Varying the Explanatory Span: Scientific Explanation for Computer Simulations

ABSTRACT

This article aims to develop a new account of scientific explanation for computer simulations. To this end, two questions are answered: what is the explanatory relation for computer simulations? And what kind of epistemic gain should be expected? For several reasons tailored to the benefits and needs of computer simulations, these questions are better answered within the unificationist model of scientific explanation. Unlike previous efforts in the literature, I submit that the explanatory relation is between the simulation model and the results of the simulation. I also argue that our epistemic gain goes beyond the unificationist account, encompassing a practical dimension as well.

Acknowledgements

The author thanks Paul Humphreys and Claus Beisbart for useful commentaries on earlier versions of this article. The author is also grateful to Raphael van Riel for providing a great atmosphere for discussions during his fellowship in the research group ‘A Study in Explanatory Power’, Universität Duisburg-Essen. He would also like to thank three anonymous referees and the editor of this journal for their critical remarks that greatly improved the argument. Finally, this article is in great debt to the keen philosophical eye of Manuel Barrantes, Itatí Branca, Andrés Ilcic, and Nico Formanek.

Notes

1 Hartmann (1996, 83) famously argued that computer simulations are methods for solving in the computer the set of the equations of dynamic models.

2 To Krohs (2008, 278), ‘theoretical models’ are mathematical models that describe a given empirical target system.

3 Let it be noted that Krohs refers to structures and mechanisms indistinguishably. This is so because he frames computer simulations within the mechanicistic theory (Krohs 2008, 282).

4 In fact, a simulation model includes a host of non-mathematical structures, such as loops, conditionals, subroutines, and other terminology that conceptually distance the simulation models from mathematical models.

5 This is not to say that computer simulations are unificatory systems. For such a claim, we also need to specify in what respects they unify. Rendering a host of simulated phenomena—some of which are clearly unknown—is a core feature of computer simulations that squares well with the unificationist. A future task is to show in what respects there is unification in computer simulations, including models that are not straightforwardly unificatory.

6 A reconstruction of computer simulations as arguments is given by Beisbart (2012). Unlike him, I am not claiming that all computer simulations are arguments, but rather that some aspects of the simulation model can be reconstructed for explanatory purposes.

7 A full description of the variables, data types, and subroutines can be found in Woolfson and Pert (1999b).

8 For an analysis of ‘brute’ and ‘independent’ facts, see Barnes (1994) and Fahrbach (2005)

9 Barnes (1992) has rightly criticized theories of scientific explanation for failing to provide a full-fledged account of what understanding consists in, and of how it is produced by scientific explanations. Here, I am only concerned with showing how understanding of results of computer simulations is realized within the unificationist account. It is therefore not within my interests to fully flesh out how such understanding is carried out. To this end, however, the ‘contextual approach to scientific understanding’ as elaborated by de Regt and Dieks (2005), and the recent work on ‘how-possible’ and ‘how-actually’ understanding by Reutlinger, Hangleiter, and Hartmann (2017) could shed some light on the issue.

Additional information

Funding

The author thanks Universidad Nacional de Córdoba and National Scientific and Technical Research Council (Argentina), and Volkswagen Stiftung (Germany) (grant number 88316) for their financial support.