Robust Simulations.

As scientists begin to study increasingly complex questions, many have turned to computer simulation to assist in their inquiry. This methodology has been challenged by both analytic modelers and experimentalists. A primary objection of analytic modelers is that simulations are simply too complicate...

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Publicado en:Philosophy of Science Vol. 74; no. 5; pp. 873 - 884
Autor principal: Muldoon, Ryan
Formato: Artículo
Publicado: Cambridge University Press Dec2007
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        au: Muldoon, Ryan
        affil: Department of Philosophy, University of Pennsylvania, 433 Logan Hall, 249 S. 36th Street, Philadelphia, PA, 19104-6304
      su:
        Computer simulation
        Methodology
        Differential equations
        Data modeling
        Simulation methods & models
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        subj:
          Computer simulation
          Methodology
          Differential equations
          Data modeling
          Simulation methods & models
      ab: As scientists begin to study increasingly complex questions, many have turned to computer simulation to assist in their inquiry. This methodology has been challenged by both analytic modelers and experimentalists. A primary objection of analytic modelers is that simulations are simply too complicated to perform model verification. From the experimentalist perspective it is that there is no means to demonstrate the reality of simulation. The aim of this paper is to consider objections from both of these perspectives, and to argue that a proper understanding and application of robustness analysis is able to resolve them.
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    language: English
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