Citation: Marenbon, J. (2025). Thomas Aquinas and Philosophy as a Way of Life. New Blackfriars, 106(6), 541–554. https://doi.org/10.1017/nbf.2025.10107.

Abstract: For Pierre Hadot, inventor of ‘Philosophy as a Way of Life’ (PWL), scholasticism, of which Aquinas is usually seen as the arch-representative, was not only the opposite of PWL but the agent of its destruction. I argue that Hadot’s view of Aquinas results from confusing ‘philosophy’ in the broad sense, which is how it needs to be understood in relation to PWL, with ‘philosophy’ in the narrower sense that it had for Aquinas himself. When Aquinas’s life and work is examined with this distinction in mind, he is seen to be as much an exponent of PWL as the medieval and modern thinkers (Boethius of Dacia, Dante, Montaigne, Kant, Nietzsche) usually cited by Hadot and his followers. This conclusion puts into doubt the historical narrative proposed by exponents of PWL. But some of Hadot’s own remarks leave room for a restricted version of PWL, stripped of its historical narrative and suggestions about the content of a philosophical life. This pure methodological Philosophy as a Way of Life, MPWL, does not make the unsustainable claims of PWL and helps to show how analytical, historical and more broadly philosophical approaches to Aquinas can be brought together.

Citation: Kahane, G. (2013). The armchair and the trolley: an argument for experimental ethics. Philosophical Studies, 162, 421–445. https://doi.org/10.1007/s11098-011-9775-5.

Abstract: Ethical theory often starts with our intuitions about particular cases and tries to uncover the principles that are implicit in them; work on the ‘trolley problem’ is a paradigmatic example of this approach. But ethicists are no longer the only ones chasing trolleys. In recent years, psychologists and neuroscientists have also turned to study our moral intuitions and what underlies them. The relation between these two inquiries, which investigate similar examples and intuitions, and sometimes produce parallel results, is puzzling. Does it matter to ethics whether its armchair conclusions match the psychologists’ findings? I argue that reflection on this question exposes psychological presuppositions implicit in armchair ethical theorising. When these presuppositions are made explicit, it becomes clear that empirical evidence can (and should) play a positive role in ethical theorising. Unlike recent assaults on the armchair, the argument I develop is not driven by a naturalist agenda, or meant to cast doubt on the reliability of our moral intuitions; on the contrary, it is even compatible with non-naturalism, and takes the reliability of intuition as its premise. The argument is rather that if our moral intuitions are reliable, then psychological evidence should play a surprisingly significant role in the justification of moral principles.

Citation: Glass, D.H. (2017). Science, God and Ockham’s razor. Philosophical Studies, 174, 1145–1161. https://doi.org/10.1007/s11098-016-0747-7.

Abstract: In discussions about the existence of God, it is sometimes claimed that the progress of science has removed the need for God. This paper uses a Bayesian analysis of Ockham’s razor to formulate and evaluate this argument, which is referred to as the science explains away God argument (SEAGA). Four different strategies for responding to this argument are presented and evaluated. It is argued that one of these strategies highlights how difficult it is to show that the conditions for applying Ockham’s razor are satisfied and hence why SEAGA is very unlikely to succeed.

Citation: Stenseke, J. (2025). Counter-productivity and suspicion: two arguments against talking about the AGI control problem. Philosophical Studies. https://doi.org/10.1007/s11098-025-02379-9.

Abstract: How do you control a superintelligent artificial being given the possibility that its goals or actions might conflict with human interests? Over the past few decades, this concern– the AGI control problem– has remained a central challenge for research in AI safety. This paper develops and defends two arguments that provide pro tanto support for the following policy for those who worry about the AGI control problem: don’t talk about it. The first is argument from counter-productivity, which states that unless kept secret, efforts to solve the control problem could be used by a misaligned AGI to counter those very efforts. The second is argument from suspicion, stating that open discussions of the control problem may serve to make humanity appear threatening to an AGI, which increases the risk that the AGI perceives humanity as a threat. I consider objections to the arguments and find them unsuccessful. Yet, I also consider objections to the don’t-talk policy itself and find it inconclusive whether it should be adopted. Additionally, the paper examines whether the arguments extend to other areas of AI safety research, such as AGI alignment, and argues that they likely do, albeit not necessarily as directly. I conclude by offering recommendations on what one can safely talk about, regardless of whether the don’t-talk policy is ultimately adopted.

Citation: Collins, H., & Thorne, S. (2026). Large language models and scientific discourse: Where’s the intelligence?. Synthese, 207. https://doi.org/10.1007/s11229-026-05531-y.

Abstract: We explore the capabilities of Large Language Models (LLMs) by comparing the way they gather data with the way humans build knowledge. Here we examine how scientific knowledge is made and compare it with LLMs. The argument is structured by reference to two figures, one representing scientific knowledge and the other LLMs. In a 2014 study, scientists explain how they choose to ignore a ‘fringe science’ paper in the domain in the domain of gravitational wave physics: the decisions are made largely as a result of tacit knowledge built up in social discourse, mostly spoken discourse, within closed groups of experts. It is argued that LLMs cannot or do not currently access such discourse, but it is typical of the early formation of scientific knowledge. LLMs ‘understanding’ builds on written literatures and is therefore insecure in the case of the initial stages of knowledge building. We refer to Colin Fraser’s ‘Dumb Monty Hall problem’ where in 2023 ChatGPT failed though a year or so later LLMs were succeeding. We argue that this is not a matter of improvement in LLMs ability to reason but in the change in the body of human written discourse on which they can draw (or changes being put in by humans ‘by hand’). We then invent a new Monty Hall prompt and compare the responses of a panel of LLMs and a panel of humans: they are starkly different but we explain that the previous mechanisms will soon allow the LLMs to align themselves to humans once more. Finally, we look at ‘overshadowing’ where a settled body of discourse becomes so dominant that LLMs fail to respond to small variations in prompts which render the old answers nonsensical. The ‘intelligence’, we argue, is in the humans not the LLMs.

Citation: Pritchard, D. (2026). Generative AI is untrustworthy. Synthese, 208. https://doi.org/10.1007/s11229-026-05755-y.

Abstract: Our reliance on the new wave of generative AI has increased exponentially in the last few years, with little sign of abating. This raises the natural question of whether we should trust this technology as a source of information. Drawing on the epistemology of trust, it is argued that generative AI does not meet the conditions for being a trustworthy source of information. In particular, it is argued that generative AI is untrustworthy because it is an unsafe source of information. This, in turn, entails that trusting generative AI as a source of information is not a route to knowledge.

Citation: Ludwig, D., & C.N. El-Hani. (2025). Transformative transdisciplinarity: An Introduction to community-based philosophy. Oxford University Press. https://doi.org/10.1093/9780197815281.001.0001.

Abstract: In the face of planetary crises, from biodiversity loss to climate change to food security, transdisciplinarity promises more effective and just responses through more equal collaborations. However, transdisciplinarity also creates complex challenges by bringing together different actors, from scientists to Indigenous communities to policymakers. Successful collaboration among such actors requires navigating different forms of knowledge, worldviews, values, and positions of power. Transformative Transdisciplinarity synthesizes insights from philosophy of science and empirical action research in addressing these challenges through a framework of partial overlaps. On the one hand, the framework highlights overlapping concerns and perspectives of actors that provide common ground for collaboration and mutual understanding. On the other hand, it emphasizes partialities that require navigating differences and tensions between actors. This book addresses the fundamental epistemological, ontological, and political questions of transdisciplinarity through this framework of partial overlaps, aiming for a transformative vision of collaborative science in the face of planetary crises. By addressing these foundational questions, Transformative Transdisciplinarity also develops a novel vision of community-based philosophy that engages with global challenges through collaborations with local communities and empirical researchers. In contrast with empirically disengaged armchair philosophy, this book shows how philosophers can play an important role in mediating between epistemologies and ontologies of diverse actors as well as the political inequalities between them.

Citation: Rescher, N. 2009. Epistemetrics. Cambridge University Press. https://doi.org/10.1017/CBO9780511498831.

Overview: When this book was originally published in 2006, Epistemetrics was not as yet a scholarly discipline. With regard to scientific information there was the discipline of scientometrics, represented by a journal of that very name. Science, however, had a monopoly on knowledge. Although it is one of our most important cognitive resources, it is not our only one. While scientometrics is a centerpiece of epistemetrics, it is not the whole of it. Nicholas Rescher’s endeavor to quantify knowledge is not only of interest in itself, but is also instructive in bringing into sharper relief the nature of and the explanatory rationale for the limits that unavoidably confront our efforts to advance the frontiers of knowledge. In particular, his book demonstrates the limitations of human knowledge and will be of great value to scholars working in this area.

Citation: Rescher, Nicholas. 2007. Interpreting philosophy: The elments of philosophical hermeneutics. De Gruyter Brill. https://doi.org/10.1515/9783110326970.

Overview: Metaphilosophy is philosophy’s poor and neglected cousin. Philosophers are on the whole too busy doing philosophy to take time to stand back and consider reflectively how the project itself actually works. And they lead tend to produce texts without too much consideration of how this looks from the standpoint of the consumer. All this, it seems to be, affords good reason for attending to philosophical hermeneutics, reflecting on the issue of how philosophical texts are to be understood and interpreted.

Citation: Ortmann, J. Forthcoming. “But What is ‘performativity of science’? Austin, perlocutionary sequels, and referent malleability.” Philosophy of Science, 1–28. https://doi.org/10.1017/psa.2026.10211.

Abstract: Ever since John L. Austin, ‘performativity’ has meant many things to many scholars—something that did not change when phrases like ‘performativity of science’ became popular, obscuring various debates in philosophy of science. In this paper, I revisit Austin’s original use of the term and propose a nomenclature of three kinds of performativity in science. This revises earlier attempts to identify ‘authentic’ or ‘genuine’ performativity in science and instead advances an integrative disambiguation—one that preserves what scholars in STS and philosophy of science have found helpful about the concept while addressing demonstrable conceptual conflations.