Advertisement

Epistemic Entitlements and the Practice of Computer Simulation

  • John SymonsEmail author
  • Ramón Alvarado
Article

Abstract

What does it mean to trust the results of a computer simulation? This paper argues that trust in simulations should be grounded in empirical evidence, good engineering practice, and established theoretical principles. Without these constraints, computer simulation risks becoming little more than speculation. We argue against two prominent positions in the epistemology of computer simulation and defend a conservative view that emphasizes the difference between the norms governing scientific investigation and those governing ordinary epistemic practices.

Keywords

Computer simulation Trust Epistemology Entitlements Models 

Notes

Acknowledgements

This paper has benefited greatly from the work of two referees for this journal. We sincerely thank both of them for their detailed criticisms and thoughtful questions. We are grateful also to Samuel Arbesman, Jack Horner, Paul Humphreys, and Andreas Kaminski for discussions that contributed to the development of this paper. This work is supported by The National Security Agency through the Science of Security initiative contract #H98230-18-D-0009.

References

  1. Adler, J. (2015). Epistemological problems of testimony. In E. N. Zalta (Ed.), The stanford encyclopedia of philosophy (summer 2015 edition). https://plato.stanford.edu/archives/sum2015/entries/testimony-episprob/. Accessed 20 Dec 2018.
  2. Alvarado, R., & Humphreys, P. (2017). Big data, thick mediation, and representational opacity. New Literary History, 48(4), 729–749.CrossRefGoogle Scholar
  3. Arkoudas, K., & Bringsjord, S. (2007). Computers, justification, and mathematical knowledge. Minds and Machines, 17(2), 185–202.CrossRefGoogle Scholar
  4. Audi, R. (1997). The place of testimony in the fabric of knowledge and justification. American Philosophical Quarterly, 34(4), 405–422.Google Scholar
  5. Barberousse, A., Franceschelli, S., & Imbert, C. (2009). Computer simulations as experiments. Synthese, 169(3), 557–574.MathSciNetCrossRefGoogle Scholar
  6. Barberousse, A., & Vorms, M. (2014). About the warrants of computer-based empirical knowledge. Synthese, 191(15), 3595–3620.CrossRefGoogle Scholar
  7. Beebee, H. (2001). Transfer of warrant, begging the question and semantic externalism. The Philosophical Quarterly, 51(204), 356–374.CrossRefGoogle Scholar
  8. Beisbart, C. (2017). Advancing knowledge through computer simulations? A socratic exercise. In M. Resch, A. Kaminski, & P. Gehring (Eds.), The science and art of simulation I (pp. 153–174). Berlin: Springer.CrossRefGoogle Scholar
  9. Borge, S. (2003). The word of others. Journal of Applied Logic, 1(1–2), 107–118.MathSciNetCrossRefGoogle Scholar
  10. Boschetti, F., Fulton, E., Bradbury, R., & Symons, J. (2012). What is a model, why people don’t trust them and why they should. In M. R. Raupach (Ed.), Negotiating our future: Living scenarios for Australia to 2050 (pp. 107–118). Australian Academy of Science.Google Scholar
  11. Burge, T. (1993). Content preservation. The Philosophical Review, 102(4), 457–488.CrossRefGoogle Scholar
  12. Burge, T. (1998). Computer proof, apriori knowledge, and other minds: The sixth philosophical perspectives lecture. Noûs, 32(S12), 1–37.MathSciNetCrossRefGoogle Scholar
  13. Davidson, D. (1973). Radical interpretation. Dialectica, 27(3–4), 313–328.CrossRefGoogle Scholar
  14. Davies, M. (2004) II—Martin Davies: Epistemic entitlement, warrant transmission and easy knowledge. In Aristotelian Society supplementary volume (Vol. 78(1)). Oxford: The Oxford University Press.Google Scholar
  15. Dretske, F. (2000). Entitlement: Epistemic rights without epistemic duties? Philosophy and Phenomenological Research, 60(3), 591–606.CrossRefGoogle Scholar
  16. Fresco, N., & Primiero, G. (2013). Miscomputation. Philosophy & Technology, 26(3), 253–272.CrossRefGoogle Scholar
  17. Frigg, R., & Reiss, J. (2009). The philosophy of simulation: Hot new issues or same old stew? Synthese, 169(3), 593–613.MathSciNetCrossRefGoogle Scholar
  18. Gramelsberger, G. (2011). Generation of evidence in simulation runs: Interlinking with models for predicting weather and climate change. Simulation & Gaming, 42(2), 212–224.CrossRefGoogle Scholar
  19. Holzmann, G. J. (2015). Code inflation. IEEE Software, 2, 10–13.CrossRefGoogle Scholar
  20. Horner, J., & Symons, J. (2014). Reply to Angius and Primiero on software intensive science. Philosophy & Technology, 27(3), 491–494.CrossRefGoogle Scholar
  21. Horner, J, & Symons, J. (forthcoming). Understanding error rates in software engineering: Conceptual, empirical, and experimental approaches.Google Scholar
  22. Hubig, C, & Kaminski, A. (2017). Outlines of a pragmatic theory of truth and error in computer simulation. In M. Resch, A. Kaminski, & P. Gehring (Eds.), The science and art of simulation I (pp. 121–136). Cham: Springer.CrossRefGoogle Scholar
  23. Humphreys, P. (2004). Extending ourselves: Computational science, empiricism, and scientific method. Oxford: Oxford University Press.CrossRefGoogle Scholar
  24. Humphreys, P. (2009). The philosophical novelty of computer simulation methods. Synthese, 169(3), 615–626.MathSciNetCrossRefGoogle Scholar
  25. Jenkins, C. S. (2007). Entitlement and rationality. Synthese, 157(1), 25–45.CrossRefGoogle Scholar
  26. Kuhl, F., Dahmann, J., & Weatherly, R. (2000). Creating computer simulation systems: An introduction to the high level architecture. Upper Saddle River: Prentice Hall.zbMATHGoogle Scholar
  27. Lackey, J. (1999). Testimonial knowledge and transmission. The Philosophical Quarterly, 49(197), 471–490.CrossRefGoogle Scholar
  28. Lazer, D., Kennedy, R., King, G., et al. (2014). The parable of Google Flu: Traps in big data analysis. Science, 434, 343.Google Scholar
  29. McEvoy, M. (2008). The epistemological status of computer-assisted proofs. Philosophia Mathematica, 16(3), 374–387.MathSciNetCrossRefGoogle Scholar
  30. McEvoy, M. (2013). Experimental mathematics, computers and the a priori. Synthese, 190(3), 397–412.MathSciNetCrossRefGoogle Scholar
  31. McGlynn, A. (2014). On Epistemic Alchemy. In D. Dodd, & E. Zardini (Eds.), Scepticism and Perceptual Justification. (pp. 173–189), OUP Oxford.Google Scholar
  32. Moretti, L., & Piazza, T. (2013). When warrant transmits and when it doesn’t: Towards a general framework. Synthese, 190(13), 2481–2503.CrossRefGoogle Scholar
  33. Morgan, M. S. (2005). Experiments versus models: New phenomena, inference and surprise. Journal of Economic Methodology, 12(2), 317–329.MathSciNetCrossRefGoogle Scholar
  34. Morrison, M. (2015). Reconstructing reality. Oxford: Oxford University Press.CrossRefGoogle Scholar
  35. Naylor, T. H., Balintfy, J. L., Burdick, D. S., & Chu, K. (1966). Computer simulation techniques. New York: Wiley.zbMATHGoogle Scholar
  36. Newman, J. (2015). Epistemic opacity, confirmation holism and technical debt: Computer simulation in the light of empirical software engineering. In International conference on history and philosophy of computing (pp. 256–272). Springer.Google Scholar
  37. Nola, R., & Sankey, H. (2014). Theories of scientific method: An introduction. Abingdon: Routledge.CrossRefGoogle Scholar
  38. Norton, S., & Suppe, F. (2001). Why atmospheric modeling is good science (pp. 67–105). Changing the atmosphere: Expert knowledge and environmental governance.Google Scholar
  39. O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. New York: Broadway Books.zbMATHGoogle Scholar
  40. Oreskes, N. (2004). The scientific consensus on climate change. Science, 306(5702), 1686–1686.CrossRefGoogle Scholar
  41. Oreskes, N., Shrader-Frechette, K., & Belitz, K. (1994). Verification, validation, and confirmation of numerical models in the earth sciences. Science, 263(5147), 641–646.CrossRefGoogle Scholar
  42. Parker, W. S. (2009). Does matter really matter? Computer simulations, experiments, and materiality. Synthese, 169(3), 483–496.CrossRefGoogle Scholar
  43. Pincock, C. (2011). Mathematics and scientific representation. Oxford: Oxford University Press.zbMATHGoogle Scholar
  44. Pryor, J. (2012). When warrant transmits. In W. Crispin (Ed.), Mind, meaning, and knowledge: Themes from the philosophy of Crispin Wright (pp. 269–303). Oxford: University Press.CrossRefGoogle Scholar
  45. Quine, W. V. (1973). The roots of reference. La Salle, Ill: Open Court.Google Scholar
  46. Quine, W. (1960). Word and object. MIT press.Google Scholar
  47. Resch, M. M., Kaminski, A., & Gehring, P. (Eds.). (2017). The science and art of simulation I: Exploring-understanding-knowing. Berlin: Springer.Google Scholar
  48. Resnik, M. (1997). Mathematics as a science of patterns. New York: Oxford University Press.zbMATHGoogle Scholar
  49. Roush, S. (2015). The epistemic superiority of experiment to simulation. Synthese, 169, 1–24.Google Scholar
  50. Ruphy, S. (2011). Limits to modeling: Balancing ambition and outcome in astrophysics and cosmology. Simulation & Gaming, 42(2), 177–194.CrossRefGoogle Scholar
  51. Ruphy, S. (2015). Computer simulations: A new mode of scientific inquiry? In S. O. Hansen (Ed.), The role of technology in science: Philosophical perspectives (pp. 131–148). Dordrecht: Springer.CrossRefGoogle Scholar
  52. Saam, N. J. (2017). Understanding social science simulations: Distinguishing two categories of simulations. In M. Resch, A. Kaminski, & P. Gehring (Eds.), The science and art of simulation I (pp. 67–84). Cham: Springer.CrossRefGoogle Scholar
  53. Steadman, I. (2013). Big data and the death of the theorist. Wired Online, 25, 2013.Google Scholar
  54. Symons, J. (2008). Computational models of emergent properties. Minds and Machines, 18(4), 475–491.CrossRefGoogle Scholar
  55. Symons, J., & Alvarado, R. (2016). Can we trust big data? Applying philosophy of science to software. Big Data & Society, 3(2), 2053951716664747.CrossRefGoogle Scholar
  56. Symons, J., & Boschetti, F. (2013). How computational models predict the behavior of complex systems. Foundations of Science, 18(4), 809–821.CrossRefGoogle Scholar
  57. Symons, J., & Horner, J. (2014). Software intensive science. Philosophy & Technology, 27(3), 461–477.CrossRefGoogle Scholar
  58. Symons, J., & Horner, J. (2017). On some limits to model-based proof of software correctness. In T. Powers (Ed.), Philosophy and computing: Essays in epistemology, philosophy of mind, logic, and ethics. Berlin: Springer.Google Scholar
  59. Tymoczko, T. (1979). The four-color problem and its philosophical significance. Journal of Philosophy, 76, 57–82.CrossRefGoogle Scholar
  60. Vallor, S. (2017). AI and the automation of wisdom. In T. Powers (Ed.), Philosophy and computing: Essays in epistemology, philosophy of mind, logic, and ethics. Philosophical Studies Series (Vol. 128, pp. 161–178). Berlin: Springer.CrossRefGoogle Scholar
  61. Williams, M. (2000). Dretske on epistemic entitlement. Philosophy and Phenomenological Research, 60(3), 607–612.CrossRefGoogle Scholar
  62. Wright, C., & Davies M. (2004) On epistemic entitlement. In Proceedings of the aristotelian society, supplementary volumes (Vol. 78, pp. 167–245). www.jstor.org/stable/4106950. Accessed 20 Dec 2018.
  63. Winsberg, E. (2010). Science in the age of computer simulation. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  64. Winsberg, E. (2015). Computer simulations in science. In E. N. Zalta (Ed.), The stanford encyclopedia of philosophy (summer 2015 edition). http://plato.stanford.edu/archives/sum2015/entries/simulations-science/. Accessed 20 Dec 2018.

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.University of KansasLawrenceUSA

Personalised recommendations