Idealization

This article argues for an anti-deflationist view of scientific representation. Our discussion begins with an analysis of the recent Callender–Cohen deflationary view on scientific representation. We then argue that there are at least two radically different ways in which a thing can be represented: one is purely symbolic, and therefore conventional, and the other is epistemic. The failure to recognize that scientific models are epistemic vehicles rather than symbolic ones has led to the mistaken view that whatever distinguishes scientific models (...) from other representational vehicles must merely be a matter of pragmatics. It is then argued that even though epistemic vehicles also contain conventional elements, they do their job of demonstration in spite of such elements. (shrink)

This article critically evaluates Itzhak Gilboa, Andrew Postlewaite, Larry Samuelson, and David Schmeidler’s account of economic models. First, it gives a selective overview of their argument, highlighting its emphasis on similarity and their oversight of the role of idealizations in economics. Second, it proposes a sketch of an account of models as arguments and argumentative devices. This account not only sheds light on Gilboa et al.’s approach, including its shortcomings, but also identifies key challenges in model-based inference, suggesting a fresh (...) perspective on the uses of models in economics for diverse objectives. (shrink)

This paper examines questions about credence and belief through the lens of the work of John Locke. Locke considers something very much like credence that he calls “degrees of assent.” This paper discusses Locke’s notion of degrees of assent and potential descriptive and normative connections between belief and credence in light of his remarks. I’ll make four main points. First, Locke’s views and arguments reflect many popular topics in contemporary epistemology. Second, Locke was a proponent of the credence-first view of (...) the belief-credence connection. Third, I’ll discuss how Locke seems to endorse both normative and descriptive thresholds for belief—and why I think he shouldn’t have endorsed both. Finally, I’ll argue that while Locke’s epistemology starkly contrasts with the ideal epistemology of his philosophical predecessors, there’s another sense in which Locke’s epistemology is idealized. (shrink)

The focus in the literature on scientific explanation has shifted in recent years towards modelbased approaches. The idea that there are simple and true laws of nature has met with objections from philosophers such as Nancy Cartwright (1983) and Paul Teller (2001), and this has made a strictly Hempelian D-N style explanation largely irrelevant to the explanatory practices of science (Hempel & Oppenheim, 1948). Much of science does not involve subsuming particular events under laws of nature. It is increasingly recognized (...) that science across the disciplines is to some degree a patchwork of scientific models, with different methods, strategies, and with varying degrees of successful prediction and explanation. And so accounts of scientific explanation have reflected this change of perspective and model-based approaches have flourished in the explanation literature (Batterman, 2002b; Bokulich, 2008; Craver, 2006; Woodward, 2003). (shrink)

Disagreement about how best to think of the relation between theories and the realities they represent has a longstanding and venerable history. We take up this debate in relation to the free energy principle (FEP) - a contemporary framework in computational neuroscience, theoretical biology and the philosophy of cognitive science. The FEP is very ambitious, extending from the brain sciences to the biology of self-organisation. In this context, some find apparent discrepancies between the map (the FEP) and the territory (target (...) systems) a compelling reason to defend instrumentalism about the FEP. We take this to be misguided. We identify an important fallacy made by those defending instrumentalism about the FEP. We call it the literalist fallacy: this is the fallacy of inferring the truth of instrumentalism based on the claim that the properties of FEP models do not literally map onto real-world, target systems. We conclude that scientific realism about the FEP is a live and tenable option. (shrink)

In the philosophy of science, increasing attention has been given to the methodological novelties associated with the study of complex systems. However, there is little agreement on exactly what complex systems are. Although many characterizations of complex systems are available, they tend to be either impressionistic or overly formal. Formal definitions rely primarily on ideas from the study of computational complexity, but the relation between these formal ideas and the messy world of empirical phenomena is unclear. Here, I give a (...) definition of complex systems that draws on algorithmic complexity theory, but also provides a way of interpreting the formal idea in an empirical setting. I then use the definition to show that two canonical forms of scientific idealization are empirically inadequate when applied to complex systems. The inadequacy of these forms of idealization is shown to be the primary reason that complex systems require novel methods. Moreover, this demand for novel methods helps explain the rise of complex systems science as an autonomous discipline. (shrink)

The fact that many scientific models are idealised, and therefore incorporate known falsehoods, seems to undermine the idea that science aims at truth. Various authors have proposed different solutions to this problem: they have claimed that idealisations are harmless because models can be "de-idealised", that the function of idealisations is to isolate explanatory relevant factors, or that idealised models still convey veridical modal information. I argue that even if these strategies succeed in making idealisations compatible with theoretical truth, a deeper (...) problem remains: the fact that idealisations improve the explanatory power of models contradicts the main argument for scientific realism, which is based on the idea that explanatory virtues are truth-conducive. There does not seem to be any simple solution to this problem. (shrink)

Recent work in epistemology and philosophy of science conceptualizes explainable AI (XAI) tools as ‘models of models’, that is representations of opaque machine learning models whose value is assessed by their representation fidelity, and by the type of understanding they convey (e.g., explanatory, or objectual). In this work, we argue that this representation-centred view rests on a mistaken way of framing what XAI is. The problem XAI addresses, we claim, is not related to a lack of understanding, but of epistemic (...) incapacitation. Stated otherwise, XAI tackles the inability to use opaque models for all the scientific purposes their predictive accuracy should make possible in scientific contexts. Drawing on the literature on ‘models for’ and on models as epistemic artefacts, we propose an alternative account we name XAI-as-mediators. In doing so, we shift the question from “How faithful does the tool represent a model?” to “What does the tool enable the user to do with the model?”. We then distinguish between two forms of mediation: access mediation, which restores epistemic access to a model’s inner structure and behaviour, and semantic mediation, which aligns the model’s reasoning space with a given domain’s conceptual and inferential practices, and also specify the epistemic virtues proper to each type of mediation. Finally, we situate the proposed framework against Morgan and Morrison’s ‘models-as-mediators’ account, and show how it can answer Watson's challenge against XAI. (shrink)

A signal development in contemporary physics is the widespread use, in explanatory contexts, of highly idealized models. This paper argues that some highly idealized models in physics have genuine explanatory power, and it extends the explanatory role for such idealizations beyond the scope of previous philosophical work. It focuses on idealizations of nonlinear oscillator systems.

This paper constitutes a radical departure from the existing philosophical literature on models, modeling-practices, and model-based science. I argue that the various entities and practices called 'models' and 'modeling-practices' are too diverse, too context-sensitive, and serve too many scientific purposes and roles, as to allow for a general philosophical analysis. From this recognition an alternative view emerges that I shall dub model anarchism.

In this paper, I begin with a discussion of Giere’s recent work arguing against taking models as works of fiction. I then move on to explore a spectrum of scientific models that goes from the obviously fictional to the not so obviously fictional. And then I discuss the modeling of the unobservable and make a case for the idea that despite difficulties of defining them, unobservable systems are modeled in a fundamentally different way than the observable systems. While idealization and (...) approximation is key to the making of models for the observable systems, they are in fact inoperable, at least not straightforwardly so, regarding models for the unobservable. And because of this point, which is so far neglected in the literature, I speculate that factionalism may have a better chance with models for the unobservable. (shrink)

In this paper I argue against a deflationist view that as representational vehicles symbols and models do their jobs in essentially the same way. I argue that symbols are conventional vehicles whose chief function is denotation while models are epistemic vehicles whose chief function is showing what their targets are like in the relevant aspects. It is further pointed out that models usually do not rely on similarity or some such relations to relate to their targets. For that referential relation (...) they reply instead on symbols (names and labels) given to them and their parts. And a Goodmanian view on pictures of fictional characters reveals the distinction between symbolic and model representations. (shrink)

In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual conditionals that can exhibit different degrees of contingency. We use the idea of possible worlds to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, it is this structure what helps explain why idealizations, despite being deformations of reality, are so successful (...) in scientific practice. (shrink)

I show how omissions lead to robustness and can justify distortions, and I give inferentially relevant explications of abstraction and idealization. Abstraction is explicated as the omission of all and only those claims that use a specific vocabulary; idealization is explicated as the distortion of only those claims that use a specific vocabulary. With these explications, abstraction can justify idealization. As examples of how abstraction justifies idealization and leads to robustness, I discuss Beauchamp and Childress's four principles of biomedical ethics (...) and the qualitative treatment of the Schrödinger equation. (shrink)

Biomarkers are central to the practice of precision oncology, which looks to novel biomarkers to ‘personalize’ cancer care. Philosophers have highlighted epistemic issues surrounding biomarkers but a general account of their role in clinical reasoning is lacking. This article examines biomarker use in clinical reasoning through the lens of abstraction. I propose clinical abstraction as a descriptive and normative account of reasoning with biomarkers that overcomes epistemic and ethical problems raised in the literature.

Why care about ideal epistemic norms? Why not merely care about norms that agents like us can actually meet? In this paper, I make two claims. First, I argue that, if we want robust epistemic norms, we can’t just do idealised epistemology. We need to confirm that the results and observations that are central in idealised epistemology also obtain in other contexts. Second, I argue that, if we really want to capture the fundamental nature of epistemic normativity, we need the (...) sort of frameworks that come from non-ideal epistemology. I develop this argument in response to ideas put forward by Jennifer Carr (2022) surrounding the distinctive properties of epistemic ideals. (shrink)

In this paper, I shall provide a novel response to the argument from context-sensitivity against truth-conditional semantics. It is often argued that the contextual influences on truth-conditions outstrip the resources of standard truth-conditional accounts, and so truth-conditional semantics rests on a mistake. The argument assumes that truth-conditional semantics is legitimate if and only if natural language sentences have truth-conditions. I shall argue that this assumption is mistaken. Truth-conditional analyses should be viewed as idealised approximations of the complexities of natural language (...) meaning. From this perspective, disparity between the scientific model and its real-world target is to be expected. I elaborate on what such an approach to semantics would look like. (shrink)

The psychological immune system is said to be a suite of cognitive traits and defense mechanisms designed to protect the self from affectively threatening information, at the expense of tracking the truth. The PIS construct has been advanced by authors such as Norman et al. (2024) and Sedikides (2021) as referring to a real psychological system. I argue for ontological skepticism about the PIS but defend the usefulness of the concept as an idealization (Potochnik 2017) that illuminates psychological causal patterns. (...) Lastly, I argue that work on the PIS can productively refine Tekin (2025)’s MuSe model of the self. (shrink)

Deep Neural Networks and other AI systems engineered using advanced machine learning techniques can tackle a wide range of tasks with proficiency that seems to match and even surpass human ability. Yet they are also notoriously opaque, and the worries surrounding their opacity have given rise to the burgeoning field of explainable artificial intelligence, or XAI, with its large variety of explainability methods. This includes post-hoc explainability methods which purport to explain opaque AI systems on the basis of their input-output (...) behaviour alone, without looking into their inner workings. But many authors have expressed deep scepticism about post-hoc explainability. In this paper, we explore the two main objections that the sceptics have raised and argue that we shouldn’t be swayed by them. Using ideas from recent literature on scientific explanation, we draw a parallel between post-hoc explainability methods and idealized models in science and use the parallel in our reply to the sceptics. (shrink)

This article examines how artistic simplicity can provide environmental understanding. Focusing on aesthetic distillation, a basic kind of simplification in representational and non-representational art that involves isolating and amplifying the core aesthetic features of a source of inspiration, I argue that artistic simplicity can embody an understanding of the sort of organic fertility or vitality in nature that fascinates artists ranging from Jean Arp to Andy Goldsworthy. Historically, the use of simple geometric or biomorphic forms in abstract art has often (...) been linked to an ideal of participating in natural processes, versus depicting them. In this light, I argue that aesthetic distillation offers understanding of organic fertility by exemplifying it – or, as Arp says, ‘producing like a plant’. This suggests a distinctive kind of naturalism: artists like Arp and Goldsworthy use aesthetic distillation to reveal how artistic creativity and aesthetic impact are continuous with nonhuman environmental processes, self-referentially displaying the naturalness of their work. Finally, I extend this framework to better account for abiotic nature, concluding that aesthetic distillation can equally be used to exemplify processes of concentration and amplification in the inorganic environment. This naturalistic understanding, embodied in artistic simplicity, counteracts human exceptionalism and disregard for nonliving nature. (shrink)

Machine learning (ML) models are increasingly discussed in the philosophy of science in terms of idealization and even by analogy with toy models. I argue that this framing conflates idealization with approximation. Once the relevant ML target is specified as relations among features represented in data---relative to a target system---the claim that ML models are dissimilar to their targets becomes too coarse. Scientific ML models can and often must be approximately similar to their targets in the relevant respects. Approximation failure (...) therefore provides the more direct standard of assessment, and ML models do not function as toy models. (shrink)

There is a question of whether de-idealization is needed for justified use of -- for 'checking' -- idealizations. We argue that the standard philosophical account of de-idealization has become too idealized, but that this does not preclude the possibility of justificatory practices which show how models can be used to make inferences about the world. In turn, motivated by examples in physics, we provide a more expansive and practice-driven account of de-idealization by relaxing the standards for closeness to more realistic (...) theoretical items, identifying at least three kinds of procedures for de-idealization: intra-model, inter-model, and measurement de-idealizations. These examples highlight how idealizations can be -- and indeed have been -- scrutinized within physics without appealing to the philosopher's idealized notion of de-idealization. (shrink)

In contrast to the syntactic conception of scientific theories, the semantic conception holds that theories are not statements about the world, but families of models. Recent debates have tended to blur the differences between these two views. Practice-oriented philosophers of science have also challenged both views on the ground that models are central to science, but autonomous from theories. However, they have not proposed any alternative. This article is an attempt to sharpen the challenges faced by the semantic view in (...) order to pave the way for a truly pragmatic conception of theories. I examine different ways in which a focus on models could make the semantic view different from the syntactic view. In the end, they share the same resources to individuate theories, they account just as well for different layers of interpretation, and they face the same issues regarding the relation between formal representation and informal experience. I argue that the only relevant contrast lies not between linguistic and non-linguistic representation, but between descriptive and schematic representation, but this ultimately favours a pragmatic view by implying a role for context in theoretical interpretation. I sketch an account of schematic representation that could serve as a basis for a pragmatic conception of theories. (shrink)

Explainable AI (xAI) methods are important for establishing trust in using black-box models. However, recent criticism has mounted against current xAI methods that they disagree, are necessarily false, and can be manipulated, which has started to undermine the deployment of black-box models. Rudin (2019) goes so far as to say that we should stop using black-box models altogether in high-stakes cases because xAI explanations ‘must be wrong’. However, strict fidelity to the truth is historically not a desideratum in science. Idealizations (...) – the intentional distortions introduced to scientific theories and models – are commonplace in the natural sciences and are seen as a successful scientific tool. Thus, it is not falsehood qua falsehood that is the issue. In this paper, I outline the need for xAI research to engage in idealization evaluation. Drawing on the use of idealizations in the natural sciences and philosophy of science, I introduce a novel framework for evaluating whether xAI methods engage in successful idealizations or deceptive explanations (SIDEs). SIDEs evaluates whether the limitations of xAI methods, and the distortions that they introduce, can be part of a successful idealization or are indeed deceptive distortions as critics suggest. I discuss the role that existing research can play in idealization evaluation and where innovation is necessary. Through a qualitative analysis we find that leading feature importance methods and counterfactual explanations are subject to idealization failure and suggest remedies for ameliorating idealization failure. (shrink)

I argue that ML models used in science function as highly idealized toy models. If we treat ML models as a type of highly idealized toy model, then we can deploy standard representational and epistemic strategies from the toy model literature to explain why ML models can still provide epistemic success despite their lack of similarity to their targets.

Moral understanding has typically been defined as grasping the explanation, q, for some proposition, p, where p states that some action is morally right (or wrong). This article deals with an underdiscussed point within the literature on moral understanding: the degree of moral understanding one has deepens with the more moral reasons that one grasps, whereby these reasons not only consist of those that speak in favor of an action’s moral permissibility but also those speaking against. I argue for a (...) surprising and important implication of this: having a deep degree of moral understanding can make it harder to carry out the right action. Furthermore, I propose that we should think of our pursuit of moral understanding in an analogous way as to how some have thought of scientific understanding: There may be good reasons to fail to appreciate all of the actual moral reasons that in fact exist; sometimes we should seek a surfaced-level moral understanding instead of something deeper. Just as idealizations used within science – which can involve deviations from the truth – can help us achieve scientific understanding, so too we might restrict the moral reasons that we seek to grasp in pursuit of moral understanding. (shrink)

Feynman diagrams are used to calculate scattering amplitudes in quantum field theory, where they simplify the derivation of individual terms in the corresponding perturbation series. Considered mathematical tools with an approximative character, the received view in the philosophy of physics denies that individual diagrams can represent physical processes. A different story, however, can be observed in physics practice. From education to high-profile research publications, Feynman diagrams are used in connection with particle phenomena without any reference to perturbative calculations. In the (...) first part of the paper, I argue that this illuminates an additional use of Feynman diagrams that is not calculatory but representational. It is not a possible translation into mathematical terms that prompts this practice but rather the epistemic insights into the target phenomenon that the diagrams provide. Based on this practical use, I intend to push back against the received view. In the second part of the paper, I conceptualize the representative use of Feynman diagrams as models that provide modal understanding of their associated target phenomena. The set of Feynman diagrams corresponding to an interaction is taken as a possibility space whose dependency relations can be analysed, allowing an agent to grasp possible target behaviour, leading to understanding. In clearly separating the diagrams from perturbative calculations for their use as a model, the concerns that hinder a representative reading can be resolved. (shrink)

Philosophical work can be analyzed from two points of view, the historical and the systematic one. Papers gathered in this book examine the work of Francesco Coniglione from these two poles of philosophical analysis. From the historical point of view, the contributors ask what Coniglione meant when he made certain statements. From the systematic point of view, a given statement is ascribed a particular meaning because it provides the answer to the interpreter’s questions, which are culturally significant for the interpreter (...) himself and the society he is living in. This volume gathers work that highlights the interpretive dimensions of philosophical inquiry by focusing on historical and systematic interpretation in the corpus of Francesco Coniglione. (shrink)

According to the Theory of the Second Best, in non-ideal circumstances, approximating ideals might be suboptimal (with respect to a specific interpretation of what “approximating an ideal” means). In this paper, I argue that the formal model underlying the Theory can apply to problems in epistemology. Two applications are discussed: First, in some circumstances, second-best problems arise in Bayesian settings. Second, the division of epistemic labour can be subject to second-best problems. These results matter. They allow us to evaluate the (...) claim, made by many philosophers, that second-best problems have import in epistemology (and the specific conditions under which the Theory finds applications). They also allow us to see that talk of “approximating an ideal” is ambiguous, and to clarify the conditions in which approximating an epistemic ideal might be beneficial. (shrink)

This paper revisits the debate on ideal and nonideal theory by taking seriously Charles Mills’s suggestion that it should be understood as a dispute between idealism and materialism. I argue that by understanding different sides of the debates as relying on idealist or materialist assumptions, respectively, we can better make sense of disagreements where theorists otherwise seem to be talking past each other. In addition, I claim that the materialist objection to ideal theory is best understood as a fetishism critique (...) in the Marxian sense: ideal theory treats products of human problem-solving as if they had independent authority over us. To justify ideal theory, one needs to accord either normative or practical authority to ideal-as-idealized-models, and materialism denies both. Finally, I show that rejecting ideal theory does not entail reformism but is compatible with radical critique. (shrink)

A family of arguments for black hole evaporation relies on conservation laws, defined through symmetries represented by Killing vector fields which exist globally or asymptotically. However, these symmetries often rely on the idealizations of stationarity and asymptotic flatness, respectively. In non-stationary or non-asymptotically-flat spacetimes where realistic black holes evaporate, the requisite Killing fields typically do not exist. Can we ‘de-idealize’ these idealizations, and subsequently the associated arguments for black hole evaporation? Here, I critically examine the strategy of using ‘approximately Killing’ (...) fields to de-idealize black hole spacetimes and approximately extend conservation laws to non-idealized cases. I argue that this approach encounters significant challenges, undermining the use of these idealizations to justify the evaporation of realistic – rather than idealized – black holes, and raising questions about the justified use of such idealizations. (shrink)

Contemporary action theory is generally concerned with giving theories of action ontology. In this paper, we make the novel proposal that the standard view in action theory—the Causal Theory of Action—should be recast as a “model”, akin to the models constructed and investigated by scientists. Such models often consist in fictional, hypothetical, or idealized structures, which are used to represent a target system indirectly via some resemblance relation. We argue that recasting the Causal Theory as a model can not only (...) accomplish the goals of causal theorists, but also give the theory greater flexibility in responding to common objections. (shrink)

This entry surveys the phenomenological concept of flesh (chair), developed primarily by Maurice Merleau-Ponty to overcome the classic divisions of Cartesian ontology - above all, that between subject and object. As an ontological "element," flesh designates the reversible, generative relation between sensing and sensible that underlies perception, intercorporeity, expression, and ideality. Its broader ontological significance rests on the insight that one's body is best understood not as a subjectively lived body opposed to an objective one, but as the dynamic, reversible (...) interdependence of both dimensions in a single being. After clarifying the term's linguistic background and its connections to other key phenomenological concepts (§1), the entry traces how flesh emerges from Merleau-Ponty's earlier work to become a central ontological principle (§2), and surveys interpretations and parallel developments that engage with, move beyond, or contest his account (§3). (shrink)

This paper explores the relation between natural modality and our modelling practices. It will be argued that some modelling practices such as abstraction and idealization should be understood as presupposing empirical claims about objective modal features of the behavior of target systems. To establish the connection between natural modality on the one hand and modelling practices on the other an analysis of natural modality in terms of empirically accessible invariance relations will be provided.

This is a comment on a not so major point in Alexander Bird’s recent excellent monograph Knowing Science (2022). Bird argues in the first few chapters for the thesis that science aims at knowledge (rather than at truth, verisimilitude, understanding, or problem solving). That is a major point. I will not quibble with this thesis, but rather with the kind of arguments he relies on to establish it. I will discuss the question of how best to argue for or against (...) the claim that X is the aim of science. If one sticks with what I take to be the only adequate source for knowledge about the aim of science, Bird’s suggestion that knowledge is the aim might need to be qualified. (shrink)

Modeling cuts across sundry scientific practices, contributing to theorizing, experimentation, prediction, measurement, scientific instrumentation, and science education. Beyond the sciences, modeling plays a crucial role in citizen engagement with science and public policy decision-making. It plays a major role in the efforts to address the huge challenges of the 21st century, including but not limited to climate change, shortage of natural resources, loss of biodiversity, and economic forecasting in increasingly unforeseeable situations. The diversity of scientific models is astounding; side-by-side mathematical (...) and scale models, technological advances such as rapidly expanding big data, computational and synthetic approaches, and generative AI are pushing modeling toward new frontiers, redefining the epistemic agency between humans and scientific instruments. A discussion of what we can achieve through modeling, and how we should manage model-based practices, is critical for ensuring a good and responsible use of this epistemic resource. The chapters of The Routledge Handbook of Philosophy of Scientific Modeling, written by experts in various areas of the philosophy of science, seek to provide enduring philosophical insights and useful analyses for understanding modeling in its multiplicity. -/- . (shrink)

Models are used to explore possibilities across all scientific fields. Climate models simulate the potential future climatic conditions under various emissions scenarios, macroeconomic models investigate the implications of various fiscal and monetary policy initiatives, and infectious diseases models study the spread of viral diseases under a range of conditions. Such modeling approaches have not gone ignored by philosophers of science, but they have only recently started to explicitly address modeling the possible. So far, the discussion has been spread across a (...) variety of more or less isolated pockets of debate in the philosophy of science. Modeling the Possible: Perspectives from Philosophy of Science draws together these studies, focusing specifically on how various modeling practices probe possibilities and justify claims concerning them. -/- The volume is divided into three sections, plus an introductory chapter. The introductory chapter provides a state-of-the-art survey of the discussions of modeling possibilities within the philosophy of science, as well as an introduction to the book’s main themes and individual papers. The three sections focus on different kinds of possibility concepts, possibility spaces, and how-possibly modeling in practical situations. The chapters contained in this volume address conceptual and theoretical issues while also presenting case studies from various scientific domains: physics, evolutionary and synthetic biology, network science, climate science, economics, and epidemiology. -/- Essential reading for philosophers of science, epistemologists, and modelers in various scientific disciplines, Modeling the Possible is also suitable for anyone interested in model-based scientific inferences, their validity, and the policy conclusions derived from them. (shrink)

The use of statistical methods to model gravitational systems is crucial to physics practice, but the extent to which thermodynamics and statistical mechanics genuinely apply to these systems is a contentious issue. This paper provides new conceptual foundations for gravitational thermodynamics by reconsidering the nature of key concepts like equilibrium and advancing a novel way of understanding thermodynamics. The challenges arise from the peculiar characteristics of the gravitational potential, leading to non-extensive energy and entropy, negative heat capacity, and a lack (...) of standard equilibrium. Hence it has been claimed that only non-equilibrium statistical mechanics is warranted in this domain, whereas thermodynamics is inapplicable. We argue instead that equilibrium statistical mechanics applies to self-gravitating systems at the relevant scale, as they display equilibrium in the form of metastable quasi-equilibrium states. We then develop a minimal framework for thermodynamics that can be applied to these systems and beyond. Thermodynamics applies in the sense that we can devise macroscopic descriptions and explanations of the behaviour of these systems in terms of coarse-grained quantities like energy and temperature within equilibrium statistical mechanics. (shrink)

This thesis explores the conceptual foundations of black hole physics through two interconnected aims. First, drawing on philosophical work on scientific modeling, explanation, and idealization, I elucidate the mathematical and methodological bases of black hole physics, thereby clarifying the physical significance of black holes across various theoretical contexts. Second, by examining physicists’ attempts to understand black holes, I critically engage with ongoing debates in the philosophy of science. I argue that black holes are physically robust and indispensable entities, serving as (...) crucial junctions between gravity, quantum physics, and thermodynamics. -/- Chapter 2 identifies three primary but different approaches to modeling classical black holes: exact solutions, topological-causal analyses, and the initial-value formulation. I argue that the distinct mathematical techniques in each of these approaches are mutually integrable and jointly support the physical significance of black holes—in particular, among other things, in substantiating the final state conjecture, which posits that Kerr-Newman family of solutions represent possible end states of gravitational collapse. -/- In Chapter 3, building on these idealized black hole models, I offer a nuanced account of scientific idealization and de-idealization. In light of asymptotic methods, I propose a tripartite framework for de-idealizing de-idealization: intra-model, inter-model, and measurement de-idealizations. These, in turn, reveal how the three main modeling strategies de-idealize black hole models in productive and interrelated ways. -/- Chapter 4 addresses the philosophical notion of universality, prompted by Chandrasekhar’s view of black holes as the simplest and most perfect macroscopic objects in the universe. I identify three complementary forms of black hole universality. I argue that explaining black hole universality requires both (1) the abstract, mathematical operation (idealization) used in topological proofs of uniqueness theorems and (2) the dynamical and causal details revealed in stability theorems. The latter justifies and de-idealizes the former, countering the claim that there are always essential and ineliminable idealizations in universality explanation. -/- The final chapter reinforces the physical status of black holes by analyzing black hole thermodynamics. I focus on the frameworks of quasi-local horizons, particularly isolated and dynamical horizons, arguing that they provide a more solid physical foundation for black hole thermodynamics than traditional arguments based on event horizons. (shrink)

Les conceptions que les étudiants en sciences ont de la nature des modèles scientifiques conduisent à une image inexacte de ceux-ci, notamment lorsque les modèles sont vus comme de simples copies de la réalité. Outre le fait qu’elle en-tretient une conception fausse de la nature de la science, cette façon de se figurer les modèles peut constituer un obstacle pédagogique à l’apprentissage. Objec-tifs : Nous évaluons l’épistémologie de Mario Bunge afin de déterminer si elle peut contribuer à résoudre les problèmes (...) liés à la nature des modèles dans un contexte d’enseignement des sciences. Approche : Après avoir identifié les principales ca-tégories des modèles chez Bunge, nous les employons pour examiner divers as-pects du développement historique des modèles atomiques, puis nous appli-quons le cadre théorique ainsi obtenu aux problèmes liés aux diverses concep-tions des modèles en enseignement des sciences. Participants : En raison de la nature théorique de cette recherche, cette étude ne fait pas appel à des partici-pants autres que les chercheurs des recherches mentionnées. Collecte et analyse des données : Nous avons effectué une analyse comparative constante afin d’identifier les schémas de signification communs au cas historique et au cadre théorique. Résultats : Les caractéristiques attribuées aux modèles par Bunge ont été identifiées dans le cas des modèles atomiques. Ces caractéristiques forment un point de vue cohérent et permettent d’aborder, dans le contexte de l’enseignement des sciences, plusieurs questions liées aux diverses conceptions des modèles. (shrink)

The “Global Artificial Intelligence (GAI): Categorical Model” is not just a book — it is a gateway into the future of Artificial Intelligence. At its core lies a bold and revolutionary idea: that reality can be understood, modelled, and transformed through a system that unites categories, objects, and decisions into a single, living framework. This work unveils how the categorical Modelling System becomes the foundation for autonomous decision-making, capable of reshaping agriculture, logistics, robotics, medicine, and beyond.

In this paper I offer a model-theoretic interpretation of Autonomy Theory as defended by Moreno, Mossio, Montévil, and Bich. I address accusations that Autonomy Theory is excessively liberal, such as those made by Garson (2017), arguing that these misunderstand the role of strategic abstractions and generalizations in theory construction. Conceiving of closure of constraints as a model-building effort that emphasizes generality—in the spirit of Levins (1966)—also clarifies its potential for application in empirical contexts.

The idea of using lattice methods to provide a mathematically well-defined formulation of realistic effective quantum field theories (QFTs) and clarify their physical content has gained traction in the last decades. In this paper, I argue that this strategy faces a two-sided obstacle: realistic lattice QFTs are (i) too different from their effective continuum counterparts even at low energies to serve as their foundational proxies and (ii) far from reproducing all of their empirical and explanatory successes to replace them altogether. (...) I briefly conclude with some lessons for the foundations of QFT. (shrink)

This chapter examines modal modelling in climate science. It considers two related topics. The first is the use of climate models to attribute extreme weather events to climate change. The second is the interpretation and use of collections of climate models. Each topic is the subject of a current debate within climate science and philosophy of science, and each has an important modal component. The debates are similar in that each involves a contrast between probabilistic and non-probabilistic methods. In each (...) case, the chapter examines the kind of modal claims scientists make and how they are justified. As climate change is an important practical problem, each debate has a link to policy decisions, and so the chapter ends with a discussion of how modal modelling, and the possibilities it identifies, can support decision-making. (shrink)

Several philosophers of science have drawn attention to a number of modeling practices where scientific models primarily contribute modal information. Examples now abound, and, recently, there have also been some preliminary attempts to address questions of under what conditions, and by virtue of what, models can perform this modal epistemic function. This paper sets out to constructively review those attempts through a prism of the more general literature on the epistemology of modality. One aim of this exercise is to expose (...) the underlying justificatory strategies of the modal modeling accounts – which are strikingly convergent with those already identified by modal epistemologists. Another aim is to highlight some challenges familiar to modal epistemology and discuss the form they take in the context of modal modeling as well as the resources that modal modeling accounts have for dealing with them. This is all done with an eye to pointing out ways forward in further developing the work on the epistemology of modal modeling. (shrink)

I argue that the methodology of model building motivates the view that the norms of formal epistemology should not be excessively demanding. This is quite a different picture than one often encounters, especially among philosophers who are sceptical of the usefulness of formal work in epistemology. I argue for this view in two ways. First, formal epistemologists are engaged in a particular kind of modelling—namely, normative modelling—which includes a feature that supports demandingness objections. One role of normative models is to (...) deliver normative guidance—indeed, this is a key difference between normative models and descriptive models. Of course, should and can are two different matters, and one might wonder: even if normative models should be action-guiding, can they be? Second, I argue that normative epistemological models can deliver on this promise. Taken together, the lines of argument make the case that normative models should and can deliver normative guidance. In other words, we can have formal epistemology without demandingness. (shrink)

Scientific models often contain assumptions known not to be true. Despite being false representations, models provide us with a key understanding of phenomena. What is more, the falsehoods that figure in models are in many cases central to them, and there is no available alternative to their use. If falsehoods play such an irreplaceable role in our understanding of phenomena, it would seem that truth is not a key concern of scientific modeling. In this paper, I assess the prospects and (...) challenges of reconciling truth and understanding in scientific modeling. More specifically, I review a thesis recently emerging in the literature, what I shall call the Derivation Thesis (DT), according to which we use models to derive true information. First, I examine different versions of the thesis and develop what I take to be its most promising formulation (what I call the generalized DT). Second, I discuss a serious challenge to the generalized DT. I consider a thought experiment in which an unreliable astrological model gives true explanations by fluke. This scenario challenges the idea that models can provide genuine understanding by generating truths. In response, I argue that genuine scientific models also fulfill a specific normative role that epistemically lucky models lack (what I call the normative generalized DT). I test this hypothesis by analysing how the Ideal Gas Law advances scientific understanding of real gases. (shrink)

From Plato to the present day, political theorists have used models of idealistic societies to think about politics. How can these idealistic models inform our thinking about political life in our non-ideal world? Not, as many political theorists have hoped, by providing normative guidance -- by showing us how things should be or where we should go. Even still, we can use these models to interpret the concepts we depend on to explain and evaluate political behavior and institutions, thereby sharpening (...) our explanations of how things work and our estimations of how to make things better. Seeing idealistic models in this light reveals a promising route to a form of political inquiry that integrates our explanatory aims and our normative aspirations. -/- (I have uploaded proofs for the table of contents, chapter 1, and the bibliography. A note on appendix corrections is available on Wiens's PhilPeople page under "Teaching Materials".). (shrink)