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

How is a relational formal ontology relational? An exploration of the semiotic logic of agency in physics, mathematics, and natural philosophy

A speculative exploration of the distinction between a relational formal ontology and a classical formal ontology for modelling phenomena in nature that exhibit relationally-mediated wholism, such as phenomena from quantum physics and biosemiotics. Whereas a classical formal ontology is based on mathematical objects and classes, a relational formal ontology is based on mathematical signs and categories. A relational formal ontology involves nodal networks (systems of constrained iterative processes) that are dynamically sustained through signalling. The nodal networks are hierarchically ordered and exhibit characteristics of deep learning. Clarifying the distinction between classical and relational formal ontologies may help to clarify the role of interpretative context in physics (eg. the role of the observer in quantum theory) and the role of hierarchical nodal networks in computational models of learning processes in generative AI

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

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

What is active touch?

What is active touch? A common conception of active touch gives a rough but rather intuitive sketch. That is, active touch can be understood as mainly object-oriented, controlled movement. While parts or the totality of this characterization is espoused by an important number of researchers on touch, I will argue that this conception faces important challenges when we pay close attention to each of its features. I hold that active touch should be considered as before all else purposive. This view has its roots in the active sensing literature in robotics but will be amended to give insight into human touch in the natural world

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

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

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

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