Advertisement

Validation of Computer Simulations from a Kuhnian Perspective

  • Eckhart ArnoldEmail author
Chapter
Part of the Simulation Foundations, Methods and Applications book series (SFMA)

Abstract

While Thomas Kuhn’s theory of scientific revolutions does not specifically deal with validation, the validation of simulations can be related in various ways to Kuhn’s theory: (1) Computer simulations are sometimes depicted as located between experiments and theoretical reasoning, thus potentially blurring the line between theory and empirical research. Does this require a new kind of research logic that is different from the classical paradigm which clearly distinguishes between theory and empirical observation? I argue that this is not the case. (2) Another typical feature of computer simulations is their being “motley” (Winsberg in Philos Sci 70:105–125, 2003) with respect to the various premises that enter into simulations. A possible consequence is that in case of failure it can become difficult to tell which of the premises is to blame. Could this issue be understood as fostering Kuhn’s mild relativism with respect to theory choice? I argue that there is no need to worry about relativism with respect to computer simulations, in particular. (3) The field of social simulations, in particular, still lacks a common understanding concerning the requirements of empirical validation of simulations. Does this mean that social simulations are still in a prescientific state in the sense of Kuhn? My conclusion is that despite ongoing efforts to promote quality standards in this field, lack of proper validation is still a problem of many published simulation studies and that, at least large parts of social simulations must be considered as prescientific.

Keywords

Computer simulations Validation of simulations Scientific paradigms 

References

  1. Ahrweiler, P., & Gilbert, N. (2015). The quality of social simulation: An example from research policy modelling. In M. Janssen, M. A. Wimmer, & A. Deljoo (Eds.), Policy practice and digital science: Integrating complex systems, social simulation and public administration in policy research (pp. 35–55). Cham: Springer International Publishing.  https://doi.org/10.1007/978-3-319-12784-2_3. ISBN: 978-3-319-12784-2.CrossRefGoogle Scholar
  2. Arnold, E. (2013). Experiments and simulations: Do they fuse? In E. Arnold, & J. Duran (Eds.), Computer simulations and the changing face of scientific experimentation (pp. 46–75). Newcastle: Cambridge Scholars Publishing. 978-1443847926.Google Scholar
  3. Arnold, E. (2014). What’s wrong with social simulations? The Monist, 97(3), 361–379.  https://doi.org/10.5840/monist201497323. ISSN: 0026-9662.CrossRefGoogle Scholar
  4. Axelrod, R. (1984). The evolution of cooperation. Basic Books.Google Scholar
  5. Axtell, R. L., et al. (2002) Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley. Proceedings of the National Academy of Sciences, 99(3), 7275–7279.  https://doi.org/10.1073/pnas.092080799. ISSN: 0027-8424.CrossRefGoogle Scholar
  6. Beisbart, C. (2011). A transformation of normal science. Computer simulations from a philosophical perspective. unpublished.Google Scholar
  7. Binmore, K. (1998) Game theory and the social contract II. Just playing. Cambridge, Massachusetts/London, England: MIT Press.Google Scholar
  8. Binmore, K. (1994). Game theory and the social contract I. Playing fair. Fourth printing. Cambridge, Massachusetts/London, England: MIT Press.Google Scholar
  9. Bird, A. (2013). Thomas Kuhn. In E. N. Zalta (Ed.), The stanford encyclopedia of philosophy. Fall 2013. Metaphysics Research Lab, Stanford University.Google Scholar
  10. Cartwright, N. (1983). How the laws of physics lie.Clarendon paperbacks. Oxford University Press. Clarendon Press. ISBN: 9780198247043.Google Scholar
  11. Carusi, A., Rodriguez, B., & Burrage, K. (2013). Model systems in computational systems biology. In E. Arnold, & J. Duran (Eds.), Computer simulations and the changing face of scientific experimentation. (Chap. 6).Google Scholar
  12. Cohen, M. D., March, J. G., & Olsen, J. P. (1972). A grabage can model of organizational choide. Administrative Science Quarterly, 17, 1–25.CrossRefGoogle Scholar
  13. Delgoshaei, B., & Fatahi, M. (2013). Garbage can decision-making in a matrix structure. A Case study of linköping university. Linköping University/Department of Management and Engineering. urn:nbn:se:liu:diva-95612.Google Scholar
  14. Dugatkin, L. A. (1997). Cooperation among animals. Oxford University Press.Google Scholar
  15. Dupré, J. (1994). Against scientific imperialism. Philosophy of Science Association Proceedings, 2, 374–381.Google Scholar
  16. Epstein, J. M. (2008). Why model? Journal of Artificial Societies and Social Simulation, 11(4), 12. ISSN: 1460-7425.Google Scholar
  17. Epstein, J. M., & Axtell, R. L. (1996). Growing artificial societies: Social science from the bottom up. MIT Press.Google Scholar
  18. Fardal, H., & Sørnes, J. O. (2008). Is strategic decision-making. A garbage can view. Issues in Informing Science and Information Technology, 5.Google Scholar
  19. Feyerabend, P. (1975/1983). Wider den Methodenzwang. Suhrkamp Verlag.Google Scholar
  20. Fioretti, G., & Lomi, A. (2008). An agent-based representation of the garbage can model of organizational choice. Journal of Artificial Societies and Social Simulation, 11(1), 1. ISSN: 1460-7425.Google Scholar
  21. Gilbert, N., & Troitzsch, K. (2005). Simulation for the social scientist. New York: Open University Press.Google Scholar
  22. Gleick, J. (2011). Chaos: Making a new science. Open Road Media.Google Scholar
  23. Grötker, R. (2005). Reine Meinungsmache. German: Technology Review (heise Verlag). http://%5C-www.%5C-heise.%5C-de/%5C-tr/%5C-artikel/%5C-Reine-%5C-Meinungsmache-%5C-277359.%5C-html.Google Scholar
  24. Guala, F. (2002). Models, simulations and experiments. In L. Magnani & N. Nersessian (Eds.), Model-based reasoning: Science, technology, values (pp. 59–74). Kluwer Academic Publishers.Google Scholar
  25. Hammerstein, P. (2003). Why is reciprocity so rare in social animals? A protestant appeal. In P. Hammerstein (Ed.), Genetic and cultural evolution (pp. 83–94). Cambridge, Massachusetts/London, England: MIT Press in cooperation with Dahlem University Press. (Chap. 5).Google Scholar
  26. Harding, S. G. (Ed.). (1976). Can theories be refuted? Essays on the Duhem-Quine thesis. Kluwer.Google Scholar
  27. Harding, A., Keegan, M., & Kelly, S. (2010). Validating a dynamic population microsimulation model: Recent experience in Australia. International Journal of Microsimulation, 3(2), 46–64.Google Scholar
  28. Hartmann, S. (1996). The world as a process: Simulations in the natural and social sciences. In R. Hegselmann et al. (Ed.) Simulation and modelling in the social sciences from the philosophy of science point of view (pp. 77–110).CrossRefGoogle Scholar
  29. Heath, B., Hill, R., & Ciarallo, F. (2009). A survey of agent-based modeling practices (January 1998 to July 2008). Journal of Artificial Societies and Social Simulation (JASSS), 12(4), 9.Google Scholar
  30. Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence: Models, analysis and simulation. Journal of Artificial Societies and Social Simulation, 5(3), 1.Google Scholar
  31. Imbert, C. (2017). Computer simulations and computational models in science. In L. Magnani, & T. Bertolotti (Eds.), Springer handbook of model-based science (pp. 735–781).  https://doi.org/10.1007/978-3-319-30526-4.Google Scholar
  32. Kästner, J., & Arnold, E. (2013). When can a computer simulation act as substitute for an experiment? A case-study from chemisty. In E. Arnold (Ed.), Homepage Eckhart Arnold, preprint.Google Scholar
  33. Kornmesser, S. (2014). Scientific revolutions without paradigm-replacement and the coexistence of competing paradigms: The case of generative grammar and construction grammar. Journal for General Philosophy of Science, 45(1), 91–118.  https://doi.org/10.1007/s10838-013-9227-3. ISSN: 1572-8587.CrossRefGoogle Scholar
  34. Kuhn, T. S. (1976). Suhrkamp: Die Struktur wissenschaftlicher Revolutionen.Google Scholar
  35. Kuhn, T. S. (1977). The essential tension. Selected studies in scientific tradition and change. The University of Chicago Press.Google Scholar
  36. Lenhard, J. (2007). Computer simulation: The cooperation between experimenting and modeling. Philosophy of Science, 74(2), 176–194.  https://doi.org/10.1086/519029.MathSciNetCrossRefGoogle Scholar
  37. Lenhard, J., & Winsberg, E. (2010). Holism, entrenchment, and the future of climate model pluralism. Studies in History and Philosophy of Modern Physics, 41, 253–262.Google Scholar
  38. Morgan, M. S. (2003). Experiments without material intervention. Model experiments, virtual experiments, and virtually experiments. In H. Radder (Ed.), The philosophy of scientific experimentation (pp. 216–233). University of Pittsburgh Press.Google Scholar
  39. Morgan, M. S., & Morrison, M. (Eds). (1999). Models as mediators. Perspectives on natural and social science.Google Scholar
  40. Morrison, M. (2009). Models, measurement and computer simulation: The changing face of experimentation. Philosophical Studies, 143, 33–57.  https://doi.org/10.1007/s11098-008-9317-y.CrossRefGoogle Scholar
  41. Moses, J. W., & Knutsen, T. L. (2012). Ways of knowing. Competing methodologies in social and political research 2nd (first ed. 2007). London: Palgrave Macmillan.Google Scholar
  42. O’Sullivan, D., et al. (2016). Short communication. Strategic directions for agent-based modeling: Avoiding the YAAWN syndrome. Journal of Land Use Science, 11(2), 177–187.CrossRefGoogle Scholar
  43. Parker, W. S. (2009). Does matter really matter? Computer simulations, experiments, and materiality. Synthese, 169, 483–496.  https://doi.org/10.1007/s11229-008-9434-3.CrossRefGoogle Scholar
  44. Peschard, I. (2010). Review of Eric Winsberg’s. Science in the age of computer simulation. University of Chicago Press. Notre Dame Philosophical Reviews (Mar. 31, 2011).Google Scholar
  45. Phelps, S. (2016). An empirical game-theoretic analysis of the dynamics of cooperation in small groups. Journal of Artificial Societies and Social Simulation, 19(2), 4.  https://doi.org/10.18564/jasss.3060. ISSN: 1460-7425.
  46. Preston, J. (2016). Paul Feyerabend. In E. N. Zalta (Ed.), The stanford encyclopedia of philosophy. Winter 2016. Metaphysics Research Lab, Stanford University.Google Scholar
  47. Railsback, S. F., Grimm, V. (2012). Agent-based and individual-based modeling. A practical introduction. Princeton University Press.Google Scholar
  48. Rendell, L., et al. (2010). Why copy others? Insights from the social learning strategies tournament. Science, 328, 208–213.  https://doi.org/10.1126/science.1184719.MathSciNetCrossRefzbMATHGoogle Scholar
  49. Reutlinger, A., Hangleiter, D., & Hartmann, S. (2017). Understanding (with) toy models. The British Journal for the Philosophy of Science, axx005.  https://doi.org/10.1093/bjps/axx005.CrossRefGoogle Scholar
  50. Schelling, T. C. (1971). Dynamic models of segregation. The Journal of Mathematical Sociology, 1(2), 143–186.  https://doi.org/10.1080/0022250X.1971.9989794. ISSN: 0022-250X.CrossRefGoogle Scholar
  51. Schurz, G. (2014). Koexistenz und Komplementarität rivalisierender Paradigmen: Analyse, Diagnose und kulturwissenschaftliches Fallbeispiel. In S. Kornmesser, & G. Schurz (Eds.), Die multiparadigmatische Struktur der Wissenschaften (pp. 47–62). Wiesbaden: Springer Fachmedien Wiesbaden.  https://doi.org/10.1007/978-3-658-00672-3_2. ISBN: 978-3-658-00672-3.Google Scholar
  52. Sismondo, S. (2010). An introduction to science and technology studies (2nd ed.). Wiley.Google Scholar
  53. Squazzoni, F., & Casnici, N. (2013). Is social simulation a social science outstation? A bibliometric analysis of the impact of JASSS. Journal of Artificial Societies and Social Simulation, 16(1), 10. ISSN: 1460-7425.Google Scholar
  54. Winsberg, E. (2015). Computer simulations in science. In E. N. Zalta (Ed.), The stanford encyclopedia of philosophy. Summer 2015. Metaphysics Research Lab, Stanford University.Google Scholar
  55. Winsberg, E. (2003). Simulated experiments: Methodology for a virtual world. Philosophy of Science, 70, 105–125.CrossRefGoogle Scholar
  56. Winsberg, E. (2009). A tale of two methods. Synthese, 169, 575–592.  https://doi.org/10.1007/s11229-008-9437-0.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Bavarian Academy of Sciences and HumanitiesMunichGermany

Personalised recommendations