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  1. Algorithmic Fairness From a Non-Ideal Perspective.Sina Fazelpour & Zachary C. Lipton - manuscript
    Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a (...)
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  2. Conditionals All the Way Down.Matheus Silva - manuscript
    It is usually accepted that unconditional statements are clearer and less problematic than conditional ones. This article goes against this popular belief by advancing the contrarian hypothesis that all unconditional statements can be reduced to conditional ones due to the way our assumptions support our assertions. In fact, considering the coherentist process by which most of our different beliefs mutually support themselves, the only genuine example of unconditional statements are cases of self-justified beliefs, but these examples are controversial and few (...)
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  3. 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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  4. Idealization and Structural Explanation in Physics.Martin King - 2014
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  5. 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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  6. Unifying the Essential Concepts of Biological Networks: Biological Insights and Philosophical Foundations.Daniel Kostic, Claus Hilgetag & Marc Tittgemeyer - forthcoming - Philosophical Transactions of the Royal Society B: Biological Sciences.
    Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation (...)
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  7. Is Truth the Gold Standard of Inquiry? A Comment on Elgin’s Argument Against Veritism.Moti Mizrahi - forthcoming - Foundations of Science:1-6.
    In True Enough, Catherine Elgin (2017) 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 (...)
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  8. Veritism Refuted? Understanding, Idealizations, and the Facts.Tamer Nawar - forthcoming - Synthese:1-19.
    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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  9. Mechanistic Idealization in Systems Biology.Dingmar van Eck & Cory Wright - forthcoming - Synthese.
    This paper adds to the philosophical literature on mechanistic explanation by elaborating two related explanatory functions of idealisation in mechanistic models. The first function involves explaining the presence of structural/organizational features of mechanisms by reference to their role as difference-makers for performance requirements. The second involves tracking counterfactual dependency relations between features of mechanisms and features of mechanistic explanandum phenomena. To make these functions salient, we relate our discussion to an exemplar from systems biological research on the mechanism for countering (...)
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  10. Multiple-Models Juxtaposition and Trade-Offs Among Modeling Desiderata.Yoshinari Yoshida - forthcoming - Philosophy of Science.
    This paper offers a characterization of what I call multiple-models juxtaposition (MMJ), a strategy for managing trade-offs among modeling desiderata. MMJ displays models of distinct phenomena together and fulfills different desiderata both in the individual models and by a comparison of those models. I discuss a concrete case from developmental biology, where MMJ coordinates generality and detail. I also clarify the distinction between MMJ and multiple-models idealization (MMI), which also uses multiple models to manage trade-offs among desiderata. MMJ and MMI (...)
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  11. Democracy of Incomplete Victories: State, Civil Society, and the Scientific Method.Juozas Kasputis - 2020 - In Fourth European Blue Sky Conference: Faultlines and frontlines of European transformation. Koszeg (Hungary): pp. 47-60.
    Fukuyama's 'The End of History' has referred to Kojeve's 'homogenous state' as some sort of conceptual container for the evolving idea of liberal democracy. This paper critically re-assess the homogeneity of state as final stage of liberal idea and defends civil society in terms of democratic governance. It also invites to discuss the role of scholars as public intellectuals and repels the ideological abuse of the scientific method.
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  12. Perspectives, Questions, and Epistemic Value.Kareem Khalifa & Jared A. Millson - 2020 - In Michela Massimi & Ana-Maria Cretu (eds.), Knowledge From a Human Point of View. Cham: Springer Verlag. pp. 87-106.
    Many epistemologists endorse true-belief monism, the thesis that only true beliefs are of fundamental epistemic value. However, this view faces formidable counterexamples. In response to these challenges, we alter the letter, but not the spirit, of true-belief monism. We dub the resulting view “inquisitive truth monism”, which holds that only true answers to relevant questions are of fundamental epistemic value. Which questions are relevant is a function of an inquirer’s perspective, which is characterized by his/her interests, social role, and background (...)
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  13. Countable Additivity, Idealization, and Conceptual Realism.Yang Liu - 2020 - Economics and Philosophy 36 (1):127-147.
    This paper addresses the issue of finite versus countable additivity in Bayesian probability and decision theory -- in particular, Savage's theory of subjective expected utility and personal probability. I show that Savage's reason for not requiring countable additivity in his theory is inconclusive. The assessment leads to an analysis of various highly idealised assumptions commonly adopted in Bayesian theory, where I argue that a healthy dose of, what I call, conceptual realism is often helpful in understanding the interpretational value of (...)
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  14. Idealization and Many Aims.Angela Potochnik - 2020 - Philosophy of Science 87 (5):933-943.
    In this article, I first outline the view developed in my recent book on the role of idealization in scientific understanding. I discuss how this view leads to the recognition of a number of kinds...
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  15. 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 it is (...)
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  16. As If: Idealization and Ideals, by Kwame Anthony Appiah.Adam Toon - 2020 - Mind 129 (513):275-283.
    As If: Idealization and Ideals, by AppiahKwame Anthony. Cambridge, MA: Harvard University Press, 2017. Pp. xvi + 218.
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  17. The Epistemic Virtue of Robustness in Climate Modeling (MA Dissertation).Parjanya Joshi - 2019 - Dissertation, Tata Institute of Social Sciences
    The aim of this dissertation is to comprehensively study various robustness arguments proposed in the literature from Levins to Lloyd as well as the opposition offered to them and pose enquiry into the degree of epistemic virtue that they provide to the model prediction results with respect to climate science and modeling. Another critical issue that this dissertation strives to examine is that of the actual epistemic notion that is operational when scientists and philosophers appeal to robustness. In attempting to (...)
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  18. Two Tales of Epistemic Models.Yang Liu - 2019 - Thought: A Journal of Philosophy 8 (4):291-302.
    This short paper has two parts. First, we prove a generalisation of Aumann's surprising impossibility result in the context of rational decision making. We then move, in the second part, to discuss the interpretational meaning of some formal setups of epistemic models, and we do so by means of presenting an interesting puzzle in epistemic logic. The aim is to highlight certain problematic aspects of these epistemic systems concerning first/third-person asymmetry which underlies both parts of the story. This asymmetry, we (...)
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  19. Genealogy and Knowledge-First Epistemology: A Mismatch?Matthieu Queloz - 2019 - Philosophical Quarterly 69 (274):100-120.
    This paper examines three reasons to think that Craig's genealogy of the concept of knowledge is incompatible with knowledge-first epistemology and finds that far from being incompatible with it, the genealogy lends succour to it. This reconciliation turns on two ideas. First, the genealogy is not history, but a dynamic model of needs. Secondly, by recognizing the continuity of Craig's genealogy with Williams's genealogy of truthfulness, we can see that while both genealogies start out from specific needs explaining what drives (...)
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  20. Knowledge of Abstract Objects in Physics and Mathematics.Michael Shaffer - 2017 - Acta Analytica 32 (4):397-409.
    In this paper a parallel is drawn between the problem of epistemic access to abstract objects in mathematics and the problem of epistemic access to idealized systems in the physical sciences. On this basis it is argued that some recent and more traditional approaches to solving these problems are problematic.
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  21. 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 with maintaining that a (...)
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  22. Explanatory Completeness and Idealization in Large Brain Simulations: A Mechanistic Perspective.Marcin Miłkowski - 2016 - Synthese 193 (5):1457-1478.
    The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: Blue Brain, used for particular simulations of the cortical column in hybrid models, and Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational explanation, I (...)
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  23. Imagination in Scientific Modeling.Adam Toon - 2016 - In Amy Kind (ed.), The Routledge Handbook of Philosophy of Imagination. Routledge. pp. 451-462.
    Modeling is central to scientific inquiry. It also depends heavily upon the imagination. In modeling, scientists seem to turn their attention away from the complexity of the real world to imagine a realm of perfect spheres, frictionless planes and perfect rational agents. Modeling poses many questions. What are models? How do they relate to the real world? Recently, a number of philosophers have addressed these questions by focusing on the role of the imagination in modeling. Some have also drawn parallels (...)
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  24. The Diverse Aims of Science.Angela Potochnik - 2015 - Studies in History and Philosophy of Science Part A 53:71-80.
    There is increasing attention to the centrality of idealization in science. One common view is that models and other idealized representations are important to science, but that they fall short in one or more ways. On this view, there must be an intermediary step between idealized representation and the traditional aims of science, including truth, explanation, and prediction. Here I develop an alternative interpretation of the relationship between idealized representation and the aims of science. In my view, continuing, widespread idealization (...)
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  25. Modeling Measurement: Error and Uncertainty.Alessandro Giordani & Luca Mari - 2014 - In Marcel Boumans, Giora Hon & Arthur Petersen (eds.), Error and Uncertainty in Scientific Practice. Pickering & Chatto. pp. 79-96.
    In the last few decades the role played by models and modeling activities has become a central topic in the scientific enterprise. In particular, it has been highlighted both that the development of models constitutes a crucial step for understanding the world and that the developed models operate as mediators between theories and the world. Such perspective is exploited here to cope with the issue as to whether error-based and uncertainty-based modeling of measurement are incompatible, and thus alternative with one (...)
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  26. Don’T Blame the Idealizations.Nicholaos Jones - 2013 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 44 (1):85-100.
    Idealizing conditions are scapegoats for scientific hypotheses, too often blamed for falsehood better attributed to less obvious sources. But while the tendency to blame idealizations is common among both philosophers of science and scientists themselves, the blame is misplaced. Attention to the nature of idealizing conditions, the content of idealized hypotheses, and scientists’ attitudes toward those hypotheses shows that idealizing conditions are blameless when hypotheses misrepresent. These conditions help to determine the content of idealized hypotheses, and they do so in (...)
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  27. Pluralism in Evolutionary Controversies: Styles and Averaging Strategies in Hierarchical Selection Theories.Rasmus Grønfeldt Winther, Michael J. Wade & Christopher C. Dimond - 2013 - Biology and Philosophy 28 (6):957-979.
    Two controversies exist regarding the appropriate characterization of hierarchical and adaptive evolution in natural populations. In biology, there is the Wright-Fisher controversy over the relative roles of random genetic drift, natural selection, population structure, and interdemic selection in adaptive evolution begun by Sewall Wright and Ronald Aylmer Fisher. There is also the Units of Selection debate, spanning both the biological and the philosophical literature and including the impassioned group-selection debate. Why do these two discourses exist separately, and interact relatively little? (...)
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  28. The Rationality Principle Idealized.Boaz Miller - 2012 - Social Epistemology 26 (1):3-30.
    According to Popper's rationality principle, agents act in the most adequate way according to the objective situation. I propose a new interpretation of the rationality principle as consisting of an idealization and two abstractions. Based on this new interpretation, I critically discuss the privileged status that Popper ascribes to it as an integral part of all social scientific models. I argue that as an idealization, the rationality principle may play an important role in the social sciences, but it also has (...)
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  29. Explanation, Idealisation and the Goldilocks Problem.Brian Weatherson - 2012 - Philosophy and Phenomenological Research 84 (2):461-473.
    Michael Strevens’s book Depth is a great achievement.1 To say anything interesting, useful and true about explanation requires taking on fundamental issues in the metaphysics and epistemology of science. So this book not only tells us a lot about scientific explanation, it has a lot to say about causation, lawhood, probability and the relation between the physical and the special sciences. It should be read by anyone interested in any of those questions, which includes presumably the vast majority of readers (...)
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  30. Understanding Scientific Study Via Process Modeling.Robert W. P. Luk - 2010 - Foundations of Science 15 (1):49-78.
    This paper argues that scientific studies distinguish themselves from other studies by a combination of their processes, their (knowledge) elements and the roles of these elements. This is supported by constructing a process model. An illustrative example based on Newtonian mechanics shows how scientific knowledge is structured according to the process model. To distinguish scientific studies from research and scientific research, two additional process models are built for such processes. We apply these process models: (1) to argue that scientific progress (...)
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  31. The Ontology of Theoretical Modelling: Models as Make-Believe.Adam Toon - 2010 - Synthese 172 (2):301-315.
    The descriptions and theoretical laws scientists write down when they model a system are often false of any real system. And yet we commonly talk as if there were objects that satisfy the scientists’ assumptions and as if we may learn about their properties. Many attempt to make sense of this by taking the scientists’ descriptions and theoretical laws to define abstract or fictional entities. In this paper, I propose an alternative account of theoretical modelling that draws upon Kendall Walton’s (...)
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  32. Models as Make-Believe.Adam Toon - 2010 - In Roman Frigg & Matthew Hunter (eds.), Beyond Mimesis and Convention: Representation in Art and Science. Boston Studies in Philosophy of Science.
    In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, I demonstrate (...)
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  33. Novel Approaches to Models: Mauricio Suárez : Fictions in Science: Philosophical Essays on Modeling and Idealization, Routledge, New York, 2009, Vii + 282 Pp, US$118 HB. [REVIEW]Adam Toon - 2010 - Metascience 19 (2):285-288.
    This paper is a review of Suarez, M. (ed.) Fictions in Science (Routledge).
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  34. Schaffner’s Model of Theory Reduction: Critique and Reconstruction.Rasmus Grønfeldt Winther - 2009 - Philosophy of Science 76 (2):119-142.
    Schaffner’s model of theory reduction has played an important role in philosophy of science and philosophy of biology. Here, the model is found to be problematic because of an internal tension. Indeed, standard antireductionist external criticisms concerning reduction functions and laws in biology do not provide a full picture of the limits of Schaffner’s model. However, despite the internal tension, his model usefully highlights the importance of regulative ideals associated with the search for derivational, and embedding, deductive relations among mathematical (...)
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  35. What Experimental Economics Teaches Us About Models. [REVIEW]Anna Alexandrova - 2008 - Journal of Economic Methodology 15:197-204.
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  36. Gravitating Towards Stability: Guidobaldo's Aristotelian-Archimedean Synthesis.Maarten Van Dyck - 2006 - History of Science 44 (4):373-407.
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  37. Bayesian Confirmation of Theories That Incorporate Idealizations.Michael J. Shaffer - 2001 - Philosophy of Science 68 (1):36-52.
    Following Nancy Cartwright and others, I suggest that most (if not all) theories incorporate, or depend on, one or more idealizing assumptions. I then argue that such theories ought to be regimented as counterfactuals, the antecedents of which are simplifying assumptions. If this account of the logic form of theories is granted, then a serious problem arises for Bayesians concerning the prior probabilities of theories that have counterfactual form. If no such probabilities can be assigned, the the posterior probabilities will (...)
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  38. Metafore, modelli, linguaggio scientifico: il dibattito postempirista.Sergio Volodia Marcello Cremaschi - 1988 - In Melchiorre Virgilio (ed.), Simbolo e conoscenza. Milan, Italy: VIta e Pensiero. pp. 31-102.
    I discuss Mary Hess’s interaction-view of scientific metaphor, outline an alternative view and show how it may prove fruitful when applied to chapters of the history of science. I start with a reconstruction of the discussion on the nature of scientific models and on their relationship to metaphors that has taken place in the Anglo-Saxon philosophy of Science starting from the Fifties; the discovery started with Stephen Pepper and Kenneth Burke, reaching Thomas Kuhn, Marx Wartofsky, and George Lakoff via Max (...)
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  39. Remarks on Scientific Metaphors.Sergio Volodia Marcello Cremaschi - 1988 - In Maria Luisa Dalla Chiara & Maria Clara Galavotti (eds.), Temi e prospettive della Logica e Filosofia della Scienza. Volume 2. Bologna, Italy: CLUEB. pp. 114-116.
    Recent contributions by Kuhn, Wartofsky, and Granger, converge in the direction of an extended view of models, one that acknowledges a metaphorical dimension in the language of science. Such a view is in some respects the opposite of the views of both Bachelard and the Logical Empiricists. A number of familiar puzzles of the philosophy of science, such as the problem of reference, the opposition of realism and instrumentalism, that between explanation and understanding, and the status of scientific objectivity, may (...)
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  40. Models, Sherlock Holmes and the Emperor Claudius.Adam Toon - manuscript
    Recently, a number of authors have suggested that we understand scientific models in the same way as fictional characters, like Sherlock Holmes. The biggest challenge for this approach concerns the ontology of fictional characters. I consider two responses to this challenge, given by Roman Frigg, Ronald Giere and Peter Godfrey-Smith, and argue that neither is successful. I then suggest an alternative approach. While parallels with fiction are useful, I argue that models of real systems are more aptly compared to works (...)
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