Advertisement

Modeling the social organization of science

Chasing complexity through simulations
  • Carlo Martini
  • Manuela Fernández Pinto
Original paper in Philosophy of Social Sciences

Abstract

At least since Kuhn’s Structure, philosophers have studied the influence of social factors in science’s pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, size of groups, and communication channels among researchers. In this paper we argue that the current research faces an empirical challenge. The challenge is to connect simulation models with data. We present possible scenarios about how the challenge may unfold.

Keywords

Social organization of science Simulation models Computer simulations Empiricism Social epistemology of science 

References

  1. Alexander, J.M., Himmelreich, J., & Thomson, C. (2015). Epistemic landscapes, optimal search, and the division of cognitive labor. Philosophy of Science, 82(3), 424–453.CrossRefGoogle Scholar
  2. Axtell, R.L., Epstein, J.M., Dean, J.S., Gumerman, G.J., Swedlund, A.C., Harburger, J., Chakravarty, S., Hammond, R., Parker, J., & Parker, M. (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.CrossRefGoogle Scholar
  3. Bala, V., & Goyal, S. (1998). Learning from neighbours. Review of Economic Studies, 65, 595–621.CrossRefGoogle Scholar
  4. Bearman, P., Moody, J., & Stovel, K. (2004). Chains of affection: the structure of adolescent romantic and sexual networks. American Journal of Sociology, 110, 44–91.CrossRefGoogle Scholar
  5. Betz, G (2011). Prediction. In Jarvie, I., & Zamora-Bonilla, J. (Eds.) Handbook of philosophy of social science (pp. 645–664). London: Sage.Google Scholar
  6. Börner, K, Boyack, K W, Milojevic, S, & Morris, S (2012). An introduction to modeling science: basic model types, key definitions, and a general framework for the comparison of process models. In Scharnhorst, A., Börner, K., & van den Besselaar, P. (Eds.) Models of science dynamics, encounters between complexity theory and information sciences (pp. 3–22). Berlin: Springer.Google Scholar
  7. Brock, W.A., & Durlauf, S.N. (1999). A formal model of theory choice in science. Economic Theory, 14, 113–30.CrossRefGoogle Scholar
  8. Cartwright, N., Shomar, T., & Suárez, M. (1995). The tool box of science: tools for the building of models with a superconductivity example. Poznan Studies in the Philosophy of the Sciences and the Humanities, 44, 137–149.Google Scholar
  9. De Langhe, R. (2014). A unified model of the division of cognitive labor. Philosophy of Science, 81(3), 444–459.CrossRefGoogle Scholar
  10. De Langhe, R., & Greiff, M. (2009). Standards and the distribution of cognitive labor. The Logic Journal of the IGPL, 18, 278–293.CrossRefGoogle Scholar
  11. Edmonds, B., Gilbert, N., Ahrweiler, P., & Scharnhorst, A. (2011). Simulating the social processes of science. Journal of Artificial Societies and Social Simulation, 14(4), 14.CrossRefGoogle Scholar
  12. Epstein, B. (2011). Agent-based modeling and the fallacies of individualism. In Humphreys, P., & Imbert, C. (Eds.) Models, simulations, and representations (pp. 115–144). New York: Routledge.Google Scholar
  13. Epstein, J.M. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60.CrossRefGoogle Scholar
  14. Eubank, S., Guclu, H., Anil Kumar, V.S., Marathe, M.V., Srinivasan, A., Toroczkai, Z., & Wang, N. (2004). Modelling disease outbreaks in realistic urban social networks. Nature, 429, 180–184.CrossRefGoogle Scholar
  15. Franses, P.H. (2002). A concise introduction to econometrics: an intuitive guide. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  16. Friedman, M. (1953). Essays in positive economics. Chicago: University of Chicago Press.Google Scholar
  17. Gell-Mann, M. (1995). What is complexity? Complexity, 1(1), 16–19.CrossRefGoogle Scholar
  18. Gibbard, A., & Varian, H.R. (1978). Economic models. The Journal of Philosophy, 75(11), 664–677.CrossRefGoogle Scholar
  19. Giere, R.N. (1988). Explaining science: a cognitive approach. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  20. Gilbert, N. (2008). Agent-based models. London: Sage Publications Inc.CrossRefGoogle Scholar
  21. Gilbert, N., & Troitzsch, K.G. (2005). Simulation for the social scientist. New York: McGraw-Hill.Google Scholar
  22. Goldman, A.I., & Shaked, M. (1991). An economic model of scientific activity and truth acquisition. Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition, 63(1), 31–55.CrossRefGoogle Scholar
  23. Grim, P., & et al. (2013). How simulations fail. Synthese, 190, 2367–2390.CrossRefGoogle Scholar
  24. Hobijn, B., & Franses, P.H. (2000). Asymptotically perfect and relative convergence of productivity. Journal of Applied Econometrics, 15, 59–81.CrossRefGoogle Scholar
  25. Hobijn, B., & Franses, P.H. (2001). Are living standards converging? Structural Change and Economic Dynamics, 12, 171–200.CrossRefGoogle Scholar
  26. Kennan, J., & Walker, J. (2011). The effect of expected income on individual migration decisions. Econometrica, 79(1), 211–251.CrossRefGoogle Scholar
  27. Kitcher, P. (1990). The division of cognitive labor. The Journal of Philosophy, 87(1), 5–22.CrossRefGoogle Scholar
  28. MacLeod, M., & Nersessian, N. (2013). Building simulations from the ground up, modeling and theory in systems biology. Philosophy of Science, 80(4), 533–556.CrossRefGoogle Scholar
  29. Mainzer, K. (2007). Thinking in complexity: the computational dynamics of matter, mind and mankind. Berlin: Springer.Google Scholar
  30. Mali, F, Kronegger, L, Doreian, P, & Ferligoj, A (2012). Dynamic scientific co-authorship networks Scharnhorst, A., Börner, K., & van den Besselaar, P. (Eds.), Springer.Google Scholar
  31. Mäki, U. (1992). On the method of isolation in economics. Poznan Studies in the Philosophy of the Sciences and the Humanities, 26, 19–54.Google Scholar
  32. Miller, J.H., & Page, S.E. (2007). Complex adaptive systems: an introduction to computational models of social life. Princeton and Oxford: Princeton University Press.Google Scholar
  33. Morgan, M.S., & Morrison, M. (Eds.) (1999). Models as mediators: perspectives on natural and social science. Cambridge: Cambridge University Press.Google Scholar
  34. Muldoon, R., & Weisberg, M. (2011). Robustness and idealization in models of cognitive labor. Synthese, 183, 161–174.CrossRefGoogle Scholar
  35. Oberkampf, W.L., & Roy, C.J. (2010). Verification and validation in scientific computing. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  36. Radicchi, F., Fortunato, S., & Vespignani, A. (2012). Citation networks. In Scharnhorst, A., Börner, K., & van den Besselaar, P. (Eds.) Models of science dynamics: encounters between complexity theory and information science (pp. 233–257): Springer.Google Scholar
  37. Reiss, J. (2012). Idealization and the aims of economics: three cheers for instrumentalism. Economics and Philosophy, 28(03), 363–383.CrossRefGoogle Scholar
  38. Rosenberg, A. (1993). Scientific innovation and the limits of social scientific prediction. Synthese, 97, 161–182.CrossRefGoogle Scholar
  39. Rosenstock, S., O’Connor, C., & Burner, J. (forthcoming). In Epistemic Networks, is Less Connectivity Really More?Philosophy of Science.Google Scholar
  40. Rudd, P.A. (2000). An introduction to classical econometric theory. Oxford: Oxford University Press.Google Scholar
  41. Schelling, T. (1969). Models of segregation. American Economic Review, 59 (2), 488–493.Google Scholar
  42. Schelling, T. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1(2), 143–186.CrossRefGoogle Scholar
  43. Sent, E.-M. (1997). An economist’s glance at goldman’s economics. Philosophy of Science, 64, 139–148.CrossRefGoogle Scholar
  44. Scharnhorst, A., Börner, K., & van den Besselaar, P. (Eds.) (2012). Models of science dynamics encounters between complexity theory and information sciences. Berlin: Springer.Google Scholar
  45. Strevens, M. (2003). The role of the priority rule in science. The Journal of Philosophy, 100, 55–79.CrossRefGoogle Scholar
  46. Thoma, J. (2015). The epistemic division of labor revisited. Philosophy of Science, 82(3), 454–472.CrossRefGoogle Scholar
  47. Van den Besselaar, P., Börner, K., & Scharnhorst, A. (2012). Science policy and the challenges for modelling science. In Scharnhorst, A., Börner, K., & van den Besselaar, P. (Eds.) Models of science dynamics: encounters between complexity theory and information science (pp. 261–266): Springer.Google Scholar
  48. Van Dijk, D., Franses, P.H., & Paap, R. (2002). A nonlinear long memory model, with an application to US unemployment. Journal of Econometrics, 110, 135–165.CrossRefGoogle Scholar
  49. Weisberg, M. (2013). Simulation and similarity: using models to understand the world. Oxford: Oxford University Press.CrossRefGoogle Scholar
  50. Weisberg, M., & Muldoon, R. (2009). Epistemic landscapes and the division of cognitive labor. Philosophy of Science, 76(2), 225–252.CrossRefGoogle Scholar
  51. Winsberg, E. (2006). Models of success versus the success of models: reliability without truth. Synthese, 152, 1–19.CrossRefGoogle Scholar
  52. Winsberg, E. (2010). Science in the age of computer simulation. Chicago: The University of Chicago Press.CrossRefGoogle Scholar
  53. Wooldridge, J.M. (2009). Introductory econometrics: a modern approach. South-Western Cengage Learning: Mason (OH).Google Scholar
  54. Ylikoski, P. (1995). The invisible hand and science. Science Studies, 8, 32–43.Google Scholar
  55. Zollman, K. (2007). The communication structure of epistemic communities. Philosophy of Science, 74(5), 574–587.CrossRefGoogle Scholar
  56. Zollman, K. (2010). The epistemic benefit of transient diversity. Erkenntnis, 72, 17–35.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Academy of Finland Centre of Excellence in the Philosophy of the Social Sciences, Social and Moral Philosophy, Department of Political and Economic StudiesUniversity of HelsinkiHelsinkiFinland
  2. 2.Department of Philosophy and Center of Applied EthicsUniversidad de los AndesBogotáColombia

Personalised recommendations