, Volume 87, Issue 2, pp 233–250 | Cite as

Simulations of opinion changes in scientific communities

  • Pawel SobkowiczEmail author


We present a computer model of opinion changes in a scientific community. The study takes into account two mechanisms of opinion formation for individual scientists: influence of coworkers with whom there is direct interaction and cumulative influence of the subject literature. We analyze the evolution of relative popularity of different competing theories, depending on their accuracy in describing observed phenomena and on current social support of the theory. We include such aspects as finite lifetime of publication impact and tendency to ‘defend’ one’s own opinions, especially if they were already published. A special class of publications, delivering crucial observational or experimental data, which may revolutionize the scientific worldview is considered. The goal of the model is to discover which conditions lead to quick domination of one theory over others, or, conversely, in which situations one may expect several explanations to co-exist.


Social simulations Opinion formation Agent based societies Metascience 


  1. Ackermann, E. (2006). Indicators of failed information epidemics in the scientific journal literature: A publication analysis of Polywater and Cold Nuclear Fusion. Scientometrics, 66(3), 451–466.CrossRefMathSciNetGoogle Scholar
  2. Anderegg, W. R. L., Prall, J. W., Harold, J., & Schneider, S. H. (2010). Expert credibility in climate change. Proceedings of the National Academy of Sciences, 107(27), 12,107–12,109.CrossRefGoogle Scholar
  3. Aspect, A., Grangier, P., & Roger, G. (1981). Experimental tests of realistic local theories via Bell’s theorem. Physical Review Letters, 47, 460–463.CrossRefGoogle Scholar
  4. Aspect, A., Grangier, P., & Roger, G. (1982). Experimental realization of Einstein-Podolsky-Rosen-Bohm Gedanken experiment: A new violation of Bell’s inequalities. Physical Review Letters, 49, 91–94.CrossRefGoogle Scholar
  5. Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.CrossRefMathSciNetGoogle Scholar
  6. Bell, J. S. (1964). On the Einstein-Podolsky-Rosen paradox. Physics, 1, 195–200.Google Scholar
  7. Bettencourt, L. M. A., Cintrón-Arias, A., Kaiser, D. I., & Castillo-Chávez, C. (2006). The power of a good idea: Quantitative modeling of the spread of ideas from epidemiological models. Physica A Statistical Mechanics and its Applications, 364, 513–536.CrossRefGoogle Scholar
  8. Börner, K., Maru, J., & Goldstone, R. (2004). The simultaneous evolution of author and paper networks. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1), 5266.CrossRefGoogle Scholar
  9. Brannon, L., Tagler, M., & Eagly, A. (2007). The moderating role of attitude strength in selective exposure to information. Journal of Experimental Social Psychology, 43(4), 611–617.CrossRefGoogle Scholar
  10. Bruckner, E., Ebeling, W., & Scharnhorst, A. (1990). The application of evolution models in scientometrics. Scientometrics, 18(1), 21–41.CrossRefGoogle Scholar
  11. Cantú, A., & Ausloos, M. (2009). Organizational and dynamical aspects of a small network with two distinct communities: Neo-creationists vs. evolution defenders. Scientometrics, 80(2), 457–472.CrossRefGoogle Scholar
  12. Carroll, S. M. (2004). Why is the universe accelerating? In W. L. Freedman (Ed.), Measuring and modeling the universe. Carnegie Observatories Astrophysics Series (Vol. 2, p. 235). Cambridge: Cambridge University Press. Google Scholar
  13. Castellano, C., Fortunato, S., & Loreto, V. (2009). Statistical physics of social dynamics. Reviews of Modern Physics, 81, 591–646.CrossRefGoogle Scholar
  14. Clauser, J. F., Horne, M. A., Shimony, A., & Holt, R. A. (1969). Proposed experiment to test local hidden-variable theories. Physical Review Letters, 23, 880–884.CrossRefGoogle Scholar
  15. Deffuant, G., Neau, D., Amblard, F., & Weisbuch, G. (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, 3, 87–98.CrossRefGoogle Scholar
  16. Dufour, C., & Tabah, A. (1998). Information epidemics and the transformation of science. In CAIS/ACSI’98: Information science at the dawn of the next millennium. Proceedings of the 26th annual conference of the Canadian Association for Information Science, Association canadienne des sciences de l’information, 3–5 June 1998 (p. 143). Ottawa, ON: Universite d’Ottawa.Google Scholar
  17. Einstein, A. (1917). Kosmologische Betrachtungen zur allgemeinen Relativitätstheorie. Sitzungsber Preuss Akad Wiss (pp. 142–152).Google Scholar
  18. Einstein, A. (1931). Zum kosmologischen Problem der allgemeinen Relativitätstheorie. Sitzungsber Preuss Akad Wiss (pp. 235–237).Google Scholar
  19. Einstein, A., Podolsky, B., & Rosen, N. (1935). Can quantum mechanical description of physical reality be considered complete? Physical Review, 47, 777–780.CrossRefzbMATHGoogle Scholar
  20. Elga, A. (2007). Reflection and disagreement. Noûs, 41(3), 478–502.CrossRefGoogle Scholar
  21. Freire, O., Jr. (2004). The historical roots of “foundations of quantum physics” as a field of research (1950–1970). Foundations of Physics, 34(11), 1741–1760.CrossRefMathSciNetGoogle Scholar
  22. Freire, O., Jr. (2006). Philosophy enters the optics laboratory: Bell’s theorem and its first experimental tests (1965–1982). Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 37(4), 577–616.CrossRefMathSciNetGoogle Scholar
  23. Galam, S. (2008). Sociophysics: A review of Galam models. International Journal of Modern Physics C, 19(3), 409–440. Scholar
  24. Galam, S., Gefen, Y., & Shapir, Y. (1982). Sociophysics: A new approach of sociological collective behaviour. I. Mean-behaviour description of a strike. Journal of Mathematical Sociology, 9, 1–13.CrossRefzbMATHGoogle Scholar
  25. Gilbert, N. (1997). A simulation of the structure of academic science. Sociological Research Online, 2(2).
  26. Goffman, W. (1966). Mathematical approach to the spread of scientific ideas—the history of mast cell research. Nature, 212(5061), 449–452.CrossRefGoogle Scholar
  27. Goldman, A. (2001). Experts: Which ones should you trust? Philosophy and Phenomenological Research, 63(1), 85–110.CrossRefGoogle Scholar
  28. Hołyst, J., Kacperski, K., & Schweitzer, F. (2001). Social impact models of opinion dynamics. Annual Review of Computational Physics, 20, 531–535.Google Scholar
  29. Huntington, P., Nicholas, D., Jamali, H., & Tenopir, C. (2006). Article decay in the digital environment: An analysis of usage of OhioLINK by date of publication, employing deep log methods. Journal of the American Society for Information Science and Technology, 57(13), 1840–1851.CrossRefGoogle Scholar
  30. Kacperski, K., & Hołyst, J. (1999). Opinion formation model with strong leader and external impact: A mean field approach. Physica A, 269, 511–526.CrossRefGoogle Scholar
  31. Kacperski, K., & Hołyst, J. (2000). Phase transitions as a persistent feature of groups with leaders in models of opinion formation. Physica A, 287, 631–643.CrossRefGoogle Scholar
  32. Knobloch-Westerwick, S., & Meng, J. (2009). Looking the other way: Selective exposure to attitude-consistent and counterattitudinal political information. Communication Research, 36(3), 426–448.CrossRefGoogle Scholar
  33. Kochen, M., & Blaivas, A. (1981). A model for the growth of mathematical specialties. Scientometrics, 3(4), 265–273.CrossRefGoogle Scholar
  34. Kuhn, T. S. (1962). The structure of scientific revolutions. Chicago: University of Chicago Press.Google Scholar
  35. Laloë, F. (2001). Do we really understand quantum mechanics? Strange correlations, paradoxes, and theorems . American Journal of Physics, 69, 655–701.CrossRefGoogle Scholar
  36. Lee, S., & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673.CrossRefGoogle Scholar
  37. Lewenstein, M., Nowak A., & Latané, B. (1992). Statistical mechanics of social impact. Physical Review A, 45, 763–776.CrossRefMathSciNetGoogle Scholar
  38. Lotka, A. (1926). The frequency distribution of scientific productivity. Journal of Washington Academy Sciences, 16, 317–323.Google Scholar
  39. Martins, A. C. R. (2010). Modeling scientific agents for a better science. Advances in Complex Systems (ACS), 13(04), 519–533.CrossRefMathSciNetGoogle Scholar
  40. Martinson, B., Anderson, M., & De Vries, R. (2005). Scientists behaving badly. Nature, 435(7043), 737–738.CrossRefGoogle Scholar
  41. Menskii, M. B. (2000). Quantum mechanics: New experiments, new applications, and new formulations of old questions. Uspekhi Fizicheskikh Nauk, 43, 585–600.CrossRefGoogle Scholar
  42. Newman, M., & Park, J. (2003). Why social networks are different from other types of networks. Physical Review E, 68(3), 36,122.CrossRefGoogle Scholar
  43. Newman, M. E. J. (2001a). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64(1), 016,131Google Scholar
  44. Newman, M. E. J. (2001b). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America, 98, 5955–5956.CrossRefGoogle Scholar
  45. Nicholas, D., Huntington, P., Dobrowolski, T., Rowlands, I., Jamali, H. R., & Polydoratou, P. (2005). Revisiting ‘obsolescence’ and journal article ‘decay’ through usage data: An analysis of digital journal use by year of publication. Information Processing and Management, 41(6), 1441–1461.CrossRefGoogle Scholar
  46. Nowak, A., & Lewenstein, M. (1996). Modeling social change with cellular automata. In R. Hegselmann, U. Mueller, & K. G. Troitzsch (Eds.), Modelling and simulation in the social sciences from a philosophy of science point of view (pp. 249–285). Dordrecht: Kluwer.Google Scholar
  47. Perlmutter, S., Aldering, G., Deustua, S., Fabbro, S., Goldhaber, G., Groom, D. E., et al. (1997). Cosmology from type IA supernovae: Measurements, calibration techniques, and implications. Bulletin of the American Astronomical Society, 29, 1351.Google Scholar
  48. Pigliucci, M., & Kaplan, J. (2000). The fall and rise of Dr Pangloss: Adaptationism and the Spandrels paper 20 years later. Trends in Ecology and Evolution, 15, 66–70.CrossRefGoogle Scholar
  49. Riess, A. G., Filippenko, A. V., Challis, P., Clocchiatti, A., Diercks, A., Garnavich, P. M., et al. (1998). Observational evidence from supernovae for an accelerating universe and a cosmological constant. The Astronomical Journal, 116, 1009–1038.CrossRefGoogle Scholar
  50. Simkin, M. V., & Roychowdhury, V. P. (2003). Read before you cite. Complex Systems, 14, 269–274.Google Scholar
  51. Simkin, M. V., & Roychowdhury, V. P. (2005a). Copied citations create renowned papers? Annals of Improbable Research, 11(1), 24–27.CrossRefGoogle Scholar
  52. Simkin, M. V., & Roychowdhury, V. P. (2005b). Stochastic modeling of citation slips. Scientometrics, 62(3), 367–384.CrossRefGoogle Scholar
  53. Simkin, M. V., & Roychowdhury, V. P. (2006). An introduction to the theory of citing. Significance, 3, 179–181.CrossRefMathSciNetGoogle Scholar
  54. Smolin, L. (2006). The trouble with physics: The rise of string theory, the fall of science and what comes next. London: Penguin Books Ltd.zbMATHGoogle Scholar
  55. Sobkowicz, P. (2009). Studies of opinion stability for small dynamic networks with opportunistic agents. International Journal of Modern Physics C (IJMPC), 20(10), 1645–1662.CrossRefzbMATHGoogle Scholar
  56. Sobkowicz, P. (2010). Effect of leader’s strategy on opinion formation in networked societies with local interactions. International Journal of Modern Physics C (IJMPC), 21(6), 839–852.CrossRefzbMATHGoogle Scholar
  57. Sterman, J. D. (1985). The growth of knowledge: Testing a theory of scientific revolutions with a formal model. Technological Forecasting and Social Change, 28(2), 93–122.CrossRefGoogle Scholar
  58. Sutton, A., Duval, S., Tweedie, R., Abrams, K., & Jones, D. (2000). Empirical assessment of effect of publication bias on meta-analyses. British Medical Journal, 320(7249), 1574.CrossRefGoogle Scholar
  59. Sznajd-Weron, K., & Sznajd, J. (2000). Opinion evolution in closed community. International Journal of Modern Physics C, 11, 1157–1166.CrossRefGoogle Scholar
  60. Tabah, A. (1996). Information epidemics and the growth of physics. PhD thesis, Graduate School of Library and Information Studies, McGill University, Montreal, Canada.Google Scholar
  61. Thurner, S., & Hanel, R. (2010). Peer-review in a world with rational scientists: Toward selection of the average. Arxiv preprint arXiv:10084324.
  62. Wiltshire, D. L. (2008a). Cosmological equivalence principle and the weak-field limit. Physical Review D, 78, 084,032.CrossRefMathSciNetGoogle Scholar
  63. Wiltshire, D. L. (2008b). Gravitational energy and cosmic acceleration. International Journal of Modern Physics D, 17, 641.CrossRefMathSciNetzbMATHGoogle Scholar
  64. Zeilinger, A. (1999). A foundational principle for quantum mechanics. Foundations of Physics, 29, 631.CrossRefMathSciNetGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2011

Authors and Affiliations

  1. 1.WarsawPoland

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