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  1. How simulations fail.Patrick Grim, Robert Rosenberger, Adam Rosenfeld, Brian Anderson & Robb E. Eason - 2011 - Synthese 190 (12):2367-2390.
    ‘The problem with simulations is that they are doomed to succeed.’ So runs a common criticism of simulations—that they can be used to ‘prove’ anything and are thus of little or no scientific value. While this particular objection represents a minority view, especially among those who work with simulations in a scientific context, it raises a difficult question: what standards should we use to differentiate a simulation that fails from one that succeeds? In this paper we build on a structural (...)
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  • Modeling Epistemology: Examples and Analysis in Computational Philosophy of Science.Patrick Grim - 2019 - In A. Del Barrio, C. J. Lynch, F. J. Barros & X. Hu (eds.), IEEE SpringSim Proceedings 2019. IEEE. pp. 1-12.
    What structure of scientific communication and cooperation, between what kinds of investigators, is best positioned to lead us to the truth? Against an outline of standard philosophical characteristics and a recent turn to social epistemology, this paper surveys highlights within two strands of computational philosophy of science that attempt to work toward an answer to this question. Both strands emerge from abstract rational choice theory and the analytic tradition in philosophy of science rather than postmodern sociology of science. The first (...)
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  • Modeling Information.Patrick Grim - 2016 - In Luciano Floridi (ed.), The Routledge Handbook of Philosophy of Information. Routledge. pp. 137-152.
    The topics of modeling and information come together in at least two ways. Computational modeling and simulation play an increasingly important role in science, across disciplines from mathematics through physics to economics and political science. The philosophical questions at issue are questions as to what modeling and simulation are adding, altering, or amplifying in terms of scientific information. What changes with regard to information acquisition, theoretical development, or empirical confirmation with contemporary tools of computational modeling? In this sense the title (...)
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  • Evolutionary Economics, Responsible Innovation and Demand: Making a Case for the Role of Consumers.Michael P. Schlaile, Matthias Mueller, Michael Schramm & Andreas Pyka - 2018 - Philosophy of Management 17 (1):7-39.
    This paper contributes to the (re-)conceptualisation of responsible innovation by proposing an evolutionary economic approach that focuses on the role of consumers in the innovation process. After a discussion of the philosophical foundations and ethical implications of this approach, which bears an explanatory potential that has not been adequately considered in previous discussions of responsible innovation, we present a first step towards capturing the important but often neglected role of consumers in innovation processes (including responsible innovation): We propose an agent-based (...)
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  • Simulation and the Problem of Simplification: Between Scylla and Charybdis?Gerhard König - 2013 - Philosophy and Technology 26 (1):81-91.
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