claim that sexual selection models need not make use of sexes and that sexes lack explanatory significance in such models. I offer a framework of reproductive dimorphism and argue that it is better than that of sexes because it (1) is more compatible with explanations of sexual selection and (2) allows for their greater applicability.

In a conventional experiment, scientists typically aim to learn about target systems by manipulating source systems of the same material type. In an analogue quantum simulation, by contrast, scientists typically aim to learn about target quantum systems of one material type via an experiment on a source quantum system of a different material type. In this article, we argue that such inferences can be justified by reference to source and target quantum systems being of the same empirical type. We illustrate this novel experimental practice of wavefunction engineering with reference to the example of Bose–Hubbard systems.

The interaction between scientific fields may be subject to strategic constraints—that is, fruitful interaction between fields may require adapting one field to the strategies informing the other. One way the concepts within an interfield field develop is by recrafting concepts originating in one practice in terms compatible with the strategies guiding the other. This conceptual accommodation may be a prerequisite to fruitful interactions between fields with different central problems. I explore these themes by considering the strategic constraints operative in the interfield field of quantum chemistry and the conceptual accommodation required for application to organic chemistry.

This paper examines constraints and their role in scientific explanation. Common views in the philosophical literature suggest that constraints are non-causal and that they provide non-causal explanations. While much of this work focuses on examples from physics, this paper explores constraints from other fields, including neuroscience, physiology, and the social sciences. I argue that these cases involve constraints that are causal and that provide a unique type of causal explanation. This paper clarifies what it means for a factor to be a constraint, when such constraints are causal, and how they figure in scientific explanation.

Philosophers of science have come to accept that contextual values can play unavoidable and desirable roles in science. This has raised concerns about the need to distinguish legitimate and illegitimate value influences in scientific inquiry. I discuss here four such concerns: epistemic distortion, value imposition, undermining of public trust in science, and the use of objectionable values. I contend that preserving epistemic integrity and avoiding value imposition provide good reasons to attempt to distinguish between legitimate and illegitimate influences of values in science. However, the trust and the objectionable values concerns constitute no good reason for demarcation criteria.

Teleosemantic theories aim to naturalize mental representation through the use of functions, typically based on past selection processes. However, the historical dependence of these theories has faced severe criticism, leading some philosophers to develop ahistorical alternatives. This article presents a new dilemma for all ahistorical teleosemantic theories, focusing in particular on the theories proposed by Timothy Schroeder and Bence Nanay. These theories require certain dispositions in the producers or consumers of mental representations, but the appeal to dispositions puts the proponents in an undesirable position: mental content is either overly dependent on current circumstances or ultimately dependent on historical factors.

Confirmation bias has been widely studied for its role in failures of reasoning. Individuals exhibiting confirmation bias fail to engage with information that contradicts their current beliefs, and, as a result, can fail to abandon inaccurate beliefs. But although most investigations of confirmation bias focus on individual learning, human knowledge is typically developed within a social structure. We use network models to show that moderate confirmation bias often improves group learning. However, a downside is that a stronger form of confirmation bias can hurt the knowledge-producing capacity of the community.

We challenge the standard interpretation of pain asymbolia (PA), a neuropsychiatric condition that causes unusual reactions to pain stimuli. The standard interpretation asserts that PA subjects experience pain but lack important features of the experience. However, we argue that the clinical evidence for PA does not support this interpretation and that the arguments put forward by the defenders of the standard interpretation end up making self-contradicting claims. Finally, we suggest that the best interpretation of the available evidence is to take a deflationist stance toward PA, at least until further evidence becomes available.