On the epistemological analysis of modeling and computational error in the mathematical sciences.

Interest in the computational aspects of modeling has been steadily growing in philosophy of science. This paper aims to advance the discussion by articulating the way in which modeling and computational errors are related and by explaining the significance of error management strategies for the rat...

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Publicado en:Synthese Vol. 191; no. 7; pp. 1451 - 1468
Autores principales: Fillion, Nicolas, Corless, Robert
Formato: Artículo
Publicado: Springer Nature May2014
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: On the epistemological analysis of modeling and computational error in the mathematical sciences.
      aug:
        au:
          Fillion, Nicolas
          Corless, Robert
        affil:
          Department of Statistics and Actuarial Sciences, Joseph L. Rotman Institute of Philosophy, University of Western Ontario, London Canada
          Department of Applied Mathematics, Joseph L. Rotman Institute of Philosophy, University of Western Ontario, London Canada
      su:
        Theory of knowledge
        Mathematical models
        Error analysis in mathematics
        Numerical analysis
        Normativity (Ethics)
        Perturbation theory
      sug:
        subj:
          Theory of knowledge
          Mathematical models
          Error analysis in mathematics
          Numerical analysis
          Normativity (Ethics)
          Perturbation theory
      keyword:
        Backward error analysis
        Computational error
        Mathematical modeling
        Modeling error
        Rational reconstruction
      ab: Interest in the computational aspects of modeling has been steadily growing in philosophy of science. This paper aims to advance the discussion by articulating the way in which modeling and computational errors are related and by explaining the significance of error management strategies for the rational reconstruction of scientific practice. To this end, we first characterize the role and nature of modeling error in relation to a recipe for model construction known as Euler's recipe. We then describe a general model that allows us to assess the quality of numerical solutions in terms of measures of computational errors that are completely interpretable in terms of modeling error. Finally, we emphasize that this type of error analysis involves forms of perturbation analysis that go beyond the basic model-theoretical and statistical/probabilistic tools typically used to characterize the scientific method; this demands that we revise and complement our reconstructive toolbox in a way that can affect our normative image of science.
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    language: English
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