When and why are motivational trade-offs evidence of sentience?

Motivational trade-off behaviours, where an organism behaves as if flexibly weighing up an opportunity for reward against a risk of injury, are often regarded as evidence that the organism has valenced experiences like pain. This type of evidence has been influential in shifting opinion regarding crabs and insects. Critics note that (i) the precise links between trade-offs and consciousness are not fully known; (ii) simple trade-offs are evinced by the nematode worm Caenorhabditis elegans, mediated by a mechanism plausibly too simple to support conscious experience; (iii) pain can sometimes interfere with rather than support making trade-offs rationally. However, rather than undermining trade-off evidence in general, such cases show that the nature of the trade-off, and its underlying neural substrate, matter. We investigate precisely how

How the “Habitable Zone” Frames Exoplanet Discovery Papers: A Quantitative Snapshot and an Epistemological Appraisal

This article investigates the uses of the concept of the habitable zone (HZ) in recent exoplanetary research. A corpus of articles (n=24) published between 2022 and 2024 was constructed, and each text was analyzed with regard to its mobilization of the HZ concept. The results show that the HZ functions in two distinct ways: as a heuristic tool, guiding research by providing operational boundaries for habitability, and as a presumptive device, where its unexamined use tends to impose the idea that planets located outside the HZ are, by default, uninhabitable. These usages demonstrate how the concept not only structures the design of scientific inquiries but also frames the broader imagination of planetary habitability and the search for life

How Theoretical Terms Effectively Refer

Scientific realists with traditional semantic inclinations are often pressed to explain away the distinguished series of referential failures that seem to plague our best past science. As recent debates make it particularly vivid, a central challenge is to find a reliable and principled way to assess referential success at the time a theory is still a live concern. In this paper, I argue that this is best done in the case of physics by examining whether the putative referent of a term is specifiable within the limited domain delineated by the range of parameters over which the theory at stake is empirically accurate. I first implement this selective principle into a general account of reference, building on Stathis Psillos's works. Then, I show that this account offers a remarkably reliable basis to assess referential success before theory change in the case of effective theories. Finally, I briefly show that this account still works well with other physical examples and explain how it helps us to handle problematic cases in the history of physical sciences

Cantor's Illusion simplified

This analysis shows Cantor's diagonal definition in his 1891 paper was not compatible with his horizontal enumeration of the infinite set M. The diagonal sequence was a counterfeit which he used to produce an apparent exclusion of a single sequence to prove the cardinality of M is greater than the cardinality of the set of integers N

Tatiana Ehrenfest-Afanassjewa’s Contributions to Dimensional Analysis

Tatiana Ehrenfest-Afanassjewa was an important physicist, mathematician, and educator in 20th century Europe. While some of her work has recently undergone reevaluation, little has been said regarding her groundbreaking work on dimensional analysis. This, in part, reflects an unfortunate dismissal of her interventions in such foundational debates by her contemporaries. In spite of this, her work on the generalized theory of homogeneous equations provides a mathematically sound foundation for dimensional analysis and has found some appreciation and development. It remains to provide a historical account of Ehrenfest-Afanassjewa's use of the theory of homogeneous functions to ground (and limit) dimensional analysis. We take as a central focus Ehrenfest-Afanassjewa's contributions to a debate on the foundations of dimensional analysis started by physicist Richard Tolman in 1914. I go on to suggest an interpretation of the more thoroughgoing intervention Ehrenfest-Afanassjewa makes in 1926 based on this earlier context, especially her limited rehabilitation of a "theory of similitude" in contradistinction to dimensional analysis. It is shown that Ehrenfest-Afanassjewa has made foundational contributions to the mathematical foundations and methodology of dimensional analysis, our conception of the relation between constants and laws, and our understanding of the quantitative nature of physics, which remain of value

Beyond transparency: computational reliabilism as an externalist epistemology of algorithms

This chapter is interested in the epistemology of algorithms. As I intend to approach the topic, this is an issue about epistemic justification. Current approaches to justification emphasize the transparency of algorithms, which entails elucidating their internal mechanisms –such as functions and variables– and demonstrating how (or that) these produce outputs. Thus, the mode of justification through transparency is contingent on what can be shown about the algorithm and, in this sense, is internal to the algorithm. In contrast, I advocate for an externalist epistemology of algorithms that I term computational reliabilism (CR). While I have previously introduced and examined CR in the field of computer simulations ([42, 53, 4]), this chapter extends this reliabilist epistemology to encompass a broader spectrum of algorithms utilized in various scientific disciplines, with a particular emphasis on machine learning applications. At its core, CR posits that an algorithm’s output is justified if it is produced by a reliable algorithm. A reliable algorithm is one that has been specified, coded, used, and maintained utilizing reliability indicators. These reliability indicators stem from formal methods, algorithmic metrics, expert competencies, cultures of research, and other scientific endeavors. The primary aim of this chapter is to delineate the foundations of CR, explicate its operational mechanisms, and outline its potential as an externalist epistemology of algorithms

AI4Science and the Context Distinction

“AI4Science” refers to the use of Artificial Intelligence (AI) in scientific research. As AI systems become more widely used in science, we need guidelines for when such uses are acceptable and when they are unacceptable. To that end, I propose that the distinction between the context of discovery and the context of justification, which comes from philosophy of science, may provide a preliminary but still useful guideline for acceptable uses of AI in science. Given that AI systems used in scientific research are black boxes, for the most part, we should use such systems in the context of discovery but not in the context of justification. The former refers to processes of idea generation, which may be unproblematically opaque whether they occur in human brains or artificial neural networks, whereas the latter refers to scientific methods by which scientific ideas are tested, confirmed, verified, and justified, which should be transparent

A No-Go Theorem for psi-ontic Models? No, Surely Not!

In a recent reply to my criticisms (Found Phys 55:5, 2025), Carcassi, Oldofredi and Aidala admitted that their no-go result for psi-ontic models is based on the implicit assumption that all states are equally distinguishable, but insisted that this assumption is a part of the psi-ontic models defined by Harrigan and Spekkens, and thus their result is still valid. In this note, I refute their argument again

Virtual Time and Execution of Algorithms in Static Networks

A concept for the emergence of a time-equivalent property from a static network of interconnected states is shown. This property is referred to as virtual time. For each state, a set of coefficients is defined, which locally represents the information embedded in the network’s connectivity. Network structures denoted as repellers feature successive splits into a steadily increasing number of quantum states. They convey an equivalent calculation of their static connectivity coefficients and virtual particles dynamically propagating within them. Strong indications are provided, that static networks are virtual Turing complete machines for algorithms with finite runtime. This opens up a wide range of possible encodings for said coefficients and motivates further research

Relationalism versus realism: a dilemma for relational quantum mechanics

Are absolute representations of reality---i.e., representations of reality from no particular point view---possible? Moore (1997) has offered abstract arguments for the following answer to this question: 'yes, invariably'. But there are questions regarding whether (and how) this conclusion can be compatible with modern physics, where absolute representations often seem hard to come by. These questions were taken up by Jacobs & Read (2025) in the context of classical spacetime physics; here, we turn our attention to quantum mechanics. In particular, when the arguments of Moore (1997) are brought into contact with the 'relational quantum mechanics' of Rovelli (1996) and collaborators, one finds that the latter is unstable: either it is not relational view, or it is not a realist view