This paper articulates how recent advances in comparative behavioral and biological research are changing the nature of the mind/body problem from a human mind/human body problem to a problem of conceptualizing and organizing diverse types of minds in a hierarchical relational structure that is non-contingently related to phylogeny. It also discusses the impact of this refinement on our assessments of modal claims about possible artificial (AI) consciousness.

This paper examines the role of artificial intelligence in scientific problem-solving, with a focus on its implications for disciplinary creativity. Drawing on recent work in the philosophy of creativity, I distinguish between creative approaches and creative products, and introduce the concept of disciplinary creativity—the creative application of discipline-specific expertise to a valued problem within that field. Through two cases in mathematics, I show that while computation can extend disciplinary creativity, certain approaches involving AI can serve to displace it. This displacement has the potential to alter (and, perhaps, diminish) the value of scientific pursuit.

This paper argues that when brain networks figure in explanations of cognition and behavior they often do so in conjunction with independent dynamical assumptions about signal transmission in the brain. In such cases explanation does not depend on network structure alone but on network structure operating in conjunction with dynamical assumptions. Moreover, dynamical assumptions embody causal information, so that the resulting explanations are not entirely non-causal. In addition, it is argued that the directional features of explanations that appeal to networks can be understood in terms of the independence of network structure and dynamics.

In this article, we study difficult theory-choice situations, where division of cognitive labor is needed. Network epistemology models suggest that reducing connectivity is needed to prevent premature convergence on bad theories. We compare how network density, community size, strength of prior beliefs, adaptive learning methods, and weak ties influence epistemic outcomes, and show that reducing connectivity is only one possible way to improve collective epistemic accuracy. Our findings suggest that gains in accuracy often come at a high cost in resources used, which should be considered when results from network epistemology models are used in applied settings.

Bayesian epistemology is broadly concerned with providing norms for rational belief and learning using the mathematics of probability theory. Many authors have worried that the theory is too idealized to accurately describe real agents. In this paper I argue that an emerging program, computable Bayesian epistemology, can describe more realistic agents while retaining sufficient generality. I situate this program by placing it among the ongoing debate about ideal versus bounded rationality. I then present the basics of computable analysis and demonstrate its usefulness by proving a simple result: there are no computable finitely additive probability measures.

Over the past 50 years, psychological research has examined the stigma associated with “biomedical” models of mental illness. Credited with reducing blameworthiness, such models are also linked to negative outcomes such as prognostic pessimism and the desire for social distance. We argue that this research is hindered by an ambiguity in the notion of a “biomedical” framing, which conflates two ideas: a dysfunction-centered framing and a biogenic framing. We disentangle these concepts and highlight evidence that the dysfunction component of the biomedical framing, rather than the biogenic component, drives the observed negative outcomes. We discuss implications for research and practice.

Scientists studying fire-prone ecosystems have to contend with uncertainty stemming from causal complexity and conflicting aims, but they manage different types of uncertainty differently. I recommend that they instead adopt a common approach to uncertainty involving context-sensitivity and adaptive learning. Such an approach is consonant with insights from pragmatism and pluralism in philosophy of science and may even suggest a productive way to frame uncertainty while facilitating public trust.

Population descriptors (PDs) in biomedicine, like race and ethnicity, suffer from two problems. They introduce explanations with low proportionality, which fail to capture relevant causal relations; and their usage is motivated by a focus on global instead of local health issues. Both problems amount to, what we call, the ‘missing locality challenge’ of PDs leading to epistemic biases and stereotypes against local communities. We introduce a new framework to meet this challenge. It ranks PDs from ‘global’ to ‘local’ contexts of application, which helps selecting appropriate descriptors for different kinds of disease causalities and groups without stereotyping them.

We explore the Maximum Sensitivity method (MaxSen) of selecting priors for Bayesian inference in the face of severe lack of evidence (Konek, 2013). MaxSen minimizes the need to rely on the epistemic luck of true parameter values falling closely to their prior estimate.We explore the impact of the choice of the scoring method on the recommendations made by MaxSen. It turns out that if we use Kullback-Leibler Divergence (KLD) as a distance measure, the recommendations are the same as that of MaxEnt. We estimate how quickly the recommended prior distribution concentration increases with the planned sample size.

AI integration into scientific communities promises accelerated discovery but raises concerns about detrimental homogenization. We develop an NK landscape model to explore these promises and risks.We find that non-personalized AI systems that offer uniform guidance yield benefits only under a narrow conjunction of problem structure, practices, and baseline research capabilities, becoming harmful otherwise. We implement two proposed mitigations: randomization and personalization. While randomization’s utility remains restricted to decomposable problems, personalization can enhance diversity, enabling benefits across a broader range of conditions. Crucially, these benefits are not automatic: they depend on effective institutional adaptation, requiring new standards, protocols, and practices.

Recently, Hasok Chang has argued that we should adopt a pragmatic account of truth on which truth is constituted by practical success. Central to his argument is the claim that truth in this sense can be pursued and that other senses of truth can’t be. In this paper, I present a dilemma for Chang’s view. Either practical success constitutes truth in a way that implies infallibility—a conclusion that I’ll argue is untenable—or the argument from pursuit fails. I end by suggesting that scientific realism alone commits us to very little with respect to truth.

Charles Peirce’s most well-known contribution to logic and scientific methodology is his account of abduction. However, what exactly Peirce meant by abduction is subject to perennial dispute. My aim in this paper is two-fold: First, I interpret Peirce’s abduction through the lens of his research in geophysics and argue this supports interpreting it as a kind of inverse modeling. Second, I show that the methods Peirce outlined for managing the underdetermination of abductive inferences are the same as those deployed by geoscientists to manage the nonuniqueness of geophysical inversions. I conclude by emphasizing the value of reintegrating Peirce-the-Philosopher with Peirce-the-Geoscientist.

Practices such as “p-hacking” or “fishing” are widely regarded as epistemically pernicious, raising a puzzle. If two researchers generate identical data sets, why should it matter that one “fished” for the result whereas the other predicted it? Some respond to this puzzle by abandoning the Principle of Total Evidence (PTE), proposing that we exclude “tainted” data or weaken our descriptions of evidence. I argue that such departures are unnecessary and counterproductive. The most common forms of p-hacking are pernicious precisely because they violate PTE. For the remaining cases, I demonstrate that suppressing evidence leads to absurd results.

Knowledge brokers, usually conceptualized as passive intermediaries between scientists and policymakers in evidence-based policymaking, are understudied in the philosophy of science. Here, we challenge that usual conceptualization. As agents in their own right, knowledge brokers have their own goals and incentives, which complicate the effects of their presence at the science–policy interface.We illustrate this in an agent-based model and suggest several avenues for further exploration of the role of knowledge brokers in evidence-based policy.

Synthetic biology defies traditional views about the scope of biological inquiry (Hull 1978; Waters 2007; Weber 2013). Some authors are skeptical about the epistemic value the field has to the rest of the life sciences (Keller 2009a). This paper demonstrates how artificial biochemistries of life serve an essential epistemic function as semifactual models in biology (Knuuttila and Koskinen 2021). As semifactual models, alternative biochemistries of life facilitate the testing of hypotheses about universal biological constraints. Importantly, clarifying this role also unveils further research questions that would lend greater rigor to synthetic biology research programs.

We establish a connection between the part-whole principle and the quantity ded κ – a generalized cardinal characteristic related to the number of Dedekind cuts of a linear order. As consequences, we improve a result of Mancosu and Massas (2024) on generalized probability functions and propose some questions.

Teleparallel Gravity (TPG) is an alternative, but empirically equivalent, spacetime theory to General Relativity. In its modern formulation, TPG purports to be both a gauge theory of translations (G), as well as locally Lorentz-invariant (L). However, the reasoning invoked in order to implement (L) and (G) is often involved. As such, clarification of the reasoning upon which TPG proponents rely in constructing their theory is sorely needed. The present paper will address this need. It will also offer a succinct methodolog ical assessment of TPG as a theory per se.

Philosophers often defend appeals to parsimony by invoking its central role in science. I argue that this move fails once we distinguish between two uses of parsimony: non-ideal and ideal. Non-ideal parsimony enjoys strong inductive support in science, since complex models are prone to overfit to predictively irrelevant noise. But philosophical data aren’t significantly noisy in the relevant sense: when our intuitions are unreliable, their unreliability typically reflects systematic bias rather than noise, which parsimony doesn’t mitigate. Philosophers therefore need ideal parsimony, which finds only weak support from science. Thus, the scientific analogy cannot vindicate the philosopher’s use of parsimony.