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  1. The scientific demarcation problem: a formal and model-based approach to falsificationism.Attard Jeremy - manuscript
    The problem of demarcating between what is scientific and what is pseudoscientific or merely unscientific - in other words, the problem of defining scientificity - remains open. The modern debate was firstly structured around Karl Popper's falsificationist epistemology from the 1930's, before diversifying a few decades later. His central idea is that what makes something scientific is not so much how adequate it is with data, but rather to what extent it might not have been so. Since the second half (...)
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  2. Mixed-grain Property Collaboration: Reconstructing Multiple Realization after the Elimination of Levels.Robert D. Rupert - manuscript
    This paper was written for and presented at a symposium on Multiple Realizability at the Central Division of the APA in 2022. It's in somewhat rough shape, especially the later parts. I hope to be in a position soon to post a revised and more carefully worked out version. The basic argument of the first half is this: Realization of the interesting sort (and thus MR of the interesting sort) requires tidy separation of levels (with realizers being at a lower (...)
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  3. Bohr's atomic model and paraconsistent logic.Pandora Hadzidaki -
    Bohr’s atomic model is one of the better known examples of empirically successful, albeit inconsistent, theoretical schemes in the history of physics. For this reason, many philosophers use this model to illustrate their position for the occurrence and the function of inconsistency in science. In this paper, I proceed to a critical comparison of the structure and the aims of Bohr’s research program – the starting point of which was the formulation of his model – with some of its contemporary (...)
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  4. (1 other version)Towards a Taxonomy of the Model-Ladenness of Data.Alisa Bokulich - forthcoming - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association.
    Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are not only data-laden, but data are also model-laden or model filtered. In this paper I elaborate and defend the second, more controversial, component of the symbiosis view. In particular, I construct a preliminary taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. These include data conversion, data correction, data interpolation, (...)
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  5. Standard Rationality versus Inclusive Rationality: A Critical Assessment.Roberto Fumagalli - forthcoming - Behavioural Public Policy.
    This paper critically assesses Rizzo and Whitman’s theory of inclusive rationality in light of the ongoing cross-disciplinary debate about rationality, welfare analyses and policy evaluation. The paper aims to provide three main contributions to this debate. First, it explicates the relation between the consistency conditions presupposed by standard axiomatic conceptions of rationality and the standards of rationality presupposed by Rizzo and Whitman’s theory of inclusive rationality. Second, it provides a qualified defence of the consistency conditions presupposed by standard axiomatic conceptions (...)
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  6. Normative Formal Epistemology as Modelling.Joe Roussos - forthcoming - The British Journal for the Philosophy of Science.
    I argue that normative formal epistemology (NFE) is best understood as modelling, in the sense that this is the reconstruction of its methodology on which NFE is doing best. I focus on Bayesianism and show that it has the characteristics of modelling. But modelling is a scientific enterprise, while NFE is normative. I thus develop an account of normative models on which they are idealised representations put to normative purposes. Normative assumptions, such as the transitivity of comparative credence, are characterised (...)
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  7. Non-Representational Models and Objectual Understanding.Christopher Pincock & Michael Poznic - 2024 - Erkenntnis:1-22.
    This paper argues that investigations into how to best make something often provide researchers with an objectual understanding of their target phenomena. This argument starts with an extended investigation into the non-representational uses of models. In particular, we identify a special sort of “design model” whose aim is to guide the production of phenomena. Clarifying how these design models are evaluated shows that they are evaluated in different ways than representational models. Once the character of design models has been fixed, (...)
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  8. (1 other version)Recipes for Science: An Introduction to Scientific Methods and Reasoning (2nd edition).Angela Potochnik, Matteo Colombo & Cory Wright - 2024 - Routledge.
    Scientific literacy is an essential aspect of an undergraduate education. Recipes for Science responds to this need by providing an accessible introduction to the nature of science and scientific methods appropriate for any beginning college student. The book is adaptable to a wide variety of different courses, such as introductions to scientific reasoning, methods courses in scientific disciplines, science education, and philosophy of science. -/- Recipes for Science ​​was first published in 2018, and a thoroughly revised second edition was published (...)
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  9. Scientific Representation. [REVIEW]Cory Wright - 2024 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 55 (2).
  10. Are Models Our Tools Not Our Masters?Caspar Jacobs - 2023 - Synthese 202 (4):1-21.
    It is often claimed that one can avoid the kind of underdetermination that is a typical consequence of symmetries in physics by stipulating that symmetry-related models represent the same state of affairs (Leibniz Equivalence). But recent commentators (Dasgupta 2011; Pooley 2021; Pooley and Read 2021; Teitel 2021a) have responded that claims about the representational capacities of models are irrelevant to the issue of underdetermination, which concerns possible worlds themselves. In this paper I distinguish two versions of this objection: (1) that (...)
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  11. Managing Performative Models.Donal Khosrowi - 2023 - Philosophy of the Social Sciences 53 (5):371-395.
    Scientific models can be performative: they can causally affect the phenomena they are intended to represent. The existing literature offers two responses. The appraisal view emphasizes that performativity can sometimes be a good-making model attribute, e.g., when predictions steer the public’s behavior in desirable ways. The mitigation view seeks to endogenize agents’ behavioral response to model-issued forecasts to get rid of performativity instead. This paper argues that neither approach is fully compelling: the appraisal view encounters severe concerns about moral values (...)
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  12. From depressed mice to depressed patients: a less “standardized” approach to improving translation.Monika Piotrowska - 2023 - Biology and Philosophy 38 (6):1-19.
    Depression is a widespread and debilitating disorder, but developing effective treatments has proven challenging. Despite success in animal models, many treatments fail in human trials. While various factors contribute to this translational failure, standardization practices in animal research are often overlooked. This paper argues that certain standardization choices in behavioral neuroscience research on depression can limit the generalizability of results from rodents to humans. This raises ethical and scientific concerns, including animal waste and a lack of progress in treating human (...)
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  13. Simple Models in Complex Worlds: Occam’s Razor and Statistical Learning Theory.Falco J. Bargagli Stoffi, Gustavo Cevolani & Giorgio Gnecco - 2022 - Minds and Machines 32 (1):13-42.
    The idea that “simplicity is a sign of truth”, and the related “Occam’s razor” principle, stating that, all other things being equal, simpler models should be preferred to more complex ones, have been long discussed in philosophy and science. We explore these ideas in the context of supervised machine learning, namely the branch of artificial intelligence that studies algorithms which balance simplicity and accuracy in order to effectively learn about the features of the underlying domain. Focusing on statistical learning theory, (...)
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  14. 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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  15. Philosophy of science in practice in ecological model building.Luana Poliseli, Jeferson G. E. Coutinho, Blandina Viana, Federica Russo & Charbel N. El-Hani - 2022 - Biology and Philosophy 37 (4):0-0.
    This article addresses the contributions of the literature on the new mechanistic philosophy of science for the scientific practice of model building in ecology. This is reflected in a one-to-one interdisciplinary collaboration between an ecologist and a philosopher of science during science-in-the-making. We argue that the identification, reconstruction and understanding of mechanisms is context-sensitive, and for this case study mechanistic modeling did not present a normative role but a heuristic one. We expect our study to provides useful epistemic tools for (...)
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  16. On the continuity of metaphysics with science: Some scepticism and some suggestions.Jack Ritchie - 2022 - Metaphilosophy 53 (2-3):202-220.
  17. Two epistemological challenges regarding hypothetical modeling.Peter Tan - 2022 - Synthese 200 (6).
    Sometimes, scientific models are either intended to or plausibly interpreted as representing nonactual but possible targets. Call this “hypothetical modeling”. This paper raises two epistemological challenges concerning hypothetical modeling. To begin with, I observe that given common philosophical assumptions about the scope of objective possibility, hypothetical models are fallible with respect to what is objectively possible. There is thus a need to distinguish between accurate and inaccurate hypothetical modeling. The first epistemological challenge is that no account of the epistemology of (...)
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  18. Comparer les modèles à l’aide du vecteur caractéristique : fonction, nature, principe et usage des modèles.Franck Varenne - 2022 - Natures Sciences Sociétés 30 (1):93-102.
    In the context of pluralization, sophistication, and combination of formal models, it is becoming difficult to propose uniform – or even comparable – model comparison practices. This paper outlines a broad and classificatory comparative epistemology of models. The aim of this epistemology is to propose applicable, and if necessary rectifiable, conceptual tools that can be useful to modellers as well as to historians and epistemologists. The notion of model characteristic vector – incorporating concepts of function, nature, principle and use of (...)
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  19. The Predictive Turn in Neuroscience.Daniel A. Weiskopf - 2022 - Philosophy of Science 89 (5):1213-1222.
    Neuroscientists have in recent years turned to building models that aim to generate predictions rather than explanations. This “predictive turn” has swept across domains including law, marketing, and neuropsychiatry. Yet the norms of prediction remain undertheorized relative to those of explanation. I examine two styles of predictive modeling and show how they exemplify the normative dynamics at work in prediction. I propose an account of how predictive models, conceived of as technological devices for aiding decision-making, can come to be adequate (...)
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  20. Modeling the Past: Using History of Science to predict alternative scenarios on science-based legislation.José Ferraz-Caetano - 2021 - Hypothesis Historia Periodical 1 (1):60-70.
    In an ever-changing world, when we search for answers on our present challenges, it can be tricky to extrapolate past realities when concerning science-based issues. Climate change, public health or artificial intelligence embody issues on how scientific evidence is often challenged, as false beliefs could drive the design of public policies and legislation. Therefore , how can we foresee if science can tip the scales of political legislation? In this article, we outline how models of historical cases can be used (...)
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  21. Emergent Models for Moral AI Spirituality.Mark Graves - 2021 - International Journal of Interactive Multimedia and Artificial Intelligence 7 (1):7-15.
    Examining AI spirituality can illuminate problematic assumptions about human spirituality and AI cognition, suggest possible directions for AI development, reduce uncertainty about future AI, and yield a methodological lens sufficient to investigate human-AI sociotechnical interaction and morality. Incompatible philosophical assumptions about human spirituality and AI limit investigations of both and suggest a vast gulf between them. An emergentist approach can replace dualist assumptions about human spirituality and identify emergent behavior in AI computation to overcome overly reductionist assumptions about computation. Using (...)
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  22. When Experiments Need Models.Donal Khosrowi - 2021 - Philosophy of the Social Sciences 51 (4):400-424.
    This paper argues that an important type of experiment-target inference, extrapolating causal effects, requires models to be successful. Focusing on extrapolation in Evidence-Based Policy, it is ar...
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  23. Models, Fictions and Artifacts.Tarja Knuuttila - 2021 - In Wenceslao J. Gonzalez (ed.), Language and Scientific Research. Springer Verlag. pp. 199-22.
    This paper discusses modeling from the artifactual perspective. The artifactual approach conceives models as erotetic devices. They are purpose-built systems of dependencies that are constrained in view of answering a pending scientific question, motivated by theoretical or empirical considerations. In treating models as artifacts, the artifactual approach is able to address the various languages of sciences that are overlooked by the traditional accounts that concentrate on the relationship of representation in an abstract and general manner. In contrast, the artifactual approach (...)
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  24. Série Investigações Filosóficas: Textos Selecionados de Filosofia da Ciência II [Philosophical Investigation Series: Selected Texts on Philosophy of Science II].Luana Poliseli (ed.) - 2021 - Pelotas: Editora da Universidade Federal de Pelotas.
    A Série Investigação Filosófica, uma iniciativa do Núcleo de Ensino e Pesquisa em Filosofia do Departamento de Filosofia da UFPel e do Grupo de Pesquisa Investigação Filosófica do Departamento de Filosofia da UNIFAP, sob o selo editorial do NEPFil online e da Editora da Universidade Federal de Pelotas, com auxílio financeiro da John Templeton Foundation, tem por objetivo precípuo a publicação da tradução para a língua portuguesa de textos selecionados a partir de diversas plataformas internacionalmente reconhecidas, tal como a Stanford (...)
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  25. The Truth About Better Understanding?Lewis Ross - 2021 - Erkenntnis 88 (2):747-770.
    The notion of understanding occupies an increasingly prominent place in contemporary epistemology, philosophy of science, and moral theory. A central and ongoing debate about the nature of understanding is how it relates to the truth. In a series of influential contributions, Catherine Elgin has used a variety of familiar motivations for antirealism in philosophy of science to defend a non- factive theory of understanding. Key to her position are: (i) the fact that false theories can contribute to the upwards trajectory (...)
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  26. Making Confident Decisions with Model Ensembles.Joe Roussos, Richard Bradley & Roman Frigg - 2021 - Philosophy of Science 88 (3):439-460.
    Many policy decisions take input from collections of scientific models. Such decisions face significant and often poorly understood uncertainty. We rework the so-called confidence approach to tackle decision-making under severe uncertainty with multiple models, and we illustrate the approach with a case study: insurance pricing using hurricane models. The confidence approach has important consequences for this case and offers a powerful framework for a wide class of problems. We end by discussing different ways in which model ensembles can feed information (...)
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  27. The Epistemic Duties of Philosophers: An Addendum.Philippe van Basshuysen & Lucie White - 2021 - Kennedy Institute of Ethics Journal 31 (4):447-451.
    We were slightly concerned, upon having read Eric Winsberg, Jason Brennan and Chris Surprenant’s reply to our paper “Were Lockdowns Justified? A Return to the Facts and Evidence”, that they may have fundamentally misunderstood the nature of our argument, so we issue the following clarification, along with a comment on our motivations for writing such a piece, for the interested reader.
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  28. Three Ways in Which Pandemic Models May Perform a Pandemic.Philippe Van Basshuysen, Lucie White, Donal Khosrowi & Mathias Frisch - 2021 - Erasmus Journal for Philosophy and Economics 14 (1):110-127.
    Models not only represent but may also influence their targets in important ways. While models’ abilities to influence outcomes has been studied in the context of economic models, often under the label ‘performativity’, we argue that this phenomenon also pertains to epidemiological models, such as those used for forecasting the trajectory of the Covid-19 pandemic. After identifying three ways in which a model by the Covid-19 Response Team at Imperial College London may have influenced scientific advice, policy, and individual responses, (...)
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  29. True Enough, by Catherine Z. Elgin.John Bengson - 2020 - Mind 129 (513):256-268.
    I identify the central theses of True Enough and argue that Elgin's principal argument for her non-factive view of understanding fails. This argument emphasizes the cognitive contributions of science (and other disciplines) that occur via false claims. Careful reflection reveals that it is actually Elgin’s view that mishandles those contributions. Her non-factive view is also unable to accommodate other types of epistemic improvement, and makes a range of simple comparisons of understanding impossible.
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  30. Why I am not a literalist.Zoe Drayson - 2020 - Mind and Language 35 (5):661-670.
    Carrie Figdor argues for literalism, a semantic claim about psychological predicates, on the basis of a scientific claim about the nature of psychological properties. I argue that her scientific claim is based on controversial interpretations of scientific modelling, and that even if it were correct it would not justify her claims that psychological predicates are undergoing radical conceptual change.
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  31. Understanding climate change with statistical downscaling and machine learning.Julie Jebeile, Vincent Lam & Tim Räz - 2020 - Synthese (1-2):1-21.
    Machine learning methods have recently created high expectations in the climate modelling context in view of addressing climate change, but they are often considered as non-physics-based ‘black boxes’ that may not provide any understanding. However, in many ways, understanding seems indispensable to appropriately evaluate climate models and to build confidence in climate projections. Relying on two case studies, we compare how machine learning and standard statistical techniques affect our ability to understand the climate system. For that purpose, we put five (...)
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  32. Philosophical dogmatism inhibiting the anti-Copernican interpretation of the Michelson Morley experiment.Spyridon Kakos - 2020 - Harmonia Philosophica 1.
    From the beginning of time, humans believed they were the center of the universe. Such important beings could be nowhere else than at the very epicenter of existence, with all the other things revolving around them. Was this an arrogant position? Only time will tell. What is certain is that as some people were so certain of their significance, aeons later some other people became too confident in their unimportance. In such a context, the Earth quickly lost its privileged position (...)
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  33. Physical Entity as Quantum Information.Vasil Penchev - 2020 - Philosophy of Science eJournal (Elsevier: SSRN) 13 (35):1-15.
    Quantum mechanics was reformulated as an information theory involving a generalized kind of information, namely quantum information, in the end of the last century. Quantum mechanics is the most fundamental physical theory referring to all claiming to be physical. Any physical entity turns out to be quantum information in the final analysis. A quantum bit is the unit of quantum information, and it is a generalization of the unit of classical information, a bit, as well as the quantum information itself (...)
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  34. Policymaking under scientific uncertainty.Joe Roussos - 2020 - Dissertation, London School of Economics
    Policymakers who seek to make scientifically informed decisions are constantly confronted by scientific uncertainty and expert disagreement. This thesis asks: how can policymakers rationally respond to expert disagreement and scientific uncertainty? This is a work of non-ideal theory, which applies formal philosophical tools developed by ideal theorists to more realistic cases of policymaking under scientific uncertainty. I start with Bayesian approaches to expert testimony and the problem of expert disagreement, arguing that two popular approaches— supra-Bayesianism and the standard model of (...)
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  35. Mechanist idealisation in systems biology.Dingmar van Eck & Cory Wright - 2020 - Synthese 199 (1-2):1555-1575.
    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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  36. Cybernetics for the command economy: Foregrounding entropy in late Soviet planning.Diana Kurkovsky West - 2020 - History of the Human Sciences 33 (1):36-51.
    The Soviet Union had a long and complex relationship with cybernetics, especially in the domain of planning. This article looks at Soviet postwar efforts to draw up plans for the rapidly developing, industrializing, and urbanizing Siberia, where cybernetic models were used to develop a vision of cybernetic socialism. Removed from Moscow bureaucracy and politics, the various planning institutes of the Siberian Academy of Sciences became a key frontier for exploring the potential of cybernetic thinking to offer a necessary corrective to (...)
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  37. Computer Simulation Validation: Fundamental Concepts, Methodological Frameworks, and Philosophical Perspectives.Claus Beisbart & Nicole J. Saam (eds.) - 2019 - Springer Verlag.
    This unique volume introduces and discusses the methods of validating computer simulations in scientific research. The core concepts, strategies, and techniques of validation are explained by an international team of pre-eminent authorities, drawing on expertise from various fields ranging from engineering and the physical sciences to the social sciences and history. The work also offers new and original philosophical perspectives on the validation of simulations. Topics and features: introduces the fundamental concepts and principles related to the validation of computer simulations, (...)
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  38. A Resilience Toolbox and Research Design for Black Sky Hazards to Power Grids.Dmitry Borisoglebsky & Liz Varga - 2019 - Complexity 2019:1-15.
    A structured collection of tools for engineering resilience and a research approach to improve the resilience of a power grid are described in this paper. The collection is organized by a two-dimensional array formed from typologies of power grid components and business processes. These two dimensions provide physical and operational outlooks, respectively, for a power grid. The approach for resilience research is based on building a simulation model of a power grid which utilizes a resilience assessment equation to assess baseline (...)
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  39. Interprétabilité et explicabilité pour l’apprentissage machine : entre modèles descriptifs, modèles prédictifs et modèles causaux. Une nécessaire clarification épistémologique.Christophe Denis & Franck Varenne - 2019 - Actes de la Conférence Nationale En Intelligence Artificielle - CNIA 2019.
    Le déficit d’explicabilité des techniques d’apprentissage machine (AM) pose des problèmes opérationnels, juridiques et éthiques. Un des principaux objectifs de notre projet est de fournir des explications éthiques des sorties générées par une application fondée sur de l’AM, considérée comme une boîte noire. La première étape de ce projet, présentée dans cet article, consiste à montrer que la validation de ces boîtes noires diffère épistémologiquement de celle mise en place dans le cadre d’une modélisation mathématique et causale d’un phénomène physique. (...)
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  40. Probing Possibilities: Toy Models, Minimal Models, and Exploratory Models.Axel Gelfert - 2019 - In Matthieu Fontaine, Cristina Barés-Gómez, Francisco Salguero-Lamillar, Lorenzo Magnani & Ángel Nepomuceno-Fernández (eds.), Model-Based Reasoning in Science and Technology: Inferential Models for Logic, Language, Cognition and Computation. Springer Verlag.
    According to one influential view, model-building in science is primarily a matter of simplifying theoretical descriptions of real-world target systems using abstraction and idealization. This view, however, does not adequately capture all types of models. Many contemporary models in the natural and social sciences – from physics to biology to economics – stand in a more tenuous relationship with real-world target systems and have a decidedly stipulative element, in that they create, by fiat, ‘model worlds’ that operate according to some (...)
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  41. Why experiments matter.Arnon Levy & Adrian Currie - 2019 - Inquiry: An Interdisciplinary Journal of Philosophy 62 (9-10):1066-1090.
    ABSTRACTExperimentation is traditionally considered a privileged means of confirmation. However, why and how experiments form a better confirmatory source relative to other strategies is unclear, and recent discussions have identified experiments with various modeling strategies on the one hand, and with ‘natural’ experiments on the other hand. We argue that experiments aiming to test theories are best understood as controlled investigations of specimens. ‘Control’ involves repeated, fine-grained causal manipulation of focal properties. This capacity generates rich knowledge of the object investigated. (...)
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  42. Epistemic Norms: Truth Conducive Enough.Lisa Warenski - 2019 - Synthese 198 (3):2721-2741.
    Epistemology needs to account for the success of science. In True Enough, Catherine Elgin argues that a veritist epistemology is inadequate to this task. She advocates shifting epistemology’s focus away from true belief and toward understanding, and further, jettisoning truth from its privileged place in epistemological theorizing. Pace Elgin, I argue that epistemology’s accommodation of science does not require rejecting truth as the central epistemic value. Instead, it requires understanding veritism in an ecumenical way that acknowledges a rich array of (...)
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  43. (1 other version)Models in Search of Targets: Exploratory Modelling and the Case of Turing Patterns.Axel Gelfert - 2018 - In Alexander Christian, David Hommen, Gerhard Schurz & N. Retzlaff (eds.), Philosophy of Science. European Studies in Philosophy of Science, vol 9. Springer. pp. 245-269.
    Traditional frameworks for evaluating scientific models have tended to downplay their exploratory function; instead they emphasize how models are inherently intended for specific phenomena and are to be judged by their ability to predict, reproduce, or explain empirical observations. By contrast, this paper argues that exploration should stand alongside explanation, prediction, and representation as a core function of scientific models. Thus, models often serve as starting points for future inquiry, as proofs of principle, as sources of potential explanations, and as (...)
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  44. From Models to Simulations.Franck Varenne - 2018 - London, UK: Routledge.
    This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. -/- Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how (...)
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  45. Scientific Realism and the Earth Sciences.Teru Miyake - 2017 - In Juha Saatsi (ed.), The Routledge Handbook of Scientific Realism. New York: Routledge. pp. 333-344.
  46. Meta-Theoretical Contributions to the Constitution of a Model-Based Didactics of Science.Yefrin Ariza, Pablo Lorenzano & Agustín Adúriz-Bravo - 2016 - Science & Education 25 (7-8):747-773.
    There is nowadays consensus in the community of didactics of science regarding the need to include the philosophy of science in didactical research, science teacher education, curriculum design, and the practice of science education in all educational levels. Some authors have identified an ever-increasing use of the concept of ‘theoretical model’, stemming from the so-called semantic view of scientific theories. However, it can be recognised that, in didactics of science, there are over-simplified transpositions of the idea of model. In this (...)
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  47. Modeling the Heavens: Sphairopoiia and Ptolemy’s Planetary Hypotheses.Elizabeth Hamm - 2016 - Perspectives on Science 24 (4):416-424.
    Ptolemy wrote the Planetary Hypotheses for both astronomers and instrument-makers. Most studies of this text concentrate on its meaning for the former, but there remain many questions surrounding its meaning for the latter.1 This article investigates the purpose of Ptolemy’s Planetary Hypotheses in light of what he says about instrument-making. It takes up the following questions: what kind of instrument does Ptolemy describe? And, could such an instrument have been constructed? I argue that he did not have one specific design (...)
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  48. Modeling Organs with Organs on Chips: Scientific Representation and Engineering Design as Modeling Relations.Michael Poznic - 2016 - Philosophy and Technology 29 (4):357-371.
    On the basis of a case study in bioengineering, this paper proposes a novel perspective on models in science and engineering. This is done with the help of two notions: representation and design. These two notions are interpreted as referring to modeling relations between vehicles and targets that differ in their respective directions of fit. The representation relation has a vehicle-to-target direction of fit and the design relation has a target-to-vehicle direction of fit. The case study of an organ on (...)
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  49. Scientific misrepresentation and guides to ontology: the need for representational code and contents.Elay Shech - 2015 - Synthese 192 (11):3463-3485.
    In this paper I show how certain requirements must be set on any tenable account of scientific representation, such as the requirement allowing for misrepresentation. I then continue to argue that two leading accounts of scientific representation— the inferential account and the interpretational account—are flawed for they do not satisfy such requirements. Through such criticism, and drawing on an analogy from non-scientific representation, I also sketch the outline of a superior account. In particular, I propose to take epistemic representations to (...)
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  50. La surprise comme mesure de l'empiricité des simulations computationnelles.Franck Varenne - 2015 - In Natalie Depraz & Claudia Serban (eds.), La surprise. A l'épreuve des langues. Hermann. pp. 199-217.
    This chapter elaborates and develops the thesis originally put forward by Mary Morgan (2005) that some mathematical models may surprise us, but that none of them can completely confound us, i.e. let us unable to produce an ex post theoretical understanding of the outcome of the model calculations. This chapter intends to object and demonstrate that what is certainly true of classical mathematical models is however not true of pluri-formalized simulations with multiple axiomatic bases. This chapter thus proposes to show (...)
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