Polycratic hierarchies and networks: what simulation-modeling at the LHC can teach us about the epistemology of simulation.

Large scale experiments at CERN's Large Hadron Collider (LHC) rely heavily on computer simulations (CSs), a fact that has recently caught philosophers' attention. CSs obviously require appropriate modeling, and it is a common assumption among philosophers that the relevant models can be ordered into...

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Publicado en:Synthese Vol. 199; no. 1/2; pp. 445 - 481
Autores principales: Boge, Florian J., Zeitnitz, Christian
Formato: Artículo
Publicado: Springer Nature Dec2021
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Acceso en línea:Ver este registro en EBSCOhost
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        10.1007/s11229-020-02667-3
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        atl: Polycratic hierarchies and networks: what simulation-modeling at the LHC can teach us about the epistemology of simulation.
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        au:
          Boge, Florian J.
          Zeitnitz, Christian
        affil:
          Institute for Theoretical Particle Physics and Cosmology, RWTH Aachen University, Aachen, Germany
          Interdisciplinary Centre for Science and Technology Studies (IZWT), Bergische Universität Wuppertal, Gaußstr. 20, 42119, Wuppertal, Germany
          Physics Department, Bergische Universität Wuppertal, Wuppertal, Germany
      su:
        Model-based reasoning
        Large Hadron Collider
        Scientific discoveries
        Climatology
        Computer simulation
        New York (State)
        Berlin (Germany)
      sug:
        subj:
          New York (State)
          Berlin (Germany)
          Model-based reasoning
          Large Hadron Collider
          Scientific discoveries
          Climatology
          Computer simulation
      keyword:
        Hierarchy of models
        High energy physics
        Phenomenological models
      ab: Large scale experiments at CERN's Large Hadron Collider (LHC) rely heavily on computer simulations (CSs), a fact that has recently caught philosophers' attention. CSs obviously require appropriate modeling, and it is a common assumption among philosophers that the relevant models can be ordered into hierarchical structures. Focusing on LHC's ATLAS experiment, we will establish three central results here: (a) with some distinct modifications, individual components of ATLAS' overall simulation infrastructure can be ordered into hierarchical structures. Hence, to a good degree of approximation, hierarchical accounts remain valid at least as descriptive accounts of initial modeling steps. (b) In order to perform the epistemic function Winsberg (in Magnani L, Nersessian N, Thagard P (eds) Model-based reasoning in scientific discovery. Kluwer Academic/Plenum Publishers, New York, pp 255–269, 1999) assigns to models in simulation—generate knowledge through a sequence of skillful but non-deductive transformations—ATLAS' simulation models have to be considered part of a network rather than a hierarchy, in turn making the associated simulation modeling messy rather than motley. Deriving knowledge-claims from this 'mess' requires two sources of justification: (i) holistic validation (also Lenhard and Winsberg in Stud Hist Philos Sci Part B Stud Hist Philos Modern Phys 41(3):253–262, 2010; in Carrier M, Nordmann A (eds) Science in the context of application. Springer, Berlin, pp 115–130, 2011), and (ii) model coherence. As it turns out, (c) the degree of model coherence sets HEP apart from other messy, simulation-intensive disciplines such as climate science, and the reasons for this are to be sought in the historical, empirical and theoretical foundations of the respective discipline.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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