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  1. Re-inflating the Conception of Scientific Representation.Chuang Liu - 2015 - International Studies in the Philosophy of Science 29 (1):41-59.
    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 (...)
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  2. Idealization and Structural Explanation in Physics.Martin King - manuscript
    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 (...)
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  3. The Literalist Fallacy & the Free Energy Principle: Model building, Scientific Realism and Instrumentalism.Michael David Kirchhoff, Julian Kiverstein & Ian Robertson - manuscript
    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 (...)
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  4. Idealisations and the no-miracle argument.Quentin Ruyant - manuscript
    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 (...)
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  5. Explanatory idealizations.Andrew Wayne - manuscript
    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.
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  6. Model Anarchism.Walter Veit - 2020
    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.
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  7. Symbols versus Models.Chuang Liu - 2013
    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 (...)
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  8. Fictional Models in Science.Chuang Liu - 2013
    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 (...)
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  9. Idealization and the structure of theories in biololgy.Alfonso Arroyo-Santos & Xavier De Donato-Rodríguez - 2008
    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 (...)
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  10. Justifying Idealization by Abstraction.Sebastian Lutz -
    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 (...)
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  11. Idealization in cognitive psychology: A case study.Colin Klein - manuscript
    develops themes from the dissertation. I argue that two models of prosopagnosia are best understood as idealizing models, and as such are subject to importantly different methodological constraints from non-idealized theories of face recognition.
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  12. Idealisation in Natural Language Semantics: Truth-Conditions for Radical Contextualists.Gabe Dupre - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
    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 (...)
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  13. Different Ways to be a Realist: A Response to Pincock.Angela Potochnik - forthcoming - In Kareem Khalifa, Insa Lawler & Elay Shech (eds.), Scientific Understanding and Representation: Modeling in the Physical Sciences.
    In his chapter in this volume, Christopher Pincock develops an argument for scientific realism based on scientific understanding, and he argues that Giere’s (2006) and my (2017, 2020) commitment to the context-dependence of scientific understanding or knowledge renders our views unable to account for an essential step in how scientists come to know. Meanwhile, in my chapter in this volume, I motivate a view that I call "causal pattern realism." In this response to Pincock's chapter, I will sketch a revised (...)
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  14. Truth and reality: How to be a scientific realist without believing scientific theories should be true.Angela Potochnik - forthcoming - In Kareem Khalifa, Insa Lawler & Elay Shech (eds.), Scientific Understanding and Representation: Modeling in the Physical Sciences.
    Scientific realism is a thesis about the success of science. Most traditionally: science has been so successful at prediction and guiding action because its best theories are true (or approximately true or increasing in their degree of truth). If science is in the business of doing its best to generate true theories, then we should turn to those theories for explanatory knowledge, predictions, and guidance of our actions and decisions. Views that are popular in contemporary philosophy of science about scientific (...)
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  15. The Exploratory Role of Idealizations and Limiting Cases in Models.Elay Shech & Axel Gelfert - forthcoming - Studia Metodologiczne.
    In this article we argue that idealizations and limiting cases in models play an exploratory role in science. Four senses of exploration are presented: exploration of the structure and representational capacities of theory; proof-of-principle demonstrations; potential explanations; and exploring the suitability of target systems. We illustrate our claims through three case studies, including the Aharonov-Bohm effect, the emergence of anyons and fractional quantum statistics, and the Hubbard model of the Mott phase transitions. We end by reflecting on how our case (...)
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  16. Epistemic and Objective Possibility in Science.Ylwa Sjölin Wirling & Till Grüne-Yanoff - forthcoming - British Journal for the Philosophy of Science.
    Scientists regularly make possibility claims. While philosophers of science are well aware of the distinction between epistemic and objective notions of possibility, we believe that they often fail to apply this distinction in their analyses of scientific practices that employ modal concepts. We argue that heeding this distinction will help further progress in current debates in the philosophy of science, as it shows that the debaters talk about different things, rather than disagree on the same issue. We first discuss how (...)
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  17. Do ML models represent their targets?Emily Sullivan - forthcoming - Philosophy of Science.
    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.
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  18. Characterizing and Measuring Racial Discrimination in Public Health Research.Morgan Thompson - forthcoming - Philosophy of Science:1-32.
    Experiences of racial discrimination can seem to be caused by one’s race, a combination of social identities, or non-social features. In other words, racial discrimination can be intersectional or attributionally ambiguous. This poses challenges for current understandings and measurement tools of racial discrimination in public health research, such as the explanation of racial health disparities. Different kinds of discriminatory experiences plausibly produce different psychological effects that mediate their negative health impacts. Thus, multiple characterizations and measurements of racial discrimination are needed. (...)
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  19. Maps and Models.Rasmus Grønfeldt Winther - forthcoming - In Routledge Handbook of Philosophy of Scientific Modeling. London, UK:
    Maps and mapping raise questions about models and modeling and in science. This chapter archives map discourse in the founding generation of philosophers of science (e.g., Rudolf Carnap, Nelson Goodman, Thomas Kuhn, and Stephen Toulmin) and in the subsequent generation (e.g., Philip Kitcher, Helen Longino, and Bas van Fraassen). In focusing on these two original framing generations of philosophy of science, I intend to remove us from the heat of contemporary discussions of abstraction, representation, and practice of science and thereby (...)
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  20. Leveraging Distortions: Explanation, Idealization, and Universality in Science.Holly Andersen - 2023 - Philosophical Review 132 (3):499-503.
    A critical review of Collin Rice's book, Leveraging Distortions: Explanation, Idealization, and Universality in Science.
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  21. Trueing.Holly Andersen - 2023 - In The Pragmatist Challenge: Pragmatist Metaphysics for Philosophy of Science. Oxford University Press.
    Even in areas of philosophy of science that don’t involve formal treatments of truth, one’s background view of truth still centrally shapes views on other issues. I offer an informal way to think about truth as trueing, like trueing a bicycle wheel. This holist approach to truth provides a way to discuss knowledge products like models in terms of how well-trued they are to their target. Trueing emphasizes: the process by which models are brought into true; how the idealizations in (...)
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  22. The comparison problem for approximating epistemic ideals.Marc-Kevin Daoust - 2023 - Ratio 36 (1):22-31.
    Some epistemologists think that the Bayesian ideals matter because we can approximate them. That is, our attitudes can be more or less close to the ones of our ideal Bayesian counterpart. In this paper, I raise a worry for this justification of epistemic ideals. The worry is this: In order to correctly compare agents to their ideal counterparts, we need to imagine idealized agents who have the same relevant information, knowledge, or evidence. However, there are cases in which one’s ideal (...)
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  23. Genesis, Structure, and Ideas: Genetic Epistemology in Early Modern Philosophy.Gregor Kroupa - 2023 - In Jure Simoniti & Gregor Kroupa (eds.), Ideas and Idealism in Philosophy. De Gruyter. pp. 69-92.
    Although the idiom “genesis and structure” is usually associated with the rise of structuralism in the late 1950s and early 1960s, the two notions are arguably among the most persistent methods in the history of modern philosophy. This article outlines the emergence of “genetic epistemology” in the seventeenth century, when the seemingly antithetical character of the conceptual pair was reworked into a productive epistemological theory, especially in Descartes, Hobbes, Spinoza, and Leibniz, who increasingly used diachronic (genetic) narratives to explain the (...)
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  24. A Dilemma for Solomonoff Prediction.Sven Neth - 2023 - Philosophy of Science 90 (2):288-306.
    The framework of Solomonoff prediction assigns prior probability to hypotheses inversely proportional to their Kolmogorov complexity. There are two well-known problems. First, the Solomonoff prior is relative to a choice of Universal Turing machine. Second, the Solomonoff prior is not computable. However, there are responses to both problems. Different Solomonoff priors converge with more and more data. Further, there are computable approximations to the Solomonoff prior. I argue that there is a tension between these two responses. This is because computable (...)
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  25. Idealizations in Physics.Elay Shech - 2023 - Cambridge, UK: Cambridge University Press.
    Idealizations are ubiquitous in physics. They are distortions or falsities that enter into theories, laws, models, and scientific representations. Various questions suggest themselves: What are idealizations? Why do we appeal to idealizations and how do we justify them? Are idealizations essential to physics and, if so, in what sense and for which purpose? How can idealizations provide genuine understanding? If our motivation for believing in the existence of unobservable entities like electrons and quarks is that they are indispensable to our (...)
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  26. Introduction to the Synthese Topical Collection 'Modal Modeling in Science: Modal Epistemology meets Philosophy of Science’.Ylwa Sjölin Wirling & Till Grüne-Yanoff - 2023 - Synthese 201 (6):1-13.
  27. Regulative Idealization: A Kantian Approach to Idealized Models.Lorenzo Spagnesi - 2023 - Studies in History and Philosophy of Science Part A 99 (C):1-9.
    Scientific models typically contain idealizations, or assumptions that are known not to be true. Philosophers have long questioned the nature of idealizations: Are they heuristic tools that will be abandoned? Or rather fictional representations of reality? And how can we reconcile them with realism about knowledge of nature? Immanuel Kant developed an account of scientific investigation that can inspire a new approach to the contemporary debate. Kant argued that scientific investigation is possible only if guided by ideal assumptions—what he calls (...)
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  28. The Modal Basis of Scientific Modelling.Tuomas E. Tahko - 2023 - Synthese 201 (75):1-16.
    The practice of scientific modelling often resorts to hypothetical, false, idealised, targetless, partial, generalised, and other types of modelling that appear to have at least partially non-actual targets. In this paper, I will argue that we can avoid a commitment to non-actual targets by sketching a framework where models are understood as having networks of possibilities as their targets. This raises a further question: what are the truthmakers for the modal claims that we can derive from models? I propose that (...)
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  29. New Developments in the Theory of the Historical Process: Polish Contributions to Non-Marxian Historical Materialism.Krzysztof Brzechczyn (ed.) - 2022 - Leiden/Boston: Brill.
    The first part of this book contains a selection of Leszek Nowak’s (1943-2009) works on non-Marxian historical materialism, which are published here in English for the first time. In these papers, Nowak constructs a dynamic model of religious community, reconstructs historiosophical assumptions of liberalism and considers the methodological status of prognosis of totalitarization of capitalist society. In the second part of the book, new contributions to non-Marxian historical materialism are presented. Their authors analyze mechanisms of the oligarchization of liberal democracy, (...)
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  30. Review of Collin Rice's Leveraging Distortions: Explanation, Idealization, and Universality in Science[REVIEW]William D'Alessandro - 2022 - BJPS Review of Books.
  31. Understanding, Idealization, and Explainable AI.Will Fleisher - 2022 - Episteme 19 (4):534-560.
    Many AI systems that make important decisions are black boxes: how they function is opaque even to their developers. This is due to their high complexity and to the fact that they are trained rather than programmed. Efforts to alleviate the opacity of black box systems are typically discussed in terms of transparency, interpretability, and explainability. However, there is little agreement about what these key concepts mean, which makes it difficult to adjudicate the success or promise of opacity alleviation methods. (...)
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  32. Analogue Quantum Simulation: A New Instrument for Scientific Understanding.Dominik Hangleiter, Jacques Carolan & Karim Thebault - 2022 - Cham: Springer.
    This book presents fresh insights into analogue quantum simulation. It argues that these simulations are a new instrument of science. They require a bespoke philosophical analysis, sensitive to both the similarities to and the differences with conventional scientific practices such as analogical argument, experimentation, and classical simulation. -/- The analysis situates the various forms of analogue quantum simulation on the methodological map of modern science. In doing so, it clarifies the functions that analogue quantum simulation serves in scientific practice. To (...)
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  33. Teleosemantics and the free energy principle.Stephen Francis Mann & Ross Pain - 2022 - Biology and Philosophy 37 (4):1-25.
    The free energy principle is notoriously difficult to understand. In this paper, we relate the principle to a framework that philosophers of biology are familiar with: Ruth Millikan’s teleosemantics. We argue that: systems that minimise free energy are systems with a proper function; and Karl Friston’s notion of implicit modelling can be understood in terms of Millikan’s notion of mapping relations. Our analysis reveals some surprising formal similarities between the two frameworks, and suggests interesting lines of future research. We hope (...)
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  34. Concrete Scale Models, Essential Idealization, and Causal Explanation.Christopher Pincock - 2022 - British Journal for the Philosophy of Science 73 (2):299-323.
    This paper defends three claims about concrete or physical models: these models remain important in science and engineering, they are often essentially idealized, in a sense to be made precise, and despite these essential idealizations, some of these models may be reliably used for the purpose of causal explanation. This discussion of concrete models is pursued using a detailed case study of some recent models of landslide generated impulse waves. Practitioners show a clear awareness of the idealized character of these (...)
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  35. Do molecules have structure in isolation? How models can provide the answer.Vanessa Seifert - 2022 - In Olimpia Lombardi, Juan Camilo Martínez & Sebastian Fortin (eds.), Philosophical Perspectives on Quantum Chemistry. Springer Cham. pp. 125–143.
    I argue that molecules may not have structure in isolation. I support this by investigating how quantum models identify structure for isolated molecules. Specifically, I distinguish between two sets of models: those that identify structure in isolation and those that do not. The former identify structure because they presuppose structural information about the target system via the Born- Oppenheimer approximation. However, it is an idealisation to assume structure in isolation because there is no empirical evidence of this. In fact, whenever (...)
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  36. Mathematical Projection of Nature in M. Heidegger's Phenomenology. His 'Unwritten Dogma' on Thought Experiments.Panos Theodorou - 2022 - In Aristides Baltas & Thodoris Dimitrakos (eds.), Philosophy and Sciences in the 20th Century, Volume II. Athens, Greece: Crete University Press. pp. 215-242.
    In §69.b of BT Heidegger attempts an existential genetic analysis of science, i.e. a phenomenology of the conceptual process of the constitution of the logical view of science (science seen as theory) starting from the Dasein. It attempts to do so by examining the special intentional-existential modification of (human) being-in-the-world, which is called the "mathematical projection of nature"; that is, by examining that special modification of our being, which places us in the state of experience that presents the world to (...)
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  37. Revisiting abstraction and idealization: how not to criticize mechanistic explanation in molecular biology.Martin Zach - 2022 - European Journal for Philosophy of Science 12 (1):1-20.
    Abstraction and idealization are the two notions that are most often discussed in the context of assumptions employed in the process of model building. These notions are also routinely used in philosophical debates such as that on the mechanistic account of explanation. Indeed, an objection to the mechanistic account has recently been formulated precisely on these grounds: mechanists cannot account for the common practice of idealizing difference-making factors in models in molecular biology. In this paper I revisit the debate and (...)
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  38. Abstraction and Idealization: Mutually Exclusive or Not?Mustafa Efe Ateş - 2021 - The Reasoner 15 (3):19-20.
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  39. Two Dimensions of Opacity and the Deep Learning Predicament.Florian J. Boge - 2021 - Minds and Machines 32 (1):43-75.
    Deep neural networks have become increasingly successful in applications from biology to cosmology to social science. Trained DNNs, moreover, correspond to models that ideally allow the prediction of new phenomena. Building in part on the literature on ‘eXplainable AI’, I here argue that these models are instrumental in a sense that makes them non-explanatory, and that their automated generation is opaque in a unique way. This combination implies the possibility of an unprecedented gap between discovery and explanation: When unsupervised models (...)
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  40. No Time for Time from No-Time.Eugene Y. S. Chua & Craig Callender - 2021 - Philosophy of Science 88 (5):1172-1184.
    Programs in quantum gravity often claim that time emerges from fundamentally timeless physics. In the semiclassical time program time arises only after approximations are taken. Here we ask what justifies taking these approximations and show that time seems to sneak in when answering this question. This raises the worry that the approach is either unjustified or circular in deriving time from no–time.
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  41. Causal Inference from Noise.Nevin Climenhaga, Lane DesAutels & Grant Ramsey - 2021 - Noûs 55 (1):152-170.
    "Correlation is not causation" is one of the mantras of the sciences—a cautionary warning especially to fields like epidemiology and pharmacology where the seduction of compelling correlations naturally leads to causal hypotheses. The standard view from the epistemology of causation is that to tell whether one correlated variable is causing the other, one needs to intervene on the system—the best sort of intervention being a trial that is both randomized and controlled. In this paper, we argue that some purely correlational (...)
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  42. g as bridge model.Devin Sanchez Curry - 2021 - Philosophy of Science 88 (5):1067-1078.
    Psychometric g—a statistical factor capturing intercorrelations between scores on different IQ tests—is of theoretical interest despite being a low-fidelity model of both folk psychological intelligence and its cognitive/neural underpinnings. Psychometric g idealizes away from those aspects of cognitive/neural mechanisms that are not explanatory of the relevant variety of folk psychological intelligence, and it idealizes away from those varieties of folk psychological intelligence that are not generated by the relevant cognitive/neural substrate. In this manner, g constitutes a high-fidelity bridge model of (...)
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  43. Adversariality and Ideal Argumentation: A Second-Best Perspective.Marc-Kevin Daoust - 2021 - Topoi 40 (5):887-898.
    What is the relevance of ideals for determining virtuous argumentative practices? According to Bailin and Battersby (2016), the telos of argumentation is to improve our cognitive systems, and adversariality plays no role in ideally virtuous argumentation. Stevens and Cohen (2019) grant that ideal argumentation is collaborative, but stress that imperfect agents like us should not aim at approximating the ideal of argumentation. Accordingly, it can be virtuous, for imperfect arguers like us, to act as adversaries. Many questions are left unanswered (...)
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  44. Scientific understanding and felicitous legitimate falsehoods.Insa Lawler - 2021 - Synthese 198 (7):6859-6887.
    Science is replete with falsehoods that epistemically facilitate understanding by virtue of being the very falsehoods they are. In view of this puzzling fact, some have relaxed the truth requirement on understanding. I offer a factive view of understanding that fully accommodates the puzzling fact in four steps: (i) I argue that the question how these falsehoods are related to the phenomenon to be understood and the question how they figure into the content of understanding it are independent. (ii) I (...)
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  45. Classical Form or Modern Scientific Rationalization? Nietzsche on the Drive to Ordered Thought as Apollonian Power and Socratic Pathology.Eli I. Lichtenstein - 2021 - Journal of Nietzsche Studies 52 (1):105-134.
    Nietzsche sometimes praises the drive to order—to simplify, organize, and draw clear boundaries—as expressive of a vital "classical" style, or an Apollonian artistic drive to calmly contemplate forms displaying "epic definiteness and clarity." But he also sometimes harshly criticizes order, as in the pathological dialectics or "logical schematism" that he associates paradigmatically with Socrates. I challenge a tradition that interprets Socratism as an especially one-sided expression of, or restricted form of attention to, the Apollonian: they are more radically disparate. Beyond (...)
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  46. Is Truth the Gold Standard of Inquiry? A Comment on Elgin’s Argument Against Veritism.Moti Mizrahi - 2021 - Foundations of Science 26 (2):275-280.
    In True enough,, Elgin argues against veritism, which is the view that truth is the paramount epistemic objective. Elgin’s argument against veritism proceeds from considering the role that models, idealizations, and thought experiments play in science to the conclusion that veritism is unacceptable. In this commentary, I argue that Elgin’s argument fails as an argument against veritism. I sketch a refutation by logical analogy of Elgin’s argument. Just as one can aim at gold medals and still find approximations to gold, (...)
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  47. Deflationary realism: Representation and idealisation in cognitive science.Dimitri Coelho Mollo - 2021 - Mind and Language 37 (5):1048-1066.
    Debate on the nature of representation in cognitive systems tends to oscillate between robustly realist views and various anti‐realist options. I defend an alternative view, deflationary realism, which sees cognitive representation as an offshoot of the extended application to cognitive systems of an explanatory model whose primary domain is public representation use. This extended application, justified by a common explanatory target, embodies idealisations, partial mismatches between model and reality. By seeing representation as part of an idealised model, deflationary realism avoids (...)
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  48. Close encounters with scientific analogies of the third kind.Francesco Nappo - 2021 - European Journal for Philosophy of Science 11 (3):1-20.
    Arguments from non-causal analogy form a distinctive class of analogical arguments in science not recognized in authoritative classifications by, e.g., Hesse and Bartha. In this paper, I illustrate this novel class of scientific analogies by means of historical examples from physics, biology and economics, at the same time emphasizing their broader significance for contemporary debates in epistemology.
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  49. Veritism refuted? Understanding, idealization, and the facts.Tamer Nawar - 2021 - Synthese 198 (5):4295-4313.
    Elgin offers an influential and far-reaching challenge to veritism. She takes scientific understanding to be non-factive and maintains that there are epistemically useful falsehoods that figure ineliminably in scientific understanding and whose falsehood is no epistemic defect. Veritism, she argues, cannot account for these facts. This paper argues that while Elgin rightly draws attention to several features of epistemic practices frequently neglected by veritists, veritists have numerous plausible ways of responding to her arguments. In particular, it is not clear that (...)
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  50. Robustness reasoning in climate model comparisons.Ryan O’Loughlin - 2021 - Studies in History and Philosophy of Science Part A 85 (C):34-43.
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