There has been increased attention on how scientific communities should respond to spurious dissent. One proposed method is to hide such dissent by preventing its publication. To investigate this, I computationally model the epistemic effects of hiding dissenting evidence on scientific communities. I find that it is typically epistemically harmful to hide dissent, even when there exists an agent purposefully producing biased dissent. However, hiding dissent also allows for quicker correct epistemic consensus among scientists. Quicker consensus may be important when policy decisions must be made quickly, such as during a pandemic, suggesting times when hiding dissent may be useful.

In this article I issue a challenge to what I call the Independence Thesis of Theory Assessment (ITTA). According to ITTA, the evidence for (or against) a theory must be assessed independently from the theory explaining the evidence. I argue that ITTA is undermined by cases of evidential uncertainty, in which scientists have been guided by the explanatory power of their theories in the assessment of the evidence. Instead, I argue, these cases speak in favor of a model of theory assessment in which explanatory power may indeed contribute to the stabilization of the evidential basis.

Bayesian confirmation does not generally agglomerate over conjunction. That is, whenever a piece of evidence $E$ confirms two hypotheses ${H_1}$ and ${H_2}$ individually, it does not follow that $E$ also confirms them conjunctively. Here, I present a condition under which the latter does follow from the former. But this new condition reveals a surprising fact: Bayesian confirmation agglomerates over conjunction whenever the evidence in question also confirms that both target hypotheses are false.

Where do values live in thought? A straightforward answer is that we (or our brains) make decisions using explicit value representations which are our values. Recent work applying reinforcement learning to decision-making and planning suggests that, more specifically, we may represent both the instrumental expected value of actions as well as the intrinsic reward of outcomes. In this paper, I argue that identifying value with either of these representations is incomplete. For agents such as humans and other animals, there is another place where reward can be located in thought: the division of the space of possibilities or “state space.”

Agent-based models (ABMs) are widely used to study how individual interactions shape collective behaviors. Critics argue that ABMs are often too simplistic to capture real-world complexities. We address this by integrating artificial neural networks into ABMs, focusing on enhancing the Hegselmann–Krause (HK) model. By using multilayer perceptrons as agents, we create more realistic ABMs that better reflect actual agents. This approach yields multiple models, as core elements of the HK model can be defined in various ways. We conduct two computational studies to compare these models with each other and with traditional individual-learning paradigms.

The debate over context and neural function pits contextualists, who maintain that neural functions vary by context, against invariantists, who maintain that they do not. In this article, I defend a moderate position that permits some context sensitive yet invariant functions. I distinguish performance from competence as well as different types of contexts, accounting for many cases of variability by context. The discussion suggests changes in networks can sometimes change the competences of parts. I conclude that some context sensitivity of function is consistent with invariant functions whereas other changes in context imply changes in the competence to perform functions.

In general relativity, the strong equivalence principle is underpinned by a geometrical account of fields on spacetime, by which all fields and bodies probe the same geometry. This geometric account implies that the parallel transport of all spacetime tensors and spinors is dictated by a single affine connection. No similar account of gauge theory is put forward by standard textbooks, which use principal bundles to coordinate the parallel transport of different, interacting particles. Nonetheless, here I argue that gauge theory does afford such a geometric account, obviating the need for principal bundles.

One type of computational indeterminacy arises from partitioning a system’s physical state space into state types that correspond to the abstract state types underlying the computation concerned. The mechanistic individuative strategy posits that computation can be uniquely identified through either narrow physical properties exclusively or wide, proximal properties. The semantic strategy posits that computation should be uniquely identified through semantic properties. We develop, and defend, an alternative functional individuative strategy that appeals—when needed—to wide, distal functions. We claim that there is no actual computation outside of a functional context. Desiderata for the underlying notion of teleofunction are discussed.

Stegenga (forthcoming) formulates and defends a novel account of scientific progress, according to which science makes progress just in case there is a change in scientific justification. Here, we present several problems for Stegenga’s account, concerning, respectively, (i) obtaining misleading evidence, (ii) losses or destruction of evidence, (iii) oscillations in scientific justification, and (iv) the possibility of scientific regress. We conclude by sketching a substantially different justification-based account of scientific progress that avoids these problems.

How do opinion diversity and belief polarisation affect epistemic group decision-making, particularly if decisions must be made without delay and on the basis of permissive evidence? In an agent-based model, we track the consistency of group opinions aggregated through sentence-wise majority voting. Simulations on the model reveal that high opinion diversity, but not polarisation, incurs a significant inconsistency risk. These results indicate that epistemic group decisions based on permissive evidence can be particularly difficult for diverse groups. The results also improve our understanding of what can reasonably be expected of expert groups, and where expert advice might have limits.

Effective field theory (EFT) is a computationally powerful theoretical framework, finding application in many areas of physics. The framework, applied to the Standard Model of particle physics, is even more empirically successful than our theoretical understanding would lead us to expect. I argue that this is a problem for our understanding of how the Standard Model relates to some successor theory. The problem manifests as two theoretical anomalies involving relevant parameters: the cosmological constant and the Higgs mass. The persistent failure to fix these anomalies from within suggests that the way forward is to go beyond the EFT framework.

Scientists have the epistemic responsibility of producing knowledge. They also have the social responsibility of aligning their research with the needs and values of various societal stakeholders. Individual scientists may be left with no guidance on how to prioritize and carry these different responsibilities. As I will argue, however, the responsibilities of science can be harmonized at the collective level. Drawing from debates in moral philosophy, I will propose a theory of the collective responsibilities of science that accounts for the internal diversity of research groups and for their different responsibilities.

The Black–Scholes (B-S) model is considered by the academic environment one of the greatest achievements of financial economics. Yet it brings with it some conundrums. The model is often used in a manner that contradicts one of its assumptions, and its predictions are not supported by market reality. Here we address, from the perspective of philosophy of science, an additional issue related to this model: the distinction between its explanatory and predictive capabilities.

This essay introduces, develops, and appraises the mitonuclear compatibility species concept (MCSC), identifying advantages and limitations with respect to alternative species concepts. While the consensus amongst most philosophers of biology is that (kind) essentialism about species is mistaken, and that species at most have relational essences, we appeal to the MCSC to defend thoroughgoing intrinsic essentialism. Namely, the doctrine that species have fully intrinsic essences and, thus, are natural kinds (of sorts), while allowing that species aren’t categorically distinct.

When many statistical hypotheses are evaluated simultaneously, statisticians often recommend adjusting (or “correcting”) standard hypothesis tests. In this article, I (1) distinguish two senses of adjustment, (2) investigate the prudential and epistemic goals that adjustment might achieve, and (3) identify conditions under which a researcher should not adjust for multiplicity in the two senses I identify. I tentatively conclude that the goals of scientists and the public may be misaligned with the decision criteria used to evaluate multiple-testing regimes.

This paper proposes a novel view in the the philosophy of race & causation literature known as “causal agnosticism” about race. Causal agnosticism about race implies that it is reasonable to refrain from making judgments about whether race is a cause. The paper’s thesis asserts that certain conditions must be met to infer that something is a cause, according to the fundamental assumptions of causal inference. However, in the case of race, these conditions are often violated. By advocating for causal agnosticism, the paper suggests a more modest approach to understanding the role of race in causal relationships.

In recent debates about the replication crisis, two positions have been dominant: one that focuses on methodological reforms and one that focuses on theory building. This paper takes up the suggestion that there might be a deeper difference in play, concerning the ways the very subject matter of psychology is construed by opposing camps, i.e., in terms of stable effects versus in terms of complexity. I argue that each gets something right, but neither is sufficient. My analysis suggests that the context sensitivity of the psychological subject matter needs to be front and center of methodological and theoretical efforts.

This article introduces the snap hook methodology, a method used notably in astrochemistry as a way to indirectly validate and assess the accuracy of computational calculations in the absence of experimental or observational data. We argue that this methodology has tremendous potential for all computationally intensive scientific fields as a substitute for traditional verification and validation standards when those are not accessible and estimating the reliability of numerical predictions becomes a real difficulty. The goal of this article is to give to this method, which seems to be implicitly relied upon in many areas, a proper formulation, in order for philosophers of science to enter the debate and to highlight its undeniable potential in terms of interdisciplinary facilitation and knowledge transmission.