Abstract
Computers have transformed science and help to extend the boundaries of human knowledge. However, does the validation and diffusion of results of computational inquiries and computer simulations call for a novel epistemological analysis? I discuss how the notion of novelty should be cashed out to investigate this issue meaningfully and argue that a consequentialist framework similar to the one used by Goldman to develop social epistemology can be helpful at this point. I highlight computational, mathematical, representational, and social stages on which the validity of simulation-based belief-generating processes hinges, and emphasize that their epistemic impact depends on the scientific practices that scientists adopt at these different stages. I further argue that epistemologists cannot ignore these partially novel issues and conclude that the epistemology of computational inquiries needs to go beyond that of models and scientific representations and has cognitive, social, and in the present case computational, dimensions.
The drunkard’s search or streetlight fallacy corresponds to a type of situation where people search at the easiest site, even if what they are searching for is unlikely to be there. Typically, the drunkard searches for her keys under a streetlight even if they were lost somewhere else.
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Notes
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Similarly, knowing the main effects of drugs may give lay people the illusion that they can safely decide whether they should take them when they are sick.
References
Andersen, H. (2014). Epistemic dependence in contemporary science: Practices and malpractices. In L. Soler, S. Zwart, M. Lynch, & V. Israel-Jost (Eds.), Commentary on epistemic dependence in contemporary science: Practices and malpractices by Hanne Andersen (pp. 161–173). Routledge Studies in the Philosophy of Science, London: Routledge.
Baker, M. (2016). 1,500 Scientists lift the lid on reproducibility. Nature News, 533(7604), 452.
Barberousse, A., Franceschelli, S., & Imbert C. (2009). Computer simulations as experiments. Synthese, 169(3), 557–574.
Barberousse, A., & Imbert, C. (2013). New mathematics for old physics: The case of lattice fluids. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 44(3), 231–241.
Barberousse, A., & Imbert, C. (2014). Recurring models and sensitivity to computational constraints. Sherwood J. B. Sugden (Ed.), Monist, 97(3), 259–279.
Beisbart, C. (2018). Are computer simulations experiments? And if not, how are they related to each other? European Journal for Philosophy of Science, 1–34.
Bloor, D. (1976). Knowledge and social imagery (Routledge Direct Editions). London, Boston: Routledge & K. Paul.
Collberg, C., & Proebsting, T. A. (2016). Repeatability in computer systems research. Communications of the ACM, 59(3), 62–69.
Collins, H. M. (1985). Changing order: Replication and induction in scientific practice. London, Beverly Hills: Sage Publications.
De Matteis, A., Pagnutti, S. (1988). Parallelization of random number generators and long-range correlations. Numerische Mathematik, 53(5), 595–608.
DeMillo, R. A., Lipton, R. J., & Sayward, F. G. (1978). Hints on test data selection: Help for the practicing programmer. Computer, 11(4), 34–41.
DeMillo, R. A., Lipton, R. J., & Perlis, A. J. (1979). Social processes and proofs of theorems and programs. Communications of the ACM, 22(5), 271–280.
Demmel, J., & Nguyen, H. D. (2013). Numerical reproducibility and accuracy at exascale. In 2013 IEEE 21st Symposium on Computer Arithmetic (pp. 235–237).
Dijkstra, E. W. (1978). On a political pamphlet from the middle ages. ACM SIGSOFT software engineering notes, 3(2), 14–16.
Dijkstra, E. W. (1972). The humble programmer. Communications of the ACM, 15(10), 859–866.
El Skaf, R., & Imbert, C. (2013). Unfolding in the empirical sciences: Experiments, thought experiments and computer simulations. Synthese, 190(16), 3451–3474.
Fetzer, J. H. (1988). Program verification: The very idea. Communications of the ACM, 31(9), 1048–1063.
Fillion, N., & Corless, R. M. (2014). On the epistemological analysis of modeling and computational error in the mathematical sciences. Synthese, 191(7), 1451–1467.
Fomel, S., & Claerbout, J. F. (2009). Guest editors’ introduction: Reproducible research. Computing in Science Engineering, 11(1), 5–7.
Fresco, N., & Primiero, G. (2013). Miscomputation. Philosophy & Technology, 26(3), 253–272.
Frigg, R., & Reiss, J. (2009). The Philosophy of simulation: Hot new issues or same old stew? Synthese, 169(3), 593–613.
Frigg, R., & Hartmann, S. (2017). Models in Science. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy, Spring 2017. Metaphysics Research Lab, Stanford University, https://plato.stanford.edu/archives/spr2017/entries/models-science/.
Goldman, A. I. (1999). Knowledge in a social world. Oxford, New York: Clarendon Press, Oxford University Press.
Hardwig, J. (1985). Epistemic dependence. Journal of Philosophy, 82(7), 335–349.
Hastie, R., Penrod, S., & Pennington, N. (1983). Inside the jury. Cambridge, Massachusetts, United States: Harvard University Press.
Heinrich, J. (2004). Detecting a bad random number generator. CDF/MEMO/STATISTICS/PUBLIC/6850. University of Pennsylvania. https://www-cdf.fnal.gov/physics/statistics/notes/cdf6850_badrand.pdf.
Hellekalek, P. (1998). Don’t trust parallel Monte Carlo. In Proceedings Parallel and Distributed Simulation Conference (pp. 82–89), Alberta, Canada.
Hill, D. R. C. (2015). Parallel random numbers, simulation, and reproducible research. Computing in Science Engineering, 17(4), 66–71.
Humphreys, P. (2004). Extending ourselves: Computational science, empiricism, and scientific method. Oxford University Press.
Humphreys, P. (2009). The philosophical novelty of computer simulation methods. Synthese, 169(3), 615–626.
Imbert, C. (2014). The identification and prevention of bad practices and malpractices in science. In L. Soler, S. Zwart, M. Lynch, & V. Israel-Jost (Eds.), Science after the practice turn in the philosophy, history, and social studies of science (pp. 174–187). Routledge Studies in the Philosophy of Science, London: Routledge
Imbert, C. (2017). Computer simulations and computational models in science. In Springer handbook of model-based science (pp. 735–781). Springer Handbooks, Cham: Springer.
Jones, D. (2010). Good practice in (pseudo) random number generation for bioinformatics applications. Technical report, UCL Bioinformatics Group.
Kalven Jr, H., & Zeisel, H. (1966). The American jury. London: The University of Chicago press.
Kitcher, P. (1992). The Naturalists Return. The Philosophical Review, 101(1), 53–114.
Kitcher, P. (1993). The Advancement of science: Science without legend, objectivity without illusions. New York: Oxford University Press, 1993.
Kitcher, P. (2002). The third way: Reflections on helen longino’s the fate of knowledge. Philosophy of science, 69(4), 549–559.
Lenhard, J., forthcoming. Holism, or the erosion of modularity-a methodological challenge for validation. Philosophy of Science.
Lenhard, J., & Carrier, M. (2017). Mathematics as a tool-tracing new roles of mathematics in the sciences.
Matsumoto, M., Wada, I., Kuramoto, A., & Ashihara, H. (2007). Common defects in initialization of pseudorandom number generators. ACM Transactions on Modeling and Computer Simulation, 17(4).
Rennie, D., Yank, V., & Emanuel, L. (1997, August 20). When authorship fails. A proposal to make contributors accountable. JAMA, 278(7), 579–585.
Rennie, D., Flanagin, A., & Yank, V. (2001). The contributions of authors. JAMA, 284(1), 89–91.
Shapiro, S. (1997). Splitting the difference: The historical necessity of synthesis in software engineering. IEEE Annals of the History of Computing, 19(1), 20–54.
Simon, H. A. (1957). Models of man: Social and rational mathematical essays on rational human behavior in a social setting. New York: Wiley.
Solomon, M. (1994). Social Empiricism. Noûs, 28(3), 325–343.
Foote, B., & Yoder, J. (1999). Pattern languages of program design 4 (= Software Patterns. 4). Addison Wesley.
Wilson, G., Aruliah D. A., Brown C. T., Hong N. P. C., Davis, M, et al. (2014). Best practices for scientific computing. PLOS Biology, 12(1).
Wimsatt, W. C. (2007). Re-engineering philosophy for limited beings: Piecewise approximations to reality. Cambridge, Mass: Harvard University Press.
Winsberg, E. B. (2010). Science in the age of computer simulation. Chicago: Etats-Unis.
Woods, J. (2013). Errors of reasoning: Naturalizing the logic of inference. College Publications.
Acknowledgements
Past interactions with Roman Frigg, Stephan Hartmann, and Paul Humphreys about various issues discussed in this chapter were extremely stimulating, and I probably owe them more than I am aware of. I am also very grateful to the editors, whose comments contributed significantly to improving this chapter.
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Imbert, C. (2019). The Multidimensional Epistemology of Computer Simulations: Novel Issues and the Need to Avoid the Drunkard’s Search Fallacy. In: Beisbart, C., Saam, N. (eds) Computer Simulation Validation. Simulation Foundations, Methods and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-70766-2_43
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