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  1. Data and Model Operations in Computational Sciences: The Examples of Computational Embryology and Epidemiology.Fabrizio Li Vigni - 2022 - Perspectives on Science 30 (4):696-731.
    Computer models and simulations have become, since the 1960s, an essential instrument for scientific inquiry and political decision making in several fields, from climate to life and social sciences. Philosophical reflection has mainly focused on the ontological status of the computational modeling, on its epistemological validity and on the research practices it entails. But in computational sciences, the work on models and simulations are only two steps of a longer and richer process where operations on data are as important as, (...)
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  • 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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  • Thought Experiments and the Scientific Imagination.Alice Murphy - 2020 - Dissertation, University of Leeds
    Thought experiments (TEs) are important tools in science, used to both undermine and support theories, and communicate and explain complex phenomena. Their interest within philosophy of science has been dominated by a narrow question: How do TEs increase knowledge? My aim is to push beyond this to consider their broader value in scientific practice. I do this through an investigation into the scientific imagination. Part one explores questions regarding TEs as “experiments in the imagination” via a debate concerning the epistemic (...)
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  • Trustworthy Simulations and Their Epistemic Hierarchy.Peter Mättig - 2021 - Synthese 199 (5-6):14427-14458.
    We analyze the usage of computer simulation at the LHC and derive seven jointly necessary requirements for a simulation to be considered ’trustworthy’, such that it can be used as proxy for experiments. We show that these requirements can also be applied to systems without direct experimental access and discuss their validity for properties that have not yet been probed. While being necessary, these requirements are not sufficient. Such trustworthy simulations will be analyzed for the relative epistemic statuses of simulation (...)
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  • Simulation and Calibration: Mitigating Uncertainty.Deborah Haar - 2021 - Philosophy of Science 88 (5):985-996.
    Calibrating a simulation is a crucial step for certain kinds of simulation modeling, and it results in a simulation that is epistemically different from its pre- or uncalibrated counterpart. This article discusses how simulation model builders mitigate uncertainty about model parameters that are necessary for modeling through calibration and argues that the simulation outcomes after calibration are physically meaningful and relevant. When evaluating the epistemic status of computer simulations, comparisons between computer simulations and traditional experiments need to consider this important (...)
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  • Technology and Mathematics.Sven Ove Hansson - 2020 - Philosophy and Technology 33 (1):117-139.
    In spite of their practical importance, the connections between technology and mathematics have not received much scholarly attention. This article begins by outlining how the technology–mathematics relationship has developed, from the use of simple aide-mémoires for counting and arithmetic, via the use of mathematics in weaving, building and other trades, and the introduction of calculus to solve technological problems, to the modern use of computers to solve both technological and mathematical problems. Three important philosophical issues emerge from this historical résumé: (...)
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  • Regimes of Evidence in Complexity Sciences.Fabrizio Li Vigni - 2021 - Perspectives on Science 29 (1):62-103.
    Since their inception in the 1980s, complexity sciences have been described as a revolutionary new domain of research. By describing some of the practices and assumptions of its representatives, the present article shows that this field is an association of subdisciplines laying on existing disciplinary footholds. The general question guiding us here is: On what basis do complexity scientists consider their inquiry methods and results as valuable? To answer it, I describe five “epistemic argumentative regimes,” namely the ways in which (...)
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  • Epistemic Entitlements and the Practice of Computer Simulation.John Symons & Ramón Alvarado - 2019 - Minds and Machines 29 (1):37-60.
    What does it mean to trust the results of a computer simulation? This paper argues that trust in simulations should be grounded in empirical evidence, good engineering practice, and established theoretical principles. Without these constraints, computer simulation risks becoming little more than speculation. We argue against two prominent positions in the epistemology of computer simulation and defend a conservative view that emphasizes the difference between the norms governing scientific investigation and those governing ordinary epistemic practices.
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