Foundations of Science

, Volume 18, Issue 4, pp 809–821 | Cite as

How Computational Models Predict the Behavior of Complex Systems

  • John SymonsEmail author
  • Fabio Boschetti


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 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Adams D. (2004) Usable knowledge in public policy. Australian Journal of Public Administration 63: 29–42CrossRefGoogle Scholar
  2. Aligica P. D. (2003) Prediction, explanation and the epistemology of future studies. Futures 35: 1027–1040CrossRefGoogle Scholar
  3. Allan, C., & Stankey, G. H. (2009a). Adaptive environmental management: A practitioner’s guide. Springer and CSIRO Publishing, Dordrecht, The Netherlands; Collingwood, Vic., Vol. xvi, p. 351Google Scholar
  4. Allan C., Stankey G. H. (2009b) Adaptive environmental management: A practitioners guide. CSIRO Publishing, Springer Canberra, New YorkCrossRefGoogle Scholar
  5. Ascher W. (1989) Beyond accuracy. International Journal of Forecasting 5: 469–484CrossRefGoogle Scholar
  6. Ascher W. (1993) The ambiguous nature of forecasts in project evaluation: Diagnosing the over-optimism of rate-of-return analysis. International Journal of Forecasting 9: 109–115CrossRefGoogle Scholar
  7. Beven K. (2006) A manifesto for the equifinality thesis. Journal of Hydrology 320: 18–36CrossRefGoogle Scholar
  8. Beven K. J. (2002) Towards a coherent philosophy for environmental modelling. Proceedings of the Royal Society A 458: 2465–2484CrossRefGoogle Scholar
  9. Boschetti F. (2010) Detecting behaviours in ecological models. Ecological Complexity 7: 76–85CrossRefGoogle Scholar
  10. Boschetti, F., Grigg, N. J., & Enting, I. (2010). Modelling = conditional prediction. Ecological Complexity, In Press, Corrected Proof.Google Scholar
  11. Brunner R. (1999) Predictions and policy decisions. Technological Forecasting and Social Change 62: 73–86CrossRefGoogle Scholar
  12. Butterworth D. S., Punt A. E. (1999) Experiences in the evaluation and implementation of management procedures. ICES Journal of Marine Science 56: 985–998CrossRefGoogle Scholar
  13. Cartwright N. (1983) How the laws of physics lie. Oxford University Press, OxfordCrossRefGoogle Scholar
  14. Chapman, K. J. (2011). A complexity-based approach to knowledge brokering and research uptake: Working to build adaptive institutions in Western Australia’s Ningaloo Region Edith Cowan University, Perth.Google Scholar
  15. Crutchfield J. P. (1994) The calculi of emergence: Computation, dynamics, and induction. Physica D 75: 11–54CrossRefGoogle Scholar
  16. de la Mare W. K. (1996) Some recent developments in the management of marine living resources. In: Floyd R. B., Shepherd A. W., De Barro P. J. (Eds.) Frontiers of population ecology. CSIRO Publishing, Melbourne, pp 599–616Google Scholar
  17. Doak D. F., Estes J. A., Halpern B. S., Jacob U., Lindberg D. R., Lovvorn J. et al (2008) Understanding and predicting ecological dynamics: are major surprises inevitable. Ecology 89: 952–961CrossRefGoogle Scholar
  18. Ellison C., Mahoney J., Crutchfield J. (2009) Prediction, retrodiction, and the amount of information stored in the present. Journal of Statistical Physics 136: 1005–1034CrossRefGoogle Scholar
  19. Frigg R., Reiss J. (2009) The philosophy of simulation: Hot new issues or same old stew?. Synthese 169(3): 593–613CrossRefGoogle Scholar
  20. Guala F. (2002) Models, simulations, and experiments. In: Magnani L., Nersessian N. (Eds.) Modelbased reasoning: Science, technology, values. Kluwer, New York, pp 59–74CrossRefGoogle Scholar
  21. Guala F. (2005) The methodology of experimental economics. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  22. Hintikka J. (1962) Knowledge and belief: An introduction to the logic of the two notions. Cornell University Press, CornellGoogle Scholar
  23. Hintikka J. (2007) Socratic epistemology: Explorations of knowledge-seeking by questioning. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  24. Holling C. (1978) Adaptive environmental assessment and management. International Institute for Applied Systems Research, OxfordGoogle Scholar
  25. Humphreys P. (1994) Extending ourselves: Computational science, empiricism, and scientific method. Oxford University Press, OxfordGoogle Scholar
  26. Israeli, N., & Goldenfeld, N. (2004). Computational Irreducibility and the Predictability of Complex Physical Systems. Physical Review Letters 92:074105-074101–074105-074104.Google Scholar
  27. Ivanović R. F., Freer J. E. (2009) Science versus politics: Truth and uncertainty in predictive modelling. Hydrological Processes 23: 2549–2554CrossRefGoogle Scholar
  28. Lee K. N. (1999) Appraising adaptive management. Conservation Ecology 3: 3Google Scholar
  29. Likens G. E. (2010) The role of science in decision making: Does evidence-based science drive environmental policy?. Frontiers in Ecology and the Environment 8: e1–e9CrossRefGoogle Scholar
  30. Meadows D. H. C. O. R. (1972) The Limits to growth; a report for the club of Rome’s project on the predicament of mankind. Universe books, New YorkGoogle Scholar
  31. Morgan, M. S., & Morrison, M. (1999). Models as mediators: perspectives on natural and social sciences. In M. S. Morgan (Ed.), Margaret Morrison. Cambridge, New York: Cambridge University PressGoogle Scholar
  32. Oreskes N. (2000) Why believe a computer? Models, measures, and meaning in the natural world. In: Schneiderman J. (Ed.) The Earth around us: Maintaining a livable planet. W.H. Freeman and Co, San Francisco, pp 70–82Google Scholar
  33. Oreskes N. (2001) Philosophical issues in model assessment. In: Anderson M. G., Bates P. D. (Eds.) Model validation: Perspectives in hydrological science. Wiley, London, pp 23–41Google Scholar
  34. Parker R. (1977) Understanding inverse theory. Annual Review of Earth and Planetary Sciences 5: 35–64CrossRefGoogle Scholar
  35. Parker W. (2009) Does matter really matter? Computer simulations, experiments, and materiality. Synthese 169(3): 483–496CrossRefGoogle Scholar
  36. Pielke R. A. (2003) The role of models in prediction for decision. In: Canham C. D., Cole J. J., Lauenroth W. K. (Eds.) Models in ecosystem science. Princeton University Press, Princeton and Oxford, pp 111–135Google Scholar
  37. Poli R. (2010) An introduction to the ontology of anticipation. Futures 42: 769–776CrossRefGoogle Scholar
  38. Putnam H. (1982) Why there isn’t a ready-made world. Synthese 51(2): 205–228CrossRefGoogle Scholar
  39. Rockström J., Steffen W., Noone K., Persson A., Chapin F. S., Lambin E. F. et al (2009) A safe operating space for humanity. Nature 461: 472–475CrossRefGoogle Scholar
  40. Rosen R. (1885) Anticipatory systems. Pergamon Press, OxfordGoogle Scholar
  41. Suchting W. A. (1967) Deductive explanation and prediction revisited. Philosophy of Science 34: 41–52CrossRefGoogle Scholar
  42. Takagi H. (2001) Interactive evolutionary computation as humanized computational intelligence technology. Computational Intelligence: Theory and Applications, Proceedings 2206: 1Google Scholar
  43. Tarantola A. (1987) Inverse problem theory. Elsevier, AmsterdamGoogle Scholar
  44. Walters C. (1986) Adaptive management of renewable resources. Macmillan, New YorkGoogle Scholar
  45. Walters C., Martell S. (2004) Fisheries ecology and management. Princeton University Press, Princeton, NJGoogle Scholar
  46. Winsberg E. (2010) Science in the age of computer simulation. University of Chicago Press, ChicagoCrossRefGoogle Scholar

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

Personalised recommendations