Foundations of Science

, Volume 18, Issue 4, pp 809–821

How Computational Models Predict the Behavior of Complex Systems


DOI: 10.1007/s10699-012-9307-6

Cite this article as:
Symons, J. & Boschetti, F. Found Sci (2013) 18: 809. doi:10.1007/s10699-012-9307-6


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.


Computational models Prediction Complexity 

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Department of PhilosophyUniversity of KansasLawrenceUSA
  2. 2.CSIRO Marine and Atmospheric ResearchMelbourneAustralia
  3. 3.School of Earth and Geographical SciencesThe University of Western AustraliaPerthAustralia

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