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Complexity and Scientific Modelling

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Abstract

It is argued that complexity is not attributable directly to systems or processes but rather to the descriptions of their `best' models, to reflect their difficulty. Thus it is relative to the modelling language and type of difficulty. This approach to complexity is situated in a model of modelling. Such an approach makes sense of a number of aspects of scientific modelling: complexity is not situated between order and disorder; noise can be explicated by approaches to excess modelling error; and simplicity is not truth indicative but a useful heuristic when models are produced by a being with a tendency to elaborate in the face of error.

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REFERENCES

  • Badii, R. and Politi, A.: 1997, Complexity: Hierarchical Structures and Scaling in Physics. Cambridge: Cambridge University Press.

    Google Scholar 

  • Chaitin, G.J.: 1966, On the Length of Programs for Computing Finite Binary Sequences, Journal of the Association of Computing Machinery13: 547–569.

    Google Scholar 

  • Crutchfield, J.P.: 1994, The Calculi of Emergence: Computation, Dynamics and Induction, Physica D75: 11–54.

    Google Scholar 

  • Edmonds, B.: 1999, What is Complexity?: the Philosophy of Complexity per se with application to some examples in evolution. In F. Heylighen and D. Aerts (eds.), The Evolution of Complexity. Dordrecht: Kluwer.

    Google Scholar 

  • Grassberger, P.: 1986, Towards a Quantitative Theory of Self-Generated Complexity, International Journal of Theoretical Physics25(9): 907–938.

    Google Scholar 

  • Kauffman, S.A.: 1993, The Origins of Order. New York: Oxford University Press.

    Google Scholar 

  • Kolmogorov, A.N.: 1965, Three Approaches to the Quantitative Definition of Information, Problems of Information Transmission1: 1–17.

    Google Scholar 

  • Murphy, P.M. and Pazzani, M.J.: 1994, Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction, Journal of Artificial Intelligence Research1: 257–275.

    Google Scholar 

  • Pearl, J.P.: 1978, On the Connection Between the Complexity and Credibility of InferredModels, International Journal of General Systems4: 255–264.

    Google Scholar 

  • Popper, K.R.: 1968, Logic of Scientific Discovery. London: Hutchinson.

    Google Scholar 

  • Quine, W.V.O.: 1960, Simple Theories of a Complex World. In Quine, W.V.O. (ed.), The Ways of Paradox. New York: Random House, 242–246.

    Google Scholar 

  • Rissanen, J.: 1990, Complexity of Models. In Zurek, W.H. (ed.), Complexity, Entropy and the Physics of Information. Redwood City, California: Addison-Wesley, 117–125.

    Google Scholar 

  • Sober, E.: 1975, Simplicity. Oxford: Clarendon Press.

    Google Scholar 

  • Solomonoff, R.J.: 1964, A Formal Theory of Inductive Inference, Information and Control7: 1–22, 224–254.

    Google Scholar 

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Edmonds, B. Complexity and Scientific Modelling. Foundations of Science 5, 379–390 (2000). https://doi.org/10.1023/A:1011383422394

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  • DOI: https://doi.org/10.1023/A:1011383422394

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