Results for 'model explanation'

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  1. On Structural Accounts of Model-Explanations.Martin King - 2016 - Synthese 193 (9):2761-2778.
    The focus in the literature on scientific explanation has shifted in recent years towards model-based approaches. In recent work, Alisa Bokulich has argued that idealization has a central role to play in explanation. Bokulich claims that certain highly-idealized, structural models can be explanatory, even though they are not considered explanatory by causal, mechanistic, or covering law accounts of explanation. This paper focuses on Bokulich’s account in order to make the more general claim that there are problems (...)
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  2. Models and Explanation.Alisa Bokulich - 2017 - In Lorenzo Magnani & Tommaso Wayne Bertolotti (eds.), Springer Handbook of Model-Based Science. Springer. pp. 103-118.
    Detailed examinations of scientific practice have revealed that the use of idealized models in the sciences is pervasive. These models play a central role in not only the investigation and prediction of phenomena, but in their received scientific explanations as well. This has led philosophers of science to begin revising the traditional philosophical accounts of scientific explanation in order to make sense of this practice. These new model-based accounts of scientific explanation, however, raise a number of key (...)
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  3. Which Models of Scientific Explanation Are (In)Compatible with IBE?Yunus Prasetya - forthcoming - British Journal for the Philosophy of Science.
    In this article, I explore the compatibility of inference to the best explanation (IBE) with several influential models and accounts of scientific explanation. First, I explore the different conceptions of IBE and limit my discussion to two: the heuristic conception and the objective Bayesian conception. Next, I discuss five models of scientific explanation with regard to each model’s compatibility with IBE. I argue that Philip Kitcher’s unificationist account supports IBE; Peter Railton’s deductive-nomological-probabilistic model, Wesley Salmon’s (...)
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  4. Minimal Models and the Generalized Ontic Conception of Scientific Explanation.Mark Povich - 2018 - British Journal for the Philosophy of Science 69 (1):117-137.
    Batterman and Rice ([2014]) argue that minimal models possess explanatory power that cannot be captured by what they call ‘common features’ approaches to explanation. Minimal models are explanatory, according to Batterman and Rice, not in virtue of accurately representing relevant features, but in virtue of answering three questions that provide a ‘story about why large classes of features are irrelevant to the explanandum phenomenon’ ([2014], p. 356). In this article, I argue, first, that a method (the renormalization group) they (...)
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  5. Extending the Argument From Unconceived Alternatives: Observations, Models, Predictions, Explanations, Methods, Instruments, Experiments, and Values.Darrell Patrick Rowbottom - 2016 - Synthese (10).
    Stanford’s argument against scientific realism focuses on theories, just as many earlier arguments from inconceivability have. However, there are possible arguments against scientific realism involving unconceived (or inconceivable) entities of different types: observations, models, predictions, explanations, methods, instruments, experiments, and values. This paper charts such arguments. In combination, they present the strongest challenge yet to scientific realism.
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  6. Mathematical Modelling and Contrastive Explanation.Adam Morton - 1990 - Canadian Journal of Philosophy 20 (Supplement):251-270.
    Mathematical models provide explanations of limited power of specific aspects of phenomena. One way of articulating their limits here, without denying their essential powers, is in terms of contrastive explanation.
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  7. Does IBE Require a ‘Model’ of Explanation?Frank Cabrera - 2020 - British Journal for the Philosophy of Science 71 (2):727-750.
    In this article, I consider an important challenge to the popular theory of scientific inference commonly known as ‘inference to the best explanation’, one that has received scant attention.1 1 The problem is that there exists a wide array of rival models of explanation, thus leaving IBE objectionably indeterminate. First, I briefly introduce IBE. Then, I motivate the problem and offer three potential solutions, the most plausible of which is to adopt a kind of pluralism about the rival (...)
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  8. The Diversity of Models as a Means to Better Explanations in Economics.Emrah Aydinonat - 2018 - Journal of Economic Methodology 25 (3):237-251.
    In Economics Rules, Dani Rodrik (2015) argues that what makes economics powerful despite the limitations of each and every model is its diversity of models. Rodrik suggests that the diversity of models in economics improves its explanatory capacities, but he does not fully explain how. I offer a clearer picture of how models relate to explanations of particular economic facts or events, and suggest that the diversity of models is a means to better economic explanations.
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  9. The Causal Mechanical Model of Explanation.James Woodward - 1989 - Minnesota Studies in the Philosophy of Science 13:359-83.
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  10. Why Attention is Not Explanation: Surgical Intervention and Causal Reasoning About Neural Models.Christopher Grimsley, Elijah Mayfield & Julia Bursten - 2020 - Proceedings of the 12th Conference on Language Resources and Evaluation.
    As the demand for explainable deep learning grows in the evaluation of language technologies, the value of a principled grounding for those explanations grows as well. Here we study the state-of-the-art in explanation for neural models for natural-language processing (NLP) tasks from the viewpoint of philosophy of science. We focus on recent evaluation work that finds brittleness in explanations obtained through attention mechanisms.We harness philosophical accounts of explanation to suggest broader conclusions from these studies. From this analysis, we (...)
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  11.  53
    Agent-Based Models as Etio-Prognostic Explanations.Olaf Dammann - 2021 - Argumenta 7 (1):19-38.
    Agent-based models (ABMs) are one type of simulation model used in the context of the COVID-19 pandemic. In contrast to equation-based models, ABMs are algorithms that use individual agents and attribute changing characteristics to each one, multiple times during multiple iterations over time. This paper focuses on three philosophical aspects of ABMs as models of causal mechanisms, as generators of emergent phenomena, and as providers of explanation. Based on my discussion, I conclude that while ABMs cannot help much (...)
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  12. How-Possibly Explanation in Biology: Lessons From Wilhelm His’s ‘Simple Experiments’ Models.Christopher Pearson - 2018 - Philosophy, Theory, and Practice in Biology 10 (4).
    A common view of how-possibly explanations in biology treats them as explanatorily incomplete. In addition to this interpretation of how-possibly explanation, I argue that there is another interpretation, one which features what I term “explanatory strategies.” This strategy-centered interpretation of how-possibly explanation centers on there being a different explanatory context within which how-possibly explanations are offered. I contend that, in conditions where this strategy context is recognized, how-possibly explanations can be understood as complete explanations. I defend this alternative (...)
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  13. Explanation and Understanding in a Model-Based Model of Cognition.Karlis Podnieks - manuscript
    This article is an experiment. Consider a minimalist model of cognition (models, means of model-building and history of their evolution). In this model, explanation could be defined as a means allowing to advance: production of models and means of model-building (thus, yielding 1st class understanding), exploration and use of them (2nd class), and/or teaching (3rd class). At minimum, 3rd class understanding is necessary for an explanation to be respected.
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  14.  85
    Models and Scientific Explanations.Robert C. Richardson - 1986 - Philosophica 37:59-72.
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  15.  15
    Explanation, Enaction and Naturalised Phenomenology.Marilyn Stendera - 2022 - Phenomenology and the Cognitive Sciences:1-21.
    This paper explores the implications of conceptualising phenomenology as explanatory for the ongoing dialogue between the phenomenological tradition and cognitive science, especially enactive approaches to cognition. The first half of the paper offers three interlinked arguments: Firstly, that differentiating between phenomenology and the natural sciences by designating one as descriptive and the other as explanatory undermines opportunities for the kind of productive friction that is required for genuine ‘mutual enlightenment’. Secondly, that conceiving of phenomenology as descriptive rather than explanatory risks (...)
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  16.  3
    Unrealistic Models in Mathematics.William D'Alessandro - forthcoming - Philosophers’ Imprint.
    Models are indispensable tools of scientific inquiry, and one of their main uses is to improve our understanding of the phenomena they represent. How do models accomplish this? And what does this tell us about the nature of understanding? While much recent work has aimed at answering these questions, philosophers' focus has been squarely on models in empirical science. I aim to show that pure mathematics also deserves a seat at the table. I begin by presenting two cases: Cramér’s random (...)
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  17. The Case for the Comparator Model as an Explanation of the Sense of Agency and its Breakdowns.Glenn Carruthers - 2012 - Consciousness and Cognition 21 (1):30-45.
    I compare Frith and colleagues’ influential comparator account of how the sense of agency is elicited to the multifactorial weighting model advocated by Synofzik and colleagues. I defend the comparator model from the common objection that the actual sensory consequences of action are not needed to elicit the sense of agency. I examine the comparator model’s ability to explain the performance of healthy subjects and those suffering from delusions of alien control on various self-attribution tasks. It transpires (...)
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  18. Mathematical Explanation by Law.Sam Baron - 2019 - British Journal for the Philosophy of Science 70 (3):683-717.
    Call an explanation in which a non-mathematical fact is explained—in part or in whole—by mathematical facts: an extra-mathematical explanation. Such explanations have attracted a great deal of interest recently in arguments over mathematical realism. In this article, a theory of extra-mathematical explanation is developed. The theory is modelled on a deductive-nomological theory of scientific explanation. A basic DN account of extra-mathematical explanation is proposed and then redeveloped in the light of two difficulties that the basic (...)
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  19. A Puzzle About Economic Explanation: Examining the Cournot and Bertrand Models of Duopoly Competition.Jonathan Nebel - 2017 - Dissertation, Kansas State University
    Economists use various models to explain why it is that firms are capable of pricing above marginal cost. In this paper, we will examine two of them: the Cournot and Bertrand duopoly models. Economists generally accept both models as good explanations of the phenomenon, but the two models contradict each other in various important ways. The puzzle is that two inconsistent explanations are both regarded as good explanations for the same phenomenon. This becomes especially worrisome when the two models are (...)
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  20. Diagrams as Locality Aids for Explanation and Model Construction in Cell Biology.Nicholaos Jones & Olaf Wolkenhauer - 2012 - Biology and Philosophy 27 (5):705-721.
    Using as case studies two early diagrams that represent mechanisms of the cell division cycle, we aim to extend prior philosophical analyses of the roles of diagrams in scientific reasoning, and specifically their role in biological reasoning. The diagrams we discuss are, in practice, integral and indispensible elements of reasoning from experimental data about the cell division cycle to mathematical models of the cycle’s molecular mechanisms. In accordance with prior analyses, the diagrams provide functional explanations of the cell cycle and (...)
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  21. Explaining Explanations in AI.Brent Mittelstadt - forthcoming - FAT* 2019 Proceedings 1.
    Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus (...)
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  22. Metaphysical Explanation: The Kitcher Picture.Sam Baron & James Norton - 2021 - Erkenntnis 86 (1):187-207.
    This paper offers a new account of metaphysical explanation. The account is modelled on Kitcher’s unificationist approach to scientific explanation. We begin, in Sect. 2, by briefly introducing the notion of metaphysical explanation and outlining the target of analysis. After that, we introduce a unificationist account of metaphysical explanation before arguing that such an account is capable of capturing four core features of metaphysical explanations: irreflexivity, non-monotonicity, asymmetry and relevance. Since the unificationist theory of metaphysical (...) inherits irreflexivity and non-monotonicity directly from the unificationist theory of scientific explanation that underwrites it, we focus on demonstrating how the account can secure asymmetry and relevance. (shrink)
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  23. Viewing-as Explanations and Ontic Dependence.William D’Alessandro - 2020 - Philosophical Studies 177 (3):769-792.
    According to a widespread view in metaphysics and philosophy of science, all explanations involve relations of ontic dependence between the items appearing in the explanandum and the items appearing in the explanans. I argue that a family of mathematical cases, which I call “viewing-as explanations”, are incompatible with the Dependence Thesis. These cases, I claim, feature genuine explanations that aren’t supported by ontic dependence relations. Hence the thesis isn’t true in general. The first part of the paper defends this claim (...)
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  24.  67
    Topological Explanations: An Opinionated Appraisal.Daniel Kostić - forthcoming - In I. Lawler, E. Shech & K. Khalifa (eds.), Scientific Understanding and Representation: Modeling in the Physical Sciences.
    This chapter provides a systematic overview of topological explanations in the philosophy of science literature. It does so by presenting an account of topological explanation that I (Kostić and Khalifa 2021; Kostić 2020a; 2020b; 2018) have developed in other publications and then comparing this account to other accounts of topological explanation. Finally, this appraisal is opinionated because it highlights some problems in alternative accounts of topological explanations, and also it outlines responses to some of the main criticisms raised (...)
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  25. Beyond Explanation: Understanding as Dependency Modeling.Finnur Dellsén - 2018 - British Journal for the Philosophy of Science (4):1261-1286.
    This paper presents and argues for an account of objectual understanding that aims to do justice to the full range of cases of scientific understanding, including cases in which one does not have an explanation of the understood phenomenon. According to the proposed account, one understands a phenomenon just in case one grasps a sufficiently accurate and comprehensive model of the ways in which it or its features are situated within a network of dependence relations; one’s degree of (...)
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  26. Narrative Explanation.J. David Velleman - 2003 - Philosophical Review 112 (1):1-25.
    A story does more than recount events; it recounts events in a way that renders them intelligible, thus conveying not just information but also understanding. We might therefore be tempted to describe narrative as a genre of explanation. When the police invite a suspect to “tell his story,” they are asking him to explain the blood on his shirt or his absence from home on the night of the murder; and whether he is judged to have a “good story” (...)
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  27.  41
    The Medical Model, with a Human Face.Justis Koon - forthcoming - Philosophical Studies:1-24.
    In this paper, I defend a version of the medical model of disability, which defines disability as an enduring biological dysfunction that causes its bearer a significant degree of impairment. We should accept the medical model, I argue, because it succeeds in capturing our judgments about what conditions do and do not qualify as disabilities, because it offers a compelling explanation for what makes a condition count as a disability, and because it justifies why the federal government (...)
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  28. Equilibrium Explanation as Structural Non-Mechanistic Explanation: The Case Long-Term Bacterial Persistence in Human Hosts.Javier Suárez & Roger Deulofeu - 2019 - Teorema: International Journal of Philosophy 3 (38):95-120.
    Philippe Huneman has recently questioned the widespread application of mechanistic models of scientific explanation based on the existence of structural explanations, i.e. explanations that account for the phenomenon to be explained in virtue of the mathematical properties of the system where the phenomenon obtains, rather than in terms of the mechanisms that causally produce the phenomenon. Structural explanations are very diverse, including cases like explanations in terms of bowtie structures, in terms of the topological properties of the system, or (...)
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  29. Mechanistic Explanation in Psychology.Mark Povich - forthcoming - In Hank Stam & Huib Looren De Jong (eds.), The SAGE Handbook of Theoretical Psychology. (Eds.) Hank Stam and Huib Looren de Jong. Sage.
    Philosophers of psychology debate, among other things, which psychological models, if any, are (or provide) mechanistic explanations. This should seem a little strange given that there is rough consensus on the following two claims: 1) a mechanism is an organized collection of entities and activities that produces, underlies, or maintains a phenomenon, and 2) a mechanistic explanation describes, represents, or provides information about the mechanism producing, underlying, or maintaining the phenomenon to be explained (i.e. the explanandum phenomenon) (Bechtel and (...)
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  30.  48
    Towards Knowledge-Driven Distillation and Explanation of Black-Box Models.Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello - 2021 - In Proceedings of the Workshop on Data meets Applied Ontologies in Explainable {AI} {(DAO-XAI} 2021) part of Bratislava Knowledge September {(BAKS} 2021), Bratislava, Slovakia, September 18th to 19th, 2021. CEUR 2998.
    We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an ap- proach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our aim is, firstly, to target (...)
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  31. Scientific Explanation and Moral Explanation.Uri D. Leibowitz - 2011 - Noûs 45 (3):472-503.
    Moral philosophers are, among other things, in the business of constructing moral theories. And moral theories are, among other things, supposed to explain moral phenomena. Consequently, one’s views about the nature of moral explanation will influence the kinds of moral theories one is willing to countenance. Many moral philosophers are (explicitly or implicitly) committed to a deductive model of explanation. As I see it, this commitment lies at the heart of the current debate between moral particularists and (...)
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  32. Improved Model Exploration for the Relationship Between Moral Foundations and Moral Judgment Development Using Bayesian Model Averaging.Hyemin Han & Kelsie J. Dawson - 2022 - Journal of Moral Education 51 (2):204-218.
    Although some previous studies have investigated the relationship between moral foundations and moral judgment development, the methods used have not been able to fully explore the relationship. In the present study, we used Bayesian Model Averaging (BMA) in order to address the limitations in traditional regression methods that have been used previously. Results showed consistency with previous findings that binding foundations are negatively correlated with post-conventional moral reasoning and positively correlated with maintaining norms and personal interest schemas. In addition (...)
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  33. Grounding-Mechanical Explanation.Kelly Trogdon - 2018 - Philosophical Studies 175 (6):1289-1309.
    Characterization of a form of explanation involving grounding on the model of mechanistic causal explanation.
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  34. Causal Models and the Logic of Counterfactuals.Jonathan Vandenburgh - manuscript
    Causal models provide a framework for making counterfactual predictions, making them useful for evaluating the truth conditions of counterfactual sentences. However, current causal models for counterfactual semantics face limitations compared to the alternative similarity-based approach: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper argues that these limitations arise from the theory of interventions where intervening on variables requires changing structural equations rather than the values of variables. Using an (...)
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  35. Explanations and candidate explanations in physics.Martin King - 2020 - European Journal for Philosophy of Science 10 (1):1-17.
    There has been a growing trend to include non-causal models in accounts of scientific explanation. A worry addressed in this paper is that without a higher threshold for explanation there are no tools for distinguishing between models that provide genuine explanations and those that provide merely potential explanations. To remedy this, a condition is introduced that extends a veridicality requirement to models that are empirically underdetermined, highly-idealised, or otherwise non-causal. This condition is applied to models of electroweak symmetry (...)
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  36. The Ontic Account of Scientific Explanation.Carl F. Craver - 2014 - In Marie I. Kaiser, Oliver R. Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the Special Sciences: The Case of Biology and History. Springer Verlag. pp. 27-52.
    According to one large family of views, scientific explanations explain a phenomenon (such as an event or a regularity) by subsuming it under a general representation, model, prototype, or schema (see Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441; Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge: MIT Press; Darden (2006); Hempel, C. G. (1965). Aspects (...)
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  37. Functional Analyses, Mechanistic Explanations, and Explanatory Tradeoffs.Sergio Daniel Barberis - 2013 - Journal of Cognitive Science 14:229-251.
    Recently, Piccinini and Craver have stated three theses concerning the relations between functional analysis and mechanistic explanation in cognitive sciences: No Distinctness: functional analysis and mechanistic explanation are explanations of the same kind; Integration: functional analysis is a kind of mechanistic explanation; and Subordination: functional analyses are unsatisfactory sketches of mechanisms. In this paper, I argue, first, that functional analysis and mechanistic explanations are sub-kinds of explanation by scientific (idealized) models. From that point of view, we (...)
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  38. New Mechanistic Explanation and the Need for Explanatory Constraints.L. R. Franklin-Hall - 2016 - In Ken Aizawa & Carl Gillett (eds.), Scientific Composition and Metaphysical Ground. Palgrave. pp. 41-74.
    This paper critiques the new mechanistic explanatory program on grounds that, even when applied to the kinds of examples that it was originally designed to treat, it does not distinguish correct explanations from those that blunder. First, I offer a systematization of the explanatory account, one according to which explanations are mechanistic models that satisfy three desiderata: they must 1) represent causal relations, 2) describe the proper parts, and 3) depict the system at the right ‘level.’ Second, I argue that (...)
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  39.  52
    Explanation and Evaluation in Foucault's Genealogy of Morality.Eli B. Lichtenstein - forthcoming - European Journal of Philosophy.
    Philosophers have cataloged a range of genealogical methods by which different sorts of normative conclusions can be established. Although such methods provide diverging ways of pursuing genealogical inquiry, they typically converge in eschewing historiographic methodology, in favor of a uniquely philosophical approach. In contrast, one genealogist who drew on historiographic methodology is Michel Foucault. This article presents the motivations and advantages of Foucault's genealogical use of such a methodology. It advances two mains claims. First, that Foucault's early 1970s work employs (...)
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  40. Searching for Noncausal Explanations in a Sea of Causes.Alisa Bokulich - 2018 - In Alexander Reutlinger & Juha Saatsi (eds.), Explanation Beyond Causation: Philosophical Perspectives on Non-Causal Explanations. Oxford University Press.
    In the spirit of explanatory pluralism, this chapter argues that causal and noncausal explanations of a phenomenon are compatible, each being useful for bringing out different sorts of insights. After reviewing a model-based account of scientific explanation, which can accommodate causal and noncausal explanations alike, an important core conception of noncausal explanation is identified. This noncausal form of model-based explanation is illustrated using the example of how Earth scientists in a subfield known as aeolian geomorphology (...)
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  41. Understanding From Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? (...)
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  42. From Paradigm-Based Explanation to Pragmatic Genealogy.Matthieu Queloz - 2020 - Mind 129 (515):683-714.
    Why would philosophers interested in the points or functions of our conceptual practices bother with genealogical explanations if they can focus directly on paradigmatic examples of the practices we now have?? To answer this question, I compare the method of pragmatic genealogy advocated by Edward Craig, Bernard Williams, and Miranda Fricker—a method whose singular combination of fictionalising and historicising has met with suspicion—with the simpler method of paradigm-based explanation. Fricker herself has recently moved towards paradigm-based explanation, arguing that (...)
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  43.  53
    ANNs and Unifying Explanations: Reply to Erasmus, Brunet, and Fisher.Yunus Prasetya - 2022 - Philosophy and Technology 35 (2):1-9.
    In a recent article, Erasmus, Brunet, and Fisher (2021) argue that Artificial Neural Networks (ANNs) are explainable. They survey four influential accounts of explanation: the Deductive-Nomological model, the Inductive-Statistical model, the Causal-Mechanical model, and the New-Mechanist model. They argue that, on each of these accounts, the features that make something an explanation is invariant with regard to the complexity of the explanans and the explanandum. Therefore, they conclude, the complexity of ANNs (and other Machine (...)
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  44. Explanation and Understanding Revisited.Panu Raatikainen - 2017 - In Human Condition. Philosophical Essays in Honour of the Centennial Anniversary of Georg Henrik von Wright. Helsinki: , The Philosophical Society of Finland. pp. 339-353.
    "Explanation and Understanding" (1971) by Georg Henrik von Wright is a modern classic in analytic hermeneutics, and in the philosophy of the social sciences and humanities in general. In this work, von Wright argues against naturalism, or methodological monism, i.e. the idea that both the natural sciences and the social sciences follow broadly the same general scientific approach and aim to achieve causal explanations. Against this view, von Wright contends that the social sciences are qualitatively different from the natural (...)
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  45. Complements, Not Competitors: Causal and Mathematical Explanations.Holly Andersen - 2017 - British Journal for the Philosophy of Science 69 (2):485-508.
    A finer-grained delineation of a given explanandum reveals a nexus of closely related causal and non- causal explanations, complementing one another in ways that yield further explanatory traction on the phenomenon in question. By taking a narrower construal of what counts as a causal explanation, a new class of distinctively mathematical explanations pops into focus; Lange’s characterization of distinctively mathematical explanations can be extended to cover these. This new class of distinctively mathematical explanations is illustrated with the Lotka-Volterra equations. (...)
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  46. Function-Theoretic Explanation and the Search for Neural Mechanisms.Frances Egan - 2017 - In Explanation and Integration in Mind and Brain Science 145-163. Oxford, UK: pp. 145-163.
    A common kind of explanation in cognitive neuroscience might be called functiontheoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes (in the system’s normal environment) the exercise of the cognitive capacity. Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that (...)
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  47. A Universe of Explanations.Ghislain Guigon - 2015 - In Karen Bennett & Dean W. Zimmerman (eds.), Oxford Studies in Metaphysics. Vol. 9. Oxford University Press. pp. 345-375.
    This article defends the principle of sufficient reason (PSR) from a simple and direct valid argument according to which PSR implies that there is a truth that explains every truth, namely an omni-explainer. Many proponents of PSR may be willing to bite the bullet and maintain that, if PSR is true, then there is an omni-explainer. I object to this strategy by defending the principle that explanation is irreflexive. Then I argue that proponents of PSR can resist the conclusion (...)
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  48. Mechanisms and Model-Based Functional Magnetic Resonance Imaging.Mark Povich - 2015 - Philosophy of Science 82 (5):1035-1046.
    Mechanistic explanations satisfy widely held norms of explanation: the ability to manipulate and answer counterfactual questions about the explanandum phenomenon. A currently debated issue is whether any nonmechanistic explanations can satisfy these explanatory norms. Weiskopf argues that the models of object recognition and categorization, JIM, SUSTAIN, and ALCOVE, are not mechanistic yet satisfy these norms of explanation. In this article I argue that these models are mechanism sketches. My argument applies recent research using model-based functional magnetic resonance (...)
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  49.  81
    When Are Purely Predictive Models Best?Robert Northcott - 2017 - Disputatio 9 (47):631-656.
    Can purely predictive models be useful in investigating causal systems? I argue ‘yes’. Moreover, in many cases not only are they useful, they are essential. The alternative is to stick to models or mechanisms drawn from well-understood theory. But a necessary condition for explanation is empirical success, and in many cases in social and field sciences such success can only be achieved by purely predictive models, not by ones drawn from theory. Alas, the attempt to use theory to achieve (...)
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  50. Aleatory Explanations Expanded.Paul Humphreys - 1982 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1982:208 - 223.
    Existing definitions of relevance relations are essentially ambiguous outside the binary case. Hence definitions of probabilistic causality based on relevance relations, as well as probability values based on maximal specificity conditions and homogeneous reference classes are also not uniquely specified. A 'neutral state' account of explanations is provided to avoid the problem, based on an earlier account of aleatory explanations by the author. Further reasons in support of this model are given, focusing on the dynamics of explanation. It (...)
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