How Computational Models Predict the Behavior of Complex Systems

Foundations of Science 18 (4):809-821 (2013)
Authors
John Symons
University of Kansas
Abstract
In this paper, we argue for the centrality of prediction in the use of computational models in science. We focus on the consequences of the irreversibility of computational models and on the conditional or ceteris paribus, nature of the kinds of their predictions. By irreversibility, we mean the fact that computational models can generally arrive at the same state via many possible sequences of previous states. Thus, while in the natural world, it is generally assumed that physical states have a unique history, representations of those states in a computational model will usually be compatible with more than one possible history in the model. We describe some of the challenges involved in prediction and retrodiction in computational models while arguing that prediction is an essential feature of non-arbitrary decision making. Furthermore, we contend that the non-predictive virtues of computational models are dependent to a significant degree on the predictive success of the models in question.
Keywords Computational models  Prediction  Complexity
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DOI 10.1007/s10699-012-9307-6
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References found in this work BETA

How the Laws of Physics Lie.Nancy Cartwright - 1983 - Oxford University Press.
Knowledge and Belief.Jaakko Hintikka - 1962 - Ithaca: Cornell University Press.

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Citations of this work BETA

Software Intensive Science.John Symons & Jack Horner - 2014 - Philosophy and Technology 27 (3):461-477.

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