New publications
Anna Martin
35th round
Inkeri Koskinen, University of Helsinki
Anna Alexandrova, Cambridge University
Conceptual Analysis Plus, in A. Curie, S. Veigl (Eds.), Methods in Philosophy of Science (105-124). Cambridge, US: MIT Press, 2025.
Alessandra, Basso, London School of Economics
Anna Alexandrova, Cambridge University
"Measurement Requires Compromises: The Case of Economic Inequality." Studies in History and Philosophy of Science 113, 2025, 88-97.
ABSTRACT
We examine considerations that enter into design and evaluation of measures in social science, categorizing them into four drivers: epistemic, ethical, pragmatic, and metrological. We call them drivers to highlight their role in guiding researchers’ decisions without determining them. Through an analysis of the World Inequality Report 2022, we reveal tensions among these drivers, illustrating the complex interplay between the various demands a measure must satisfy. Our analysis highlights the need for case-by-case compromises to address these tensions, as optimizing one driver often comes at the expense of another. We explore the extent to which these compromises shape measurement practice and the principles that guide researchers in balancing them. While existing literature on measurement assumes that tensions can be resolved with good practice and use, we argue that developing a good measure requires balancing multiple demands, recognising that it might be impossible to meet all of them simultaneously.
Daniel Kostić, Institute of Philosophy and Sociology, Polish Academy of Sciences & Theoretical Philosophy, Faculty of Philosophy, University of Groningen
Kareem Khalifa, University of California, Los Angeles
“Does Functional Connectivity Explain?” Synthese 206, 2025, 207. https://doi.org/10.1007/s11229-025-05296-w
ABSTRACT
Many successful explanations show how causally individuated parts are responsible for the occurrence of the phenomena that scientists seek to explain. On this view, parts that are chosen only by convention, and related only through correlations, cannot possibly figure in successful explanations. This is because without some form of causal grounding, it seems unintelligible why any explanatory relation between these parts and the phenomenon of interest would hold. This problem is particularly pronounced in functional connectivity models (FC) in neuroscience. These models typically represent time series of recurrent neural activity in conventionally determined spatial regions (as a network’s nodes) and synchronization likelihoods among these time series (as its edges). Many neuroscientists and philosophers maintain that because of this, FC models cannot provide explanations. We formulate this problem more precisely and then show that it rests on an impoverished interpretation of scientific models in general and FC models in particular. We then provide a positive account of how FC models provide a variety of neuroscientific explanations.
Kristian G. Barman, CLPS-Centre for Logic and Philosophy of Science, UGent, Ghent, Belgium
Sascha Caron, IMAPP, Radboud University and Nikhef, Nijmegen, The Netherlands
Emily Sullivan, Utrecht University, Utrecht, The Netherlands
Henk W. de Regt, Institute for Science in Society, Radboud University, Nijmegen, The Netherlands
Roberto Ruiz de Austri, 5 Instituto de Física Corpuscular (IFIC), CSIC-UV, Valencia, Spain
Mieke Boon, University of Twente, Enschede, The Netherlands
Michael Färber, TU Dresden & ScaDS.AI, Dresden/Leipzig, Germany
Stefan Fröse, ErUM-Data-Hub & TU Dortmund University, Dortmund, Germany
Tobias Golling, University of Geneva, Geneva, Switzerland
Luis G. Lopez, Munich Center for Mathematical Philosophy, LMU Munich, Munich, Germany
Faegheh Hasibi, Computing and Information Science, Radboud University, Nijmegen, The Netherlands
Lukas Heinrich, Physics Department, TUM School of Natural Sciences, Technical University of Munich, Munich, Germany
Andreas Ipp, TU Wien, Vienna, Austria
Rukshak Kapoor, Thapar Institute of Engineering and Technology (TIET), Patiala, India
Gregor Kasieczka, Universität Hamburg, Hamburg, Germany
Daniel Kostić, Institute of Philosophy and Sociology at Polish Academy of Sciences, Warsaw, Poland
Michael Krämer, RWTH Aachen University, Aachen, Germany
Jesus Marco, Institute of Physics of Cantabria (IFCA), CSIC-UC, Santander, Spain
Sydney Otten, Ippen Digital, Munich, Germany
Pawel Pawlowski, Universidad de Oviedo and ICTEA, Oviedo, Spain
Pietro Vischia, Universidad de Oviedo and ICTEA, Oviedo, Spain
Erik Weber, GRAPPA, Institute of Physics, University of Amsterdam, Amsterdam, The Netherlands
Christoph Weniger, GRAPPA, Institute of Physics, University of Amsterdam, Amsterdam, The Netherlands
Large Physics models: Towards a Collaborative Approach With Large Language Models and Foundation Models, The European Physical Journal C 85, article number 1066, 2025. https://link.springer.com/article/10.1140/epjc/s10052-025-14707-8
ABSTRACT
This paper explores the development and evaluation of physics-specific large-scale AI models, which we refer to as large physics models (LPMs). These models, based on foundation models such as large language models (LLMs) are tailored to address the unique demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing insights from physical theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability through testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining large physics models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models.
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Volodymyr Kuznetsov, H. Skovoroda Institute of Philosophy, National Academy of Sciences of Ukraine (Kyiv) https://orcid.org/0000-0002-8193-8548
Arina Oriekhova, Taras Shevchenko National University of Kyiv https://orcid.org/0000-0002-5846-8787
Maksym Sahan, Taras Shevchenko National University of Kyiv https://orcid.org/0009-0007-9155-0882
Philosophy of Scientific Theories as a Branch of Theoretical Philosophy. Part I, Sententiae, 44(2), 2025, 224–252. https://doi.org/10.31649/sent44.02.224
ABSTRACT
Interview by Arina Oriekhova and Maksym Sahan with Volodymyr Kuznetsov, dedicated to the state of the Ukrainian philosophy of science in the 1960s–1980s. It also includes 1) the explanation of its title; 2) the evaluation of the current philosophy of science from the perspective of the philosophy of scientific theories; and 3) the explanation of some reasons why Western philosophers are unfamiliar with Ukrainian philosophy of science. I can translate the mentioned fragments from Ukrainian into English, with some additions. If you're interested in these, please send me a message to vlad...@gmail.com
Martin Justin, University of Maribor
Dunja Šešelja, Ruhr University Bochum
Christian Straßer, Ruhr University Bochum
Borut Trpin, LMU Munich & University of Maribor & University of Ljubljana
The Mix Matters: Exploring the Interplay Between Epistemic and Zetetic Norms in Scientific Disagreement. Forthcoming in The British Journal for the Philosophy of Science. https://doi.org/10.1086/737742
ABSTRACT
What is the rational response to a scientific disagreement? Many epistemologists argue that disagreement with an epistemic peer should generally lead to conciliation by lowering confidence in the disputed belief or even suspending judgment altogether. Although this conciliatory approach is widely regarded as a norm of individual rationality, its value in the context of collective scientific inquiry is less clear. Some have even raised concerns that conciliating in scientific disagreements may slow progress or reduce the efficiency of inquiry. In this article, we introduce a novel agent-based model that captures key aspects of scientific disagreement by incorporating both epistemic norms, which govern belief revision, and zetetic norms, which guide how scientists pursue inquiry. Our results indicate that the effects of conciliating in the face of disagreement—whether detrimental or beneficial—depend on the zetetic norms that scientists follow. When scientists focus on exploiting the hypothesis that they believe is most likely to succeed, remaining steadfast is more effective. However, with exploratory scientists, conciliation does not negatively affect group performance. These findings highlight the critical role of zetetic norms in determining the rational response to disagreement in scientific practice.
Mariangela Zoe Cocchiaro, Jagiellonian University
Borut Trpin, LMU Munich & University of Maribor & University of Ljubljana
The Puzzle of Scientific Disagreement. European Journal for Philosophy of Science, 15(2), article number 35, 2025. https://doi.org/10.1007/s13194-025-00662-4
ABSTRACT
Scientists often find themselves in disagreement with their peers, yet continue to hold fast to their views. While Conciliationism, a prominent position in the epistemology of disagreement, condemns such steadfastness as epistemically irrational, philosophers of science often defend it as rationally permissible–indeed, even beneficial for scientific progress. This tension gives rise to what we call the puzzle of scientific disagreement.
Borut Trpin, LMU Munich
Affirming the Explanandum, Analysis, 84(4), 2024, 788-796. https://doi.org/10.1093/analys/anae003
ABSTRACT
Affirming the consequent is an inferential pattern in which one infers the antecedent of a given conditional from its consequent. Abductive inference is structurally similar: given some evidence, one infers a hypothesis that explains the evidence. I show that a Bayesian analysis of affirming the consequent helps us understand under which conditions abduction may be justified. This provides a Bayesian vindication of explanatory inference.
Mario Günther, LMU Munich & Australian National University
Borut Trpin, LMU Munich & University of Maribor
Bayesians Still Don’t Learn From Conditionals, Acta Analytica, 38(3), 2023, 439-451. https://doi.org/10.1007/s12136-022-00527-y
ABSTRACT
One of the open questions in Bayesian epistemology is how to rationally learn from indicative conditionals (Douven, 2016). Eva et al. (Mind 129(514):461–508, 2020) propose a strategy to resolve this question. They claim that their strategy provides a “uniquely rational response to any given learning scenario”. We show that their updating strategy is neither very general nor always rational. Even worse, we generalize their strategy and show that it still fails. Bad news for the Bayesians.
Dinçer Çevik, Muğla Sıtkı Koçman Üniversitesi, Turkey
How Could Models in Economics Possibly Provide ‘How-Actually, Explanations?, Problemos, 107, 2025. https://doi.org/10.15388/Problemos.2025.107.6
ABSTRACT
Much of the debate about model-based explanations in economics revolves around How-Possibly Explanations (HPEs). HPEs propose a potential way in which something could occur. I argue that these debates often occur without adequately considering the various ways in which economic models can provide How-Actually Explanations (HAEs). HAEs concentrate on what actually happens, providing explanations based on real-world events. I suggest that adopting a pluralistic and pragmatic approach is among the most effective methods for exploring the potential of HAEs in economics. To support my argument, I will use two case studies from microeconomics and macroeconomics. I contend that these case studies provide grounds to believe that a pluralist and pragmatist perspective could clarify how models in economics might offer HAEs.
Dinçer Çevik, Muğla Sıtkı Koçman Üniversitesi, Turkey
Philosophy of Archaeology, in A. Tucker, D. Cernín (Eds.), The Bloomsbury Handbook of the Philosophy of the Historical Sciences and Big History, (433-450). London: Bloomsbury Publishing, 2025.
ABSTRACT
Archaeology has been a philosophically and methodologically self- reflective discipline. As Merilee Salmon (1982b: 2) put it, archaeologists’ philosophical self-reflections about their discipline can be traced back to the concerns mid-century American archaeology had about appropriate classificatory schemes. Conversely, archaeology periodically has attracted the attention of philosophers. For instance, Carl Gustav Hempel took into consideration the relationship between archaeological inference and laws at the end of his classic ‘The Function of General Laws in History’ (Hempel 1942: 48). Even earlier, in the nineteenth century, William Whewell discussed comparative archaeology as an example for the ‘palaetiological sciences’ (Whewell 1847: 637).
Aviezer Tucker, Harvard University
David Cernín, University of Ostrava
(Eds.), The Bloomsbury Handbook of Big History: The Philosophy of the Historical Sciences. Bloomsbury Academic, 2025.
ABSTRACT
Big History expands the scope of historiography to study all the past, from the Big Bang to the present. Big History is decidedly non-anthropocentric, recognising that humans appeared only very recently from a much deeper past. The Bloomsbury Handbook of Big History brings together an international cast of leading and emerging scholars from a range of disciplines to provide the first comprehensive and balanced exploration of this new and increasingly significant field. The handbook considers the ways in which Big History broadens the scope of evidence historians use. It reveals how Big History allows for the use of information signals from the past, such as material remains, languages, ancient DNA, and fossils, using recently discovered techniques such as carbon-dating, the extraction of ancient DNA, and the analysis of background radiation from the origin of the universe. Understanding Big History requires an examination of the ontology and epistemology of the past and its scientific inference and representation; chapters on the ontology of the past, historical information, historiographic experiments and predictions, the origin and the end of history, historical counterfactuals, contingency, necessity, and determination and under-determination of the past are included to facilitate this understanding. The handbook also provides a sustained analysis of the theoretical assumptions associated with synthetic historical science: the syntheses of genetics, linguistics, and archaeology to infer human prehistory, quantum physics and cosmology to infer the origin of the universe, and environmental science and biology to infer the history of life; as well as the distinct historical sciences and their methods: cosmology, evolutionary biology, geology, archaeology, historical linguistics, and human historiography. This ground-breaking volume is vital reading for all historians, philosophers and historical scientists interested in becoming more scientifically self-conscious when adopting philosophical, theoretical, and methodological approaches to the past.
Miroslav Vacura, Department of Philosophy, Prague University of Economics and Business, Prague, Czech Republic
Bridging the Emotional Gap: Philosophical Insights into Sensual Robots with Large Language Model Technology, Open Philosophy, 8(1), 2025. https://doi.org/10.1515/opphil-2025-0082
ABSTRACT
This article explores the potential of using large
language model (LLM) technology in the development of robots to enhance their
sociability and sensuality, while also addressing a number of general issues
related to human–machine interaction. Based on a philosophical delineation of
the foundations of sensuality, the study defines four fundamental
characteristics necessary for robots to convincingly simulate social,
emotional, and sensory interactions. The text provides an overview of recent
advances in social robots and briefly describes LLM technology from both
technical and philosophical perspectives. The article highlights the
transformative role of LLM in enabling communication, giving an overview of the
eight characteristics in which the deployment of this technology represents an
advance in human–robot interaction. Robots equipped with LLM technology offer
more intuitive and personalized interaction, building on previous communication
history, can flexibly adapt to changes in the conversational situation, and
understand the subtle nuances of language. They are able to interact
emotionally, intimately, and sensually without being judgmental or rejecting
users, achieving an emotional connection to the user that robots using
traditional technologies have not been able to. At the same time, these new
robots have a vast amount of knowledge at their disposal that they are able to
use very quickly in communication.