Daniel Kostic

What I Wish I Had Known About Germany Earlier A German newspaper commissioned an article from me but then refused to publish it.

Having lived in Germany during my PhD, I find it really difficult to disagree with Weiwei’s eloquent assessment. It’s hilariously on brand that they cancelled his piece. Still, there are many redeeming things about Berlin, even about its bureaucracy.
hyperallergic.com/1050197/what...

still time to register www.philsci.org/psa_around_t...

Check out the schedule of the online conference PSA Around the World Eastern Europe (pm of Nov 6,14,22). A collaboration between @philsci.bsky.social and @eenphilsci.bsky.social it showcases #philsci from, about, and otherwise connected to the region. Wonderful names and titles, looking forward!

The report of Dagstuhl Seminar 25142 "Explainability in Focus: Advancing Evaluation through Reusable Experiment Design" is now published as part of the periodical Dagstuhl Reports: drops.dagstuhl.de/entities/doc...

Organized by: Simone Stumpf, Elizabeth Daly and Stefano Teso

#philsci

That misrepresents our argument. We argue that rich interpretations (contrastive explanantia, qualitative reasoning, and sharpening) are necessary for establishing proper counterfactual dependencies in any type of explanation. The full open-access paper is a better source than just the abstract.

Studying philosophy does make people better thinkers, according to new research on more than 600,000 college grads Philosophers are fond of saying that their field boosts critical thinking. Two of them decided to put that claim to the test.

Did a philosopher write this

theconversation.com/studying-phi...

Current Workshop CFA: 8th Scientific Understanding and Representation (SURe) annual workshop   Call for abstracts        We invite authors to submit abstracts of up to 750-words for the upcoming...

CFA: the 8th SURe workshop will take place May 27-29, 2026, at the IFiS PAN in Warsaw.

Submission deadline: 20 January 2026.

For more info visit: sure-workshop.weebly.com/current-work...

@philsci.bsky.social @epsaphilsci.bsky.social

Coherence as a constraint on scientific inquiry - Synthese We investigate the epistemic role of coherence in scientific reasoning, focusing on its use as a heuristic for filtering evidence. Using a novel computational model based on Bayesian networks, we simulate agents who update their beliefs under varying levels of noise and bias. Some agents treat reductions in coherence as higher-order evidence and interpret such drops as signals that something has gone epistemically awry, even when the source of error is unclear. Our results show that this strategy can improve belief accuracy in noisy environments but tends to mislead when evidence is systematically biased. We explore the implications for the rationality of coherence-based reasoning in science.

Just published: Coherence as a Constraint on Scientific Inquiry, Synthese (with @martinjustin.bsky.social).
TL,DR: coherence considerations can be good or bad in scientific inquiry. Our paper suggests how to determine when it is the former rather than the latter.
link.springer.com/article/10.1...

We then provide a positive account of how FC models provide a variety of neuroscientific explanations.

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.

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).

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.

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.

Does functional connectivity explain? - Synthese 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.

My paper with Kareem Khalifa "Does functional connectivity explain?" is now published in open access in Synthese: shorturl.at/vqOjq
@philsci.bsky.social @epsaphilsci.bsky.social @hoposjournal.bsky.social @ishpssb.bsky.social @danisbassett.bsky.social @nunetsi.bsky.social @sfiscience.bsky.social

B&W hard copy of a volume of Aristotelian Society 2025

Table of contents and first page of an article

Didn’t expect to receive a hard copy of this volume. So satisfying to hold. An honour to philosophise alongside these wonderful thinkers. Here’s the link academic.oup.com/aristotelian...

It really is an amazing speech

Just as large experimental collaborations transformed physics, we propose a similar collective effort to build AI systems that can deepen our understanding of the universe.

Our vision is that LPMs will act as true collaborators in physics research, helping to generate hypotheses, design experiments, analyze complex data, and open up new directions of inquiry.

We outline a roadmap built on three interconnected pillars: developing models tailored to physics, evaluating their accuracy and reliability through rigorous benchmarks, and reflecting philosophically on what it means for AI to contribute to scientific understanding.

These models would be trained to handle the unique demands of physics—mathematical reasoning, data from experiments and simulations, and the synthesis of theories and literature.

@fhasibi.bsky.social @mikraemer.bsky.social @pietrovischia.bsky.social

We argue that the physics community should not rely solely on commercial large language models but instead take the lead in developing dedicated Large Physics Models (LPMs).

Large physics models: towards a collaborative approach with large language models and foundation models - The European Physical Journal C 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.

Our paper "Large physics models: towards a collaborative approach with large language models and foundation models" is now published online! @philsci.bsky.social
@epsaphilsci.bsky.social @hoposjournal.bsky.social @ishpssb.bsky.social @henkderegt.bsky.social @lglopez.bsky.social

Indeed, lucky you! The first stanza of Ghetto Defendant by The Clash just popped into my head and now I can’t get it out:

Starved in metropolis
Hooked on necropolis
Addict of metropolis
Do the worm on the acropolis
Slam dance cosmopolis
Enlighten the populace

What a strangely happy association.

Pianeta Lab: Soluzioni Concrete per le Sfide Socio-Ambientali Pianeta Lab è il laboratorio dove nascono idee e diventano azioni. Un luogo aperto che unisce attivistə, cittadinə, imprese, artistə e istituzioni per affrontare insieme le grandi sfide sociali e ambi...

Italians and beyond: this week we launch Pianeta Lab in Modena and Bologna, an experimental space to give people a voice in developing technology & policies that affect our everyday lives. We need your help to get this off the ground! Please consider donating: www.ideaginger.it/progetti/pia...

Thanks, Insa!

The PSA’s “Office Hour” Returns - Daily Nous The Philosophy of Science Association (PSA) is continuing its excellent expert "office hour" series this semester---a program other academic associations should consider adopting. "The PSA Office Hour aims to facilitate interactions between our graduate student membership and prominent philosophers of science, and in a more controlled, accessible, and carbon-conscious setting than is provided by our

The Philosophy of Science Association makes experts on various topics available for conversation with graduate students anywhere with its "Office Hour" program. Other associations should consider doing this.

Cool, it’s on its way via email. I’d be happy to read your draft too, if you think another pair of eyes would help.

Thanks, Holly! I can send you a pre-print, if you want.

Thanks!