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  1. An Instrument for What? Digital Computers, Simulation and Scientific Practice.Wendy S. Parker - 2010 - Spontaneous Generations 4 (1):39-44.
    As a device used by scientists in the course of performing research, the digital computer might be considered a scientific instrument. But if so, what is it an instrument for? This paper explores a number of answers to this question, focusing on the use of computers in a simulating mode.
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  • Experiments Versus Models: New Phenomena, Inference and Surprise.Mary S. Morgan - 2005 - Journal of Economic Methodology 12 (2):317-329.
    A comparison of models and experiments supports the argument that although both function as mediators and can be understood to work in an experimental mode, experiments offer greater epistemic power than models as a means to investigate the economic world. This outcome rests on the distinction that whereas experiments are versions of the real world captured within an artificial laboratory environment, models are artificial worlds built to represent the real world. This difference in ontology has epistemic consequences: experiments have greater (...)
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  • How Can Computer Simulations Produce New Knowledge?Claus Beisbart - 2012 - European Journal for Philosophy of Science 2 (3):395-434.
    It is often claimed that scientists can obtain new knowledge about nature by running computer simulations. How is this possible? I answer this question by arguing that computer simulations are arguments. This view parallels Norton’s argument view about thought experiments. I show that computer simulations can be reconstructed as arguments that fully capture the epistemic power of the simulations. Assuming the extended mind hypothesis, I furthermore argue that running the computer simulation is to execute the reconstructing argument. I discuss some (...)
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  • Computer Simulations in Science.Eric Winsberg - forthcoming - Stanford Encyclopedia of Philosophy.
  • A Tale of Two Methods.Eric Winsberg - 2009 - Synthese 169 (3):575 - 592.
    Simulations (both digital and analog) and experiments share many features. But what essential features distinguish them? I discuss two proposals in the literature. On one proposal, experiments investigate nature directly, while simulations merely investigate models. On another proposal, simulations differ from experiments in that simulationists manipulate objects that bear only a formal (rather than material) similarity to the targets of their investigations. Both of these proposals are rejected. I argue that simulations fundamentally differ from experiments with regard to the background (...)
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  • The Normative Force of Reasoning.Ralph Wedgwood - 2006 - Noûs 40 (4):660–686.
    What exactly is reasoning? Like many other philosophers, I shall endorse a broadly causal conception of reasoning. Reasoning is a causal process, in which one mental event (say, one’s accepting the conclusion of a certain argument) is caused by an antecedent mental event (say, one’s considering the premises of the argument). Just like causal accounts of action and causal accounts of perception, causal accounts of reasoning have to confront a version of what has come to be known as the problem (...)
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  • Are Computer Simulations Experiments? And If Not, How Are They Related to Each Other?Claus Beisbart - 2018 - European Journal for Philosophy of Science 8 (2):171-204.
    Computer simulations and experiments share many important features. One way of explaining the similarities is to say that computer simulations just are experiments. This claim is quite popular in the literature. The aim of this paper is to argue against the claim and to develop an alternative explanation of why computer simulations resemble experiments. To this purpose, experiment is characterized in terms of an intervention on a system and of the observation of the reaction. Thus, if computer simulations are experiments, (...)
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  • What is a Computer Simulation? A Review of a Passionate Debate.Nicole Saam - 2017 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 48 (2):293-309.
    Where should computer simulations be located on the ‘usual methodological map’ which distinguishes experiment from theory? Specifically, do simulations ultimately qualify as experiments or as thought experiments? Ever since Galison raised that question, a passionate debate has developed, pushing many issues to the forefront of discussions concerning the epistemology and methodology of computer simulation. This review article illuminates the positions in that debate, evaluates the discourse and gives an outlook on questions that have not yet been addressed.
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  • Testing Major Evolutionary Hypotheses About Religion with a Random Sample.David Sloan Wilson - 2005 - Human Nature 16 (4):382-409.
  • Structure‐Mapping: A Theoretical Framework for Analogy.Dedre Gentner - 1983 - Cognitive Science 7 (2):155-170.
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  • The Concept of Observation in Science and Philosophy.Dudley Shapere - 1982 - Philosophy of Science 49 (4):485-525.
    Through a study of a sophisticated contemporary scientific experiment, it is shown how and why use of the term 'observation' in reference to that experiment departs from ordinary and philosophical usages which associate observation epistemically with perception. The role of "background information" is examined, and general conclusions are arrived at regarding the use of descriptive language in and in talking about science. These conclusions bring out the reasoning by which science builds on what it has learned, and, further, how that (...)
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  • Naming and Necessity.S. Kripke - 1972 - Tijdschrift Voor Filosofie 45 (4):665-666.
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  • Computer Simulation, Measurement, and Data Assimilation.Wendy S. Parker - 2017 - British Journal for the Philosophy of Science 68 (1):273-304.
    This article explores some of the roles of computer simulation in measurement. A model-based view of measurement is adopted and three types of measurement—direct, derived, and complex—are distinguished. It is argued that while computer simulations on their own are not measurement processes, in principle they can be embedded in direct, derived, and complex measurement practices in such a way that simulation results constitute measurement outcomes. Atmospheric data assimilation is then considered as a case study. This practice, which involves combining information (...)
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  • Confirmation Via Analogue Simulation: What Dumb Holes Could Tell Us About Gravity.Radin Dardashti, Karim P. Y. Thébault & Eric Winsberg - 2017 - British Journal for the Philosophy of Science 68 (1).
    In this article we argue for the existence of ‘analogue simulation’ as a novel form of scientific inference with the potential to be confirmatory. This notion is distinct from the modes of analogical reasoning detailed in the literature, and draws inspiration from fluid dynamical ‘dumb hole’ analogues to gravitational black holes. For that case, which is considered in detail, we defend the claim that the phenomena of gravitational Hawking radiation could be confirmed in the case that its counterpart is detected (...)
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  • Lessons From the Large Hadron Collider for Model-Based Experimentation: The Concept of a Model of Data Acquisition and the Scope of the Hierarchy of Models.Koray Karaca - 2018 - Synthese 195 (12):1-22.
    According to the hierarchy of models account of scientific experimentation developed by Patrick Suppes and elaborated by Deborah Mayo, theoretical considerations about the phenomena of interest are involved in an experiment through theoretical models that in turn relate to experimental data through data models, via the linkage of experimental models. In this paper, I dispute the HoM account in the context of present-day high-energy physics experiments. I argue that even though the HoM account aims to characterize experimentation as a model-based (...)
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  • Computer Simulations as Experiments.Anouk Barberousse, Sara Franceschelli & Cyrille Imbert - 2009 - Synthese 169 (3):557 - 574.
    Whereas computer simulations involve no direct physical interaction between the machine they are run on and the physical systems they are used to investigate, they are often used as experiments and yield data about these systems. It is commonly argued that they do so because they are implemented on physical machines. We claim that physicality is not necessary for their representational and predictive capacities and that the explanation of why computer simulations generate desired information about their target system is only (...)
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  • Models, Measurement and Computer Simulation: The Changing Face of Experimentation.Margaret Morrison - 2009 - Philosophical Studies 143 (1):33-57.
    The paper presents an argument for treating certain types of computer simulation as having the same epistemic status as experimental measurement. While this may seem a rather counterintuitive view it becomes less so when one looks carefully at the role that models play in experimental activity, particularly measurement. I begin by discussing how models function as “measuring instruments” and go on to examine the ways in which simulation can be said to constitute an experimental activity. By focussing on the connections (...)
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  • Why Monte Carlo Simulations Are Inferences and Not Experiments.Claus Beisbart & John D. Norton - 2012 - International Studies in the Philosophy of Science 26 (4):403-422.
    Monte Carlo simulations arrive at their results by introducing randomness, sometimes derived from a physical randomizing device. Nonetheless, we argue, they open no new epistemic channels beyond that already employed by traditional simulations: the inference by ordinary argumentation of conclusions from assumptions built into the simulations. We show that Monte Carlo simulations cannot produce knowledge other than by inference, and that they resemble other computer simulations in the manner in which they derive their conclusions. Simple examples of Monte Carlo simulations (...)
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  • Does Matter Really Matter? Computer Simulations, Experiments, and Materiality.Wendy S. Parker - 2009 - Synthese 169 (3):483-496.
    A number of recent discussions comparing computer simulation and traditional experimentation have focused on the significance of “materiality.” I challenge several claims emerging from this work and suggest that computer simulation studies are material experiments in a straightforward sense. After discussing some of the implications of this material status for the epistemology of computer simulation, I consider the extent to which materiality (in a particular sense) is important when it comes to making justified inferences about target systems on the basis (...)
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  • Connectionism and Cognitive Architecture: A Critical Analysis.Jerry A. Fodor & Zenon W. Pylyshyn - 1988 - Cognition 28 (1-2):3-71.
    This paper explores the difference between Connectionist proposals for cognitive a r c h i t e c t u r e a n d t h e s o r t s o f m o d e l s t hat have traditionally been assum e d i n c o g n i t i v e s c i e n c e . W e c l a i m t h a t t h (...)
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  • Error and the Growth of Experimental Knowledge.Deborah Mayo - 1997 - British Journal for the Philosophy of Science 48 (3):455-459.
     
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  • Lessons From the Large Hadron Collider for Model-Based Experimentation: The Concept of a Model of Data Acquisition and the Scope of the Hierarchy of Models.Koray Karaca - 2018 - Synthese 195 (12):5431-5452.
    According to the hierarchy of models account of scientific experimentation developed by Patrick Suppes and elaborated by Deborah Mayo, theoretical considerations about the phenomena of interest are involved in an experiment through theoretical models that in turn relate to experimental data through data models, via the linkage of experimental models. In this paper, I dispute the HoM account in the context of present-day high-energy physics experiments. I argue that even though the HoM account aims to characterize experimentation as a model-based (...)
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  • Is Computer Simulation Changing the Face of Experimentation?Ronald N. Giere - 2009 - Philosophical Studies 143 (1):59 - 62.
    Morrison points out many similarities between the roles of simulation models and other sorts of models in science. On the basis of these similarities she claims that running a simulation is epistemologically on a par with doing a traditional experiment and that the output of a simulation therefore counts as a measurement. I agree with her premises but reject the inference. The epistemological payoff of a traditional experiment is greater (or less) confidence in the fit between a model and a (...)
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  • Computer Simulations and Experiments: The Case of the Higgs Boson.Michela Massimi & Wahid Bhimji - 2015 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 51:71-81.
  • Error and the Growth of Experimental Knowledge.Deborah Mayo - 1996 - International Studies in the Philosophy of Science 15 (1):455-459.
  • Models and Analogies in Science.Mary B. Hesse - 1966 - Philosophy and Rhetoric 3 (3):190-191.
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  • Order Out of Chaos? A Case Study in High Energy Physics.Rafaela Hillerbrand - 2012 - Studia Philosophica Estonica 5 (2):61-78.
    In recent years, computational sciences such as computational hydrodynamics or computational field theory have supplemented theoretical and experimental investigations in many scientific fields. Often, there is a seemingly fruitful overlap between theory, experiment, and numerics. The computational sciences are highly dynamic and seem a fairly successful endeavor---at least if success is measured in terms of publications or engineering applications. However, for theories, success in application and correctness are two very different things; and just the same may hold for "methodologies" like (...)
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  • Introduction : The Extended Mind in Focus.Richard Menary - unknown
  • The Philosophy of Scientific Experimentation: A Review.H. Radder - unknown
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