David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Synthese 190 (12):2367-2390 (2013)
‘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 analysis of simulation developed in previous work to provide an evaluative account of the variety of ways in which simulations do fail. We expand the structural analysis in terms of the relationship between a simulation and its real-world target emphasizing the important role of aspects intended to correspond and also those specifically intended not to correspond to reality. The result is an outline both of the ways in which simulations can fail and the scientific importance of those various forms of failure.
|Keywords||Computer simulation Game theory Model Simulation Scientific methodology|
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Judea Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
James Woodward (2003). Making Things Happen: A Theory of Causal Explanation. Oxford University Press.
Nancy Cartwright (1983). How the Laws of Physics Lie. Oxford University Press.
Kendall L. Walton (1990). Mimesis as Make-Believe: On the Foundations of the Representational Arts. Harvard University Press.
Bas C. Van Fraassen (1980). The Scientific Image. Oxford University Press.
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