Authors
Claus Beisbart
University of Bern
Abstract
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 are analysed to identify the underlying inferences
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DOI 10.1080/02698595.2012.748497
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References found in this work BETA

Models and Analogies in Science.Mary B. Hesse - 1963 - University of Notre Dame Press.
Image and Logic: A Material Culture of Microphysics.Peter Galison (ed.) - 1997 - University of Chicago Press: Chicago.
Models and Analogies in Science.Mary Hesse - 1965 - British Journal for the Philosophy of Science 16 (62):161-163.
Science in the Age of Computer Simulation.Eric Winsberg - 2010 - University of Chicago Press.

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Citations of this work BETA

Computer Simulations in Science.Eric Winsberg - forthcoming - Stanford Encyclopedia of Philosophy.
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 Simulation and the Features of Novel Empirical Data.Greg Lusk - 2016 - Studies in History and Philosophy of Science Part A 56:145-152.

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