Computational Modeling in Philosophy

(2022)
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Abstract

Computational modeling should play a central role in philosophy. In this introduction to our topical collection, we propose a small topology of computational modeling in philosophy in general, and show how the various contributions to our topical collection ft into this overall picture. On this basis, we describe some of the ways in which computational models from other disciplines have found their way into philosophy, and how the principles one found here still underlie current trends in the feld. Moreover, we argue that philosophers contribute to computational modeling not only by building their own models, but also by thinking about the various applications of the method in philosophy and the sciences. In this context, we note that models in philosophy are usually simple, while models in the sciences are often more complex and empirically grounded. Bridging certain methodological gaps that arise from this discrepancy may prove to be challenging and fruitful for the further development of computational modeling in philosophy and beyond.

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Author Profiles

Stephan Hartmann
Ludwig Maximilians Universität, München
Merdes Christoph J
Universität Erlangen-Nürnberg

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References found in this work

Bayesian Epistemology.Luc Bovens & Stephan Hartmann - 2003 - Oxford: Oxford University Press. Edited by Stephan Hartmann.
Signals: Evolution, Learning, and Information.Brian Skyrms - 2010 - Oxford, GB: Oxford University Press.
Bayesian Philosophy of Science.Jan Sprenger & Stephan Hartmann - 2019 - Oxford and New York: Oxford University Press.
Science in the age of computer simulation.Eric B. Winsberg - 2010 - Chicago: University of Chicago Press.

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