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Models, Explanation, Representation, and the Philosophy of Computer Simulations

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Philosophy of Computing

Part of the book series: Philosophical Studies Series ((PSSP,volume 143))

Abstract

The philosophical study of computer simulations has been largely subordinated to the analysis of sets of equations and their implementation on the computer. What has received less attention, however, is whether simulation models can be taken as units of analysis in their own right. Here I present my own experimental work investigating this issue. This article explores the capacity of programming languages to represent target systems and submits that, in a number of cases, the representation of simulation models differs in non-trivial ways from sets of equations. If my claim is correct, then a few important methodological and epistemological concerns emerge that need our attention. This article finishes by briefly addressing some implications for the philosophy of computer simulation.

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Notes

  1. 1.

    Mathematical models consist of a set of equations that describe certain aspects of physical reality, broadly conceived. Mathematical modeling is the specific practices attached to such models.

  2. 2.

    For an historical analysis of the definitions of computer simulations and their philosophical significance, see Durán (2019).

  3. 3.

    Exploration here should be interpreted as mathematically finding the space of solutions to the dynamic model. This explains why all five functions of simulations described by Hartmann heavily resemble finding solutions to the underlying mathematical model (Hartmann 1996, 84–85).

  4. 4.

    Winsberg has written extensively about computer simulations, and among his essays we can find more comprehensive analyses of simulations that include those that do not depend on well-defined set of equations.

  5. 5.

    In Parker’s defense, an exact characterization of a simulation model might not be relevant for her purposes. It is still striking to see, nonetheless, that philosophers still pass equations for simulations.

  6. 6.

    For a short review of Lenhard’s book, see Durán (2020b).

  7. 7.

    As a reviewer correctly pointed out, running a computer simulation regarding the possible path of a hurricane, for instance, requires inter alia information about the weather and its patterns.

  8. 8.

    Fetzer has offered further arguments as to how computer models can be singled out (Fetzer 1999).

  9. 9.

    Admittedly, nested conditionals are not the only option for representing the relations between nodes. In fact, they might not even be the best option. For instance, the pyramid of doom is a common problem that arises when a program uses too many levels of nested conditionals—other syntactic structures also apply, like nested indentation to control access to a function. See Accessed November 2021.

  10. 10.

    Note that not only is the representation of the target system affected by instrumenting a core simulation, but also the feasibility of the computation as a whole and the accuracy of the output obtained thereafter.

  11. 11.

    This simulation is also presented and discussed in more depth in [hidden].

  12. 12.

    Of course, mathematics is a discipline in its own right, in the same sense that computer simulations is also a field in its own right within computer science and engineering.

  13. 13.

    Historians of science and technology have, indeed, discussed these issues (e.g., De Mol and Primiero 2014; Haigh et al. 2016; De Mol and Bullynck 2018; De Mol 2019). Here, I am rather thinking of the philosopher interested in the social aspects of scientific practice.

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Acknowledgements

I would like to thank the editors of the proceeding, Nancy Abigail Nuñez and Björn Lundgren, for their patience and encouragement. Also, thanks go to Giuseppe Primiero for our discussions on the nature of algorithms and computer programs. Some of the ideas discussed with him ended up in this article. All mistakes are of course of my authorship. I would also like to thank Fondo para la Investigación Científica y Tecnológica (FONCYT - Argentina) - PICT 2016-1524, for their financial support. Finally, I would like to thank the section Values, Technology and Innovation, Faculty of Technology, Policy and Management at the Delft University of Technology for their unrivaled support.

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  1. TU Delft, Faculty of Technology, Policy and Management, Delft, The Netherlands

    Juan Manuel Durán

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  1. Juan Manuel Durán
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Correspondence to Juan Manuel Durán .

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  1. The Department of Philosophy and Religious Studies, Utrecht University, Utrecht, The Netherlands

    Björn Lundgren

  2. Institute of Philosophy, Czech Academy of Sciences, Prague, Czech Republic

    Nancy Abigail Nuñez Hernández

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Durán, J.M. (2022). Models, Explanation, Representation, and the Philosophy of Computer Simulations. In: Lundgren, B., Nuñez Hernández, N.A. (eds) Philosophy of Computing. Philosophical Studies Series, vol 143. Springer, Cham. https://doi.org/10.1007/978-3-030-75267-5_9

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