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Boon and Bane: On the Role of Adjustable Parameters in Simulation Models

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Mathematics as a Tool

Part of the book series: Boston Studies in the Philosophy and History of Science ((BSPS,volume 327))

Abstract

We claim that adjustable parameters play a crucial role in building and applying simulation models. We analyze that role and illustrate our findings using examples from equations of state in thermodynamics. In building simulation models, two types of experiments, namely, simulation and classical experiments, interact in a feedback loop, in which model parameters are adjusted. A critical discussion of how adjustable parameters function shows that they are boon and bane of simulation. They help to enlarge the scope of simulation far beyond what can be determined by theoretical knowledge, but at the same time undercut the epistemic value of simulation models.

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Notes

  1. 1.

    Humphreys (2004) contributed the first monograph to the field. Parker (2013) or Winsberg (2014) provide valid overview articles that include many references.

  2. 2.

    A variety of good motivations are given in, for instance, Axelrod (1997), Barberousse et al. (2009), Dowling (1999), Galison (1996), Humphreys (1994), Hughes (1999), Keller (2003), Morgan (2003), Morrison (2009), Rohrlich (1991), Winsberg (2003).

  3. 3.

    If you want to avoid talking about experiment in this context, these properties can be known only by actually conducting simulations. Mark Bedau (2011) has highlighted properties that can be known only by actually conducting the computational process of a simulation and has aptly called them “weakly emergent.”

  4. 4.

    In this respect, our work elaborates the notion of “exploratory cooperation” in simulation modeling, put forward in Lenard (2007).

  5. 5.

    Our claim is open to many guises of how “real” is spelled out in philosophical terms. People concerned with issues of realism might want to resort to “target system,” which is a less laden term (though it does not solve any of the questions).

  6. 6.

    Addressing the intricate questions about correspondence and representation, we refer to Weisberg’s recent work (2013), which offers a taxonomy for the relationships between model and target system.

  7. 7.

    We will not discuss classes of simulation models like artificial neural networks. Arguably, they have a very generic structure and extraordinary adaptability. Essentially, they are a proposal to parameterize the entire behavior (if in an opaque, or implicit way).

  8. 8.

    The coincidence of computer modeling, exploratory setting of parameters, and proliferation of models has been discussed by Lenhard (2016) in the context of computational chemistry.

  9. 9.

    Actually, even the objects of mathematics kept ready surprises. The development of the discipline has been accompanied by an extraordinary – and often unexpected – malleability of objects.

  10. 10.

    Here, our paper ties in with recent accounts of how simulation influences the standard notion of experiment and measurement (cf. Morrison 2009, 2014, Tal 2013).

  11. 11.

    Cf. Oberkampf and Roy (2010, section 13.5.1) for a systematic proposal of how parameters influence the validation of simulations from an engineering perspective.

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Acknowledgments

H.H. gratefully acknowledges support of this work by the Reinhart Koselleck program of Deutsche Forschungsgemeinschaft.

J.L. gratefully acknowledges support of this work by DFG SPP 1689.

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Authors and Affiliations

  1. Laboratory of Engineering Thermodynamics (LTD), University of Kaiserslautern, Kaiserslautern, Germany

    Hans Hasse

  2. Department of Philosophy, Bielefeld University, Bielefeld, Germany

    Johannes Lenhard

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  1. Hans Hasse
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  2. Johannes Lenhard
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Correspondence to Johannes Lenhard .

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Editors and Affiliations

  1. Department of Philosophy, Bielefeld University, Bielefeld, Germany

    Johannes Lenhard

  2. Department of Philosophy, Bielefeld University, Bielefeld, Germany

    Martin Carrier

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Hasse, H., Lenhard, J. (2017). Boon and Bane: On the Role of Adjustable Parameters in Simulation Models. In: Lenhard, J., Carrier, M. (eds) Mathematics as a Tool. Boston Studies in the Philosophy and History of Science, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-54469-4_6

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