Brouwer’s intuitionism was a far-reaching attempt to reform the foundations of mathematics. Although the mathematical community was reluctant to accept Brouwer’s work, its response to later-developed brands of intuitionism, such as those presented by Hermann Weyl and Arend Heyting, was different. The article accounts for this difference by analyzing the intuitionistic versions of Brouwer, Weyl, and Heyting in light of a two-tiered model of the body and image of mathematical knowledge. Such a perspective provides a richer account of each story and points to a possible connection between the community’s reaction and the changes each mathematician had proposed.

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.

Economists often consult multiple models in order to combat model uncertainty in the face of misspecification. By examining modeling practices at the Bank of England, this paper identifies an important, but underappreciated modeling procedure. Sometimes an idealized model is manipulated to reproduce the results from another distinct auxiliary model, ones which it could not produce on its own. However, this procedure does not involve making the original model “more realistic,” insofar as this means adding in additional causal factors. This suggests that there are ways to make models more representationally adequate that do not involve de-idealization in the straightforward sense.

According to David Albert, there are certain situations where we can cause events that lie in our past. In response to a well-known objection that we never observe backward causation, he argues that there are good reasons why we can’t tell when it obtains. However, I identify another difficulty with Albert’s view: at face value, it has the unattractive consequence that backward causation is not just possible, but rife. In this article, I show how this implication can be blocked. I then use my analysis to defend Albert’s account from a second well-known objection, namely, that it allows us to control the past.

Bargaining games have played a prominent role in modeling the evolution of social conventions. Previous models assumed that agents must choose from a predetermined set of strategies. I present a new model of two agents learning in bargaining games in which new strategies must be invented and reinforced. I study the efficiency and fairness of the model outcomes. The outcomes are somewhat efficient, but a significant part of the resource is wasted nonetheless. I implement two forms of forgetting and restrictions to the set of strategies that can be invented.

There is a well-known equivalence between avoiding accuracy dominance and having probabilistically coherent credences (see, e.g., de Finetti 1974; Joyce 2009; Predd et al. 2009; Pettigrew 2016). However, this equivalence has been established only when the set of propositions on which credence functions are defined is finite. In this article, I establish connections between accuracy dominance and coherence when credence functions are defined on an infinite set of propositions. In particular, I establish the necessary results to extend the classic accuracy argument for probabilism to certain classes of infinite sets of propositions, including countably infinite partitions.

Recent models of scientific collaboration show that minorities can end up at a disadvantage in bargaining scenarios. However, these models presuppose the existence of social categories. Here, I present a model of scientific collaboration in which inequality arises in the absence of social categories. I assume that all agents are identical except for the position that they occupy in the collaboration network. I show that inequality arises in the absence of social categories. I also show that this is due to the structure of the collaboration network and that similar patterns arise in two real-world collaboration networks.

Recently, several funding agencies have introduced the distribution of funds by a lottery system; however, the effects of this system on the productivity of the research community are unclear. Simulation studies in philosophy of science have argued that a combination of peer review and lottery is an optimal method. However, these models overlook several important aspects of research activities, such as baseline funding through block grants. In this article, I present a general theoretical model that incorporates these aspects and argue that the conventional combination of peer review and baseline funding outperforms the combination of peer review and lottery in many situations.

I argue that the temporal asymmetry of influence is not merely the result of thermodynamics: it is a consequence of the fact that the modal structure of the universe must admit only processes that cannot give rise to contradictions. I appeal to the process matrix formalism developed in the field of quantum foundations to characterize processes that are compatible with local free will while ruling out contradictions, and I argue that this gives rise to “consistent-chaining” requirements that explain the temporal asymmetry of influence. I compare this view to the perspectival account of causation advocated by Price and Ramsey.

Court cases involving sexual assault and police violence rely heavily on victim testimony. We consider what we call the Traumatic Untrustworthiness Argument (TUA) according to which we should be skeptical about victim testimony because people are particularly liable to misremember traumatic events. The TUA is not obviously based in mere distrust of women, people of color, disabled people, poor people, and so forth. Rather, it seeks to justify skepticism on epistemic and empirical grounds. We consider how the TUA might appeal to the psychology and neuroscience of memory for empirical support. However, we argue that neither support the TUA.

Current cosmological observations place little constraints on the nature of dark matter, allowing the development of a large number of models and various methods for probing their properties, which seem to provide ideal grounds for the employment of robustness arguments. In this article, the extent to which such arguments can be used to overcome various methodological and theoretical challenges is examined. The conclusion is that although robustness arguments have a limited scope in the context of dark-matter research, they can still be used to increase the scientists’ confidence about the properties of specific models.

Developing tools is a crucial aspect of experimental practice, yet most discussions of scientific change traditionally emphasize theoretical over technological change. To elaborate on the role of tools in scientific change, I offer an account that shows how scientists use tools in exploratory experiments to form novel concepts. I apply this account to two cases in neuroscience and show how tool development and concept formation are often intertwined in episodes of tool-driven change. I support this view by proposing common normative principles that specify when exploratory concept formation and tool development succeed (rather than fail) to initiate scientific change.

The COVID-19 global pandemic had a profound effect on scientific practice. During this time, officials crucially relied on the work done by modelers. This raises novel questions for the philosophy of science. Here I investigate the possibility of 'natural models' in predicting the SARS-Cov-2 virus’s trajectory for epidemiological purposes. I argue that to the extent that these can be considered scientific models, they support the possibility of a continuum from scientific models to natural models differing in artifactual commitment. In making my case, I draw from work on both model organisms and natural experiments as well as recent work in epidemiology.

All extant purely probabilistic measures of explanatory power satisfy the following technical condition: if Pr(E | H1) > Pr(E | H2) and Pr(E | ∼H1) < Pr(E | ∼H2), then H1’s explanatory power with respect to E is greater than H2’s explanatory power with respect to E. We argue that any measure satisfying this condition faces three serious problems—the Problem of Temporal Shallowness, the Problem of Negative Causal Interactions, and the Problem of Nonexplanations. We further argue that many such measures face a fourth problem—the Problem of Explanatory Irrelevance.

“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.

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.

Forster and Sober (1994) introduced the “sub-family problem” for model selection criteria that recommend balancing goodness-of-fit against simplicity. This problem arises when a maximally simple model (family of hypotheses) is artificially constructed to have excellent fit with the data. We argue that the problem arises because of a violation of the general maxim that balancing goodness-of-fit against simplicity leads to desirable inferences only if one is comparing models for the consideration of which one has a positive reason independently of the current data.

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.

Various errors can affect scientific code and detecting them is a central concern within computational science. Could formal verification methods, which are now available tools, be widely adopted to guarantee the general reliability of scientific code? After discussing their benefits and drawbacks, we claim that, absent significant changes as regards features like their user-friendliness and versatility, these methods are unlikely to be adopted throughout computational science, beyond certain specific contexts for which they are well-suited. This issue exemplifies the epistemological heterogeneity of computational science: Profoundly different practices can be appropriate to meet the reliability challenge that rises for scientific code.

If one has Humean inclinations, what account should one provide for idealization laws? I introduce the currently most popular Humean approach to laws of nature, the best systems account, along with some basic requirements for how to be Humean. I then show why idealization laws are unlikely to be accommodated within this account of laws. Finally, I offer an alternative approach that takes idealization laws to be meta-laws, placing requirements on the theorems of the best system.