Abstract
The article analyzes the notions of analogue and digital simulation as found in scientific and philosophical literature. The purpose is to distinguish computer simulations from laboratory experimentation on several grounds, including ontological, epistemological, pragmatic/intentional, and methodological. To this end, it argues that analogue simulations are best understood as part of the laboratory instrumentarium, whereas digital simulations are computational methods for solving a simulation model. The article ends by showing how the analogue-digital distinction is at the heart of contemporary debates on the epistemological and methodological power of computer simulations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Andreas Kaminski pointed out that the notion of abstraction is present in laboratory practice as well as in scientific modeling and theorizing. In this respect, it should not be understood that laboratory practice excludes instances of abstraction, but rather that they are more material—in the straightforward sense of manipulating material products—than computer simulations. I will discuss these ideas in more detail in Sect. 3.
- 2.
Let us note that it is not enough to distinguish analogue simulations from digital simulations by saying that the latter, and not the former, are models implemented on the computer. Although correct in itself, this distinction does not provide any useful insight into the characteristics of analogue simulations nor reasons for distinguising them from computer simulations. Grasping this insight is essential for understanding the epistemological, methodological, and pragmatic value of each kind of simulation.
- 3.
I am significantly simplifying Goodman’s ideas on analogue and digital. A further distinction is that differentiated systems could be non-dense, and therefore analogue and not digital. For examples on these cases, see Lewis (1971).
- 4.
Pylyshyn is neither interested in belaboring the notions of analogue and computational process, nor in asserting grounds for a distinction. Rather, he is interested in showing that concrete features of some systems (e.g., biological, technological, etc.) are more appropriately described at the symbolic level, whereas other features are best served by the vocabulary of physics.
- 5.
The idea of ‘parallel causal-structures isomorphic to the phenomenon’ is rather difficult to pin down. For a closer look, please refer to Trenholme (1994, p. 118). I take it as a way to describe two systems sharing the same causal relations. I base my interpretation on the author’s comment in the appendix: “The simulated system causally affects the simulating system through sensory input thereby initiating a simulation run whose causal structure parallels that of the run being undergone by the simulated system” (Ibid., 128). Also, the introduction of ‘isomorphism’ as the relation of representation can be quite problematic. On this last point, see, for instance, Suárez (2003).
- 6.
Unfortunately, Trenholme does not give more details on the notion of ‘intentional concepts.’ Now, given that this term belongs to the terminological canon of cognitive sciences, and given that Trenholme is following Pylyshyn in these respects, it seems appropriate to suggest that a definition could be found in Pylyshyn’s work. In this respect, Pylyshyn talks about several concepts that could be related, such as intentional terms (Pylyshyn 1989, p. 5), intentional explanation (Ibid., 212), intentional objects (Ibid., 262), and intentional descriptions (Ibid., 20).
- 7.
Trenholme uses the notions of symbolic process and symbolic computation interchangeably (Trenholme 1994, p. 118).
- 8.
Let us note that these working conceptualizations mirror many of the definitions already found in the specialized literature (for instance, Winsberg 2015). In here, I am only interested in the analogue-digital distinction as means for grounding philosophical studies on computer simulations and laboratory experimentation.
- 9.
- 10.
In that article, I urged for a change in the evaluation of the epistemological and methodological assessment of computer simulations. For my position on the issue, see Durán (2013b).
References
Baird, Davis. 2004. Thing Knowledge: A Philosophy of Scientific Instruments. Berkeley: University of California Press.
Boukharta, Lars, Hugo Gutiérrez-de-Terán, and Johan Åqvist. 2014. Computational Prediction of Alanine Scanning and Ligand Binding Energetics in G-Protein Coupled Receptors. PLOS Computational Biology 10 (4): 6146–6156.
Comrie, Leslie John. 1932. The Application of the Hollerith Tabulating Machine to Brown’s Tables of the Moon. Monthly Notices of the Royal Astronomical Society 92 (7): 694–707.
Durán, Juan M. 2013a. The Use of the ‘Materiality Argument’ in the Literature on Computer Simulations. In Computer Simulations and the Changing Face of Scientific Experimentation, ed. Juan M. Durán and Eckhart Arnold, 76–98. Newcastle: Cambridge Scholars Publishing.
Durán, Juan M. 2013b. Explaining Simulated Phenomena: A Defense of the Epistemic Power of Computer Simulations. Dissertation, Universität Stuttgart. http://elib.uni-stuttgart.de/opus/volltexte/2014/9265/.
Giere, Ronald N. 2009. Is Computer Simulation Changing the Face of Experimentation? Philosophical Studies 143 (1): 59–62.
Goodman, Nelson. 1968. Languages of Art. An Approach to a Theory of Symbols. Indianapolis: The Bobbs-Merrill Company.
Guala, Francesco. 2002. Models, Simulations, and Experiments. In Model-Based Reasoning: Science, Technology, Values, ed. Lorenzo Magnani and Nancy J. Nersessian, 59–74. New York: Kluwer.
Harré, Rom. 2003. The Materiality of Instruments in a Metaphysics for Experiments. In The Philosophy of Scientific Experimentation, ed. Hans Radder, 19–38. Pittsburgh: University of Pittsburgh Press.
Kroes, Peter. 2003. Physics, Experiments, and the Concept of Nature. In The Philosophy of Scientific Experimentation, ed. Hans Radder, 68–86. Pittsburgh: University of Pittsburgh Press.
Lewis, David. 1971. Analog and Digital. Noûs 5 (3): 321–327.
Morgan, Mary S. 2003. Experiments without Material Intervention. In The Philosophy of Scientific Experimentation, ed. Hans Radder, 216–235. Pittsburgh: University of Pittsburgh Press.
Morgan, Mary S. 2005. Experiments versus Models: New Phenomena, Inference and Surprise. Journal of Economic Methodology 12 (2): 317–329.
Morrison, Margaret. 2009. Models, Measurement and Computer Simulation: The Changing Face of Experimentation. Philosophical Studies 143 (1): 33–57.
Parker, Wendy S. 2009. Does Matter Really Matter? Computer Simulations, Experiments, and Materiality. Synthese 169 (3): 483–496.
Pylyshyn, Zenon W. 1989. Computation and Cognition: Toward a Foundation for Cognitive Science. Cambridge, MA: MIT Press.
Schelling, Thomas C. 1971. Dynamic Models of Segregation. The Journal of Mathematical Sociology 1 (2): 143–186.
Suárez, Mauricio. 2003. Scientific Representation: Against Similarity and Isomorphism. International Studies in the Philosophy of Science 17 (3): 225–244.
Trenholme, Russell. 1994. Analog Simulation. Philosophy of Science 61 (1): 115–131.
Winsberg, Eric. 2009. A Tale of Two Methods. Synthese 169 (3): 575–592.
Winsberg, Eric. 2015. Computer Simulations in Science. In Stanford Encyclopedia of Philosophy (Summer 2015 Edition), ed. Edward N. Zalta. plato.stanford.edu/archives/sum2015/entries/simulations-science/. Cited June 2016.
Acknowledgments
Thanks go to Andreas Kaminski for comments on a previous version of the article.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Durán, J.M. (2017). Varieties of Simulations: From the Analogue to the Digital. In: Resch, M., Kaminski, A., Gehring, P. (eds) The Science and Art of Simulation I . Springer, Cham. https://doi.org/10.1007/978-3-319-55762-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-55762-5_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55761-8
Online ISBN: 978-3-319-55762-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)