Erik Weber

In this paper we chart epistemological similarities between shared function talk in biology and the engineering sciences, focusing on the notions of biological advantage function and technical advantage function. We start by showing that biological advantage function ascriptions are common in biology and that technical advantage function ascriptions are common in engineering science. We then proceed to show that these ascriptions have a very similar structure and that their epistemic value also is similar: both biological advantage function and technical advantage function ascriptions provide the means to answer what-would-happen-if questions. We develop and illustrate our claims with four case studies: two from biology, and two from engineering design research. Our results offer new insights into a relatively neglected issue in both philosophy of biology and technology, viz. assessing the explanatory and predictive utility of function ascriptions.

In this paper, we analyse the fruitfulness of Ronald Giere’s comparative model for causation in populations. While the original model was primarily developed to capture the meaning of causal claims in the biomedical and health sciences, we want to show that the model is not only useful in these domains, but can also fruitfully be applied to other scientific domains. Specifically, we demonstrate that the model is fruitful for characterizing the meaning of causal claims found in classical genetics, epidemiology and electoral sociology. Additionally, we propose an adapted comparative model which is needed to get a grip on higher level causal claims. We show that such claims are present in population genetics, (environmental) epidemiology and electoral systems research. We conclude with some reflections on the implications of our findings for the issue of causal pluralism.

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.

In the literature on scientific explanation, there is a classical distinction between explanations of particular facts and explanations of laws. This paper is about explanations of laws, more specifically about microexplanations of laws in physics. We investigate whether providing unificatory information has a surplus value in micro-explanations of physical laws. Unificatory information is information that provides ontological unification in the sense defined by Uskali Mäki. We argue that providing unificatory information may lead to explanations with more explanatory power (we use Jim Woodward’s concept of explanatory power for that) and that it may lead to more strongly supported explanations.En la literatura sobre explicación científica hay una distinción clásica entre explicaciones de hechos particulares y explicaciones de leyes. El presente artículo trata de las explicaciones de las leyes, más en concreto, de las microexplicaciones de las leyes en la física. Analizamos si proporcionar información unificadora posee un valor adicional en las microexplicaciones de las leyes físicas. La información unificadora es información que proporciona unificación ontológica en el sentido definido por Uskali Mäki. Argumentamos que proporcionar información unificadora puede llevar a explicaciones con mayor poder explicativo (aquí empleamos el concepto de poder explicativo de Jim Woodward), y también a explicaciones más firmemente apoyadas.

The scope of this paper can be clarified by means of a well-known phenomenon that is usually called ‘industrial melanism’: the fact that the melanic form of the peppered moth became dominant in industrial areas in England in the second half of the nineteenth century. Such changes in relative phenotype frequencies are important explananda for population biologists. Apart from trying to explain such changes over time, population biologists also often try to explain differences between populations, e.g. why yellow shell colour is dominant in certain colonies of land snails and almost absent in other colonies. The causal explanations that are given to address such explananda are the objects of analysis in this paper. Our primary aim is to explicate their structure: we want to capture the typical ingredients of causal explanations in population biology, and their organisation. Based on this explication, we discuss how natural selection fits into recent mechanical philosophy of science, and engage in the debate on the nature of evolutionary theory.

The general view is that metaphysical explanation is asymmetric. For instance, if resemblance facts can be explained by facts about their relata, then, by the asymmetry of explanation, these latter facts cannot in turn be explained by the former. The question however is: is there any reason to hold on to the asymmetry? If so, what does it consist in? In the paper we approach these questions by comparing them to analogous questions that have been investigated for scientific explanations. Three main asymmetry criteria have been proposed for the latter: (i) causation, (ii) unification, and (iii) explanatory dependence. We argue that the last criterion, but not the former two, can be of help to metaphysical explanation: metaphysical explanations are asymmetric if the explanatory dependence criterion (in modified format) holds of them.

Jonathan Schaffer has argued that a contrastive causal ontology is beneficial in juridical contexts: lawyers and judges should treat the causal relation as a quaternary relation, not as binary one. In this paper we investigate to what extent a contrastive causal ontology is beneficial in genetics and in physics. We conclude that it is beneficial in these scientific domains. We also point out that the nature of the benefit differs in the three context that we discuss. Key words: Contrastive causation, causation in genetics, causation in physics, Jonathan Schaffer.

This paper investigates the working-method of three important philosophers of explanation: Carl Hempel, Philip Kitcher and Wesley Salmon. We argue that they do three things: construct an explication in the sense of Carnap, which then is used as a tool to make descriptive and normative claims about the explanatory practice of scientists. We also show that they did well with respect to, but that they failed to give arguments for their descriptive and normative claims. We think it is the responsibility of current philosophers of explanation to go on where Hempel, Kitcher and Salmon failed. However, we should go on in a clever way. We call this clever way the “pragmatic approach to scientific explanation.” We clarify what this approach consists in and defend it.