Advertisement

European Journal for Philosophy of Science

, Volume 8, Issue 3, pp 855–875 | Cite as

Scientific polarization

  • Cailin O’Connor
  • James Owen Weatherall
Paper in General Philosophy of Science

Abstract

Contemporary societies are often “polarized”, in the sense that sub-groups within these societies hold stably opposing beliefs, even when there is a fact of the matter. Extant models of polarization do not capture the idea that some beliefs are true and others false. Here we present a model, based on the network epistemology framework of Bala and Goyal (Learning from neighbors, Rev. Econ. Stud. 65(3), 784–811 1998), in which polarization emerges even though agents gather evidence about their beliefs, and true belief yields a pay-off advantage. As we discuss, these results are especially relevant to polarization in scientific communities, for these reasons. The key mechanism that generates polarization involves treating evidence generated by other agents as uncertain when their beliefs are relatively different from one’s own.

Keywords

Polarization Network Network epistemology Social epistemology Agent based modeling Theory change 

Notes

Acknowledgments

Thanks to Justin P. Bruner, Calvin Cochran, and the School of Philosophy at Australian National University where most of the research for the paper was carried out. This material is based upon work supported by the National Science Foundation under grant no. STS-1535139.

References

  1. Abramowitz, A. (2010). The disappearing center: Engaged citizens, polarization, and American democracy. New Haven: Yale University Press.Google Scholar
  2. Angere, S. (2010). Knowledge in a social network. Synthese, 167–203.Google Scholar
  3. Axelrod, R. (1997). The dissemination of culture: a model with local convergence and global polarization. Journal of Conflict Resolution, 41(2), 203–226.CrossRefGoogle Scholar
  4. Bala, V., & Goyal, S. (1998). Learning from neighbors. Review of Economic Studies, 65(3), 595–621.CrossRefGoogle Scholar
  5. Baldassarri, D., & Bearman, P. (2007). Dynamics of political polarization. American Sociological Review, 72(5), 784–811.CrossRefGoogle Scholar
  6. Barrett, J.A., Mohseni, A., Skyrms, B. (2017). Self assembling networks. The British Journal for the Philosophy of Science, (forthcoming).Google Scholar
  7. Benoît, J.-P., & Dubra, J. (2014). A theory of rational attitude polarization.Google Scholar
  8. Bramson, A., Grim, P., Singer, D.J., Berger, W.J., Sack, G., Fisher, S., Flocken, C., Holman, B. (2017). Understanding polarization: meanings, measures, and model evaluation. Philosophy of Science, 84(1), 115–159.CrossRefGoogle Scholar
  9. Burgdorfer, W., Barbour, A.G., Hayes, S.F., Benach, J.L., Grunwaldt, E., Davis, J.P. (1982). Lyme disease-a tick-borne spirochetosis? Science, 216(4552), 1317–1319.CrossRefGoogle Scholar
  10. Cook, J., & Lewandowsky, S. (2016). Rational irrationality: Modeling climate change belief polarization using Bayesian networks. Topics in Cognitive Science, 8(1), 160–179.CrossRefGoogle Scholar
  11. Deffuant, G. (2006). Comparing extremism propagation patterns in continuous opinion models. Journal of Artificial Societies and Social Simulation, 9(3).Google Scholar
  12. Deffuant, G., Amblard, F., Weisbuch, G., Faure, T. (2002). How can extremism prevail? A study based on the relative agreement interaction model. Journal of Artificial Societies and Social Simulation 5(4).Google Scholar
  13. Dixit, A.K., & Weibull, J.W. (2007). Political polarization. Proceedings of the National Academy of Sciences, 104(18), 7351–7356.CrossRefGoogle Scholar
  14. Embers, M.E., Barthold, S.W., Borda, J.T., Bowers, L., Doyle, L., Hodzic, E., Jacobs, M.B., Hasenkampf, N.R., Martin, D.S., Narasimhan, S., et al. (2012). Persistence of Borrelia burgdorferi in rhesus macaques following antibiotic treatment of disseminated infection. PloS One, 7(1), e29914.CrossRefGoogle Scholar
  15. Festinger, L., Schachter, S., Back, K. (1950). Social pressures in informal groups; a study of human factors in housing.Google Scholar
  16. Galam, S. (2010). Public debates driven by incomplete scientific data: the cases of evolution theory, global warming and H1N1 pandemic influenza. Physica A: Statistical Mechanics and its Applications, 389(17), 3619–3631.CrossRefGoogle Scholar
  17. Galam, S. (2011). Collective beliefs versus individual inflexibility: The unavoidable biases of a public debate. Physica A: Statistical Mechanics and its Applications, 390(17), 3036–3054.CrossRefGoogle Scholar
  18. Galam, S., & Moscovici, S. (1991). Towards a theory of collective phenomena: consensus and attitude changes in groups. European Journal of Social Psychology, 21 (1), 49–74.CrossRefGoogle Scholar
  19. Hegselmann, R., Krause, U., et al. (2002). Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 5(3).Google Scholar
  20. Hegselmann, R., Krause, U., et al. (2006). Truth and cognitive division of labor: First steps towards a computer aided social epistemology. Journal of Artificial Societies and Social Simulation, 9(3), 10.Google Scholar
  21. Holman, B., & Bruner, J.P. (2015). The problem of intransigently biased agents. Philosophy of Science, 82(5), 956–968.CrossRefGoogle Scholar
  22. Jeffrey, R.C. (1990). The logic of decision. 2nd edn.Google Scholar
  23. Jern, A., Chang, K.-M.K., Kemp, C. (2014). Belief polarization is not always irrational. Psychological Review, 121(2), 206.CrossRefGoogle Scholar
  24. Kitcher, P. (1990). The division of cognitive labor. The Journal of Philosophy, 87(1), 5–22.CrossRefGoogle Scholar
  25. Klempner, M.S., Linden, T.H., Evans, J., Schmid, C.H., Johnson, G.M., Trevino, R.P., Norton, D., Levy, L., Wall, D., McCall, J., et al. (2001). Two controlled trials of antibiotic treatment in patients with persistent symptoms and a history of Lyme disease. New England Journal of Medicine, 345(2), 85–92.CrossRefGoogle Scholar
  26. Kummerfeld, E., & Zollman, K.J.S. (2015). Conservatism and the scientific state of nature. The British Journal for the Philosophy of Science, 67(4), 1057–1076.CrossRefGoogle Scholar
  27. Kurz, S., & Rambau, J. (2011). On the Hegselmann–Krause conjecture in opinion dynamics. Journal of Difference Equations and Applications, 17(6), 859–876.CrossRefGoogle Scholar
  28. La Rocca, C.E., Braunstein, L.A., Vazquez, F. (2014). The influence of persuasion in opinion formation and polarization. EPL (Europhysics Letters), 106(4), 40004.CrossRefGoogle Scholar
  29. Liu, Q., Zhao, J., Wang, L., Wang, X. (2014). A Multi-Agent model of opinion formation with truth seeking and endogenous leaders. IFAC Proceedings Volumes, 47(3), 11709–11714.CrossRefGoogle Scholar
  30. Lord, C.G., Ross, L., Lepper, M.R. (1979). Biased assimilation and attitude polarization: the effects of prior theories on subsequently considered evidence. Journal of Personality and Social Psychology, 37(11), 2098.CrossRefGoogle Scholar
  31. Macy, M.W., Kitts, J.A., Flache, A., Benard, S. (2003). Polarization in dynamic networks: a Hopfield model of emergent structure. In R. Brieger, K. Carley, P. Pattison (Eds.) Dynamic social network modeling and analysis (pp. 162–173). Washington, DC: National Academic Press.Google Scholar
  32. Mäs, M., & Flache, A. (2013). Differentiation without distancing. Explaining bi-polarization of opinions without negative influence. PloS One, 8(11), e74516.CrossRefGoogle Scholar
  33. Mayo-Wilson, C., Zollman, K.J.S., Danks, D. (2011). The independence thesis: when individual and social epistemology diverge. Philosophy of Science, 78(4), 653–677.CrossRefGoogle Scholar
  34. McCright, A.M., & Dunlap, R.E. (2011). The politicization of climate change and polarization in the American public’s views of global warming, 2001–2010. The Sociological Quarterly, 52(2), 155–194.CrossRefGoogle Scholar
  35. Nowak, A., Szamrej, J., Latané, B. (1990). From private attitude to public opinion: a dynamic theory of social impact. Psychological Review, 97(3), 362.CrossRefGoogle Scholar
  36. O’Connor, C., & Weatherall, J.O. (2017). Do as I say, Not as I do, or, Conformity in scientific networks. https://arxiv.org/abs/1803.09905.
  37. O’Connor, C., & Weatherall, J.O. (2018). The misinformation age: how false beliefs spread. New Haven: Yale University Press. In press.Google Scholar
  38. Olsson, E.J. (2013). A Bayesian simulation model of group deliberation and polarization. Bayesian argumentation. Springer, 113–133.Google Scholar
  39. Oreskes, N. (2004). The scientific consensus on climate change. Science, 306(5702), 1686–1686.CrossRefGoogle Scholar
  40. Oreskes, N., & Conway, E. (2010). Merchants of doubt. New York: Bloomsbury Press.Google Scholar
  41. Rosenstock, S., Bruner, J., O’Connor, C. (2017). In epistemic networks, is less really more? Philosophy of Science, 84(2), 234–252.CrossRefGoogle Scholar
  42. Singer, D.J., Bramson, A., Grim, P., Holman, B., Jung, J., Kovaka, K., Ranginani, A., Berger, W. (2017). Rational social and political polarization.Google Scholar
  43. Steere, A.C., Coburn, J., Glickstein, L. (2004). The emergence of Lyme disease. Journal of Clinical Investigation, 113(8), 1093.CrossRefGoogle Scholar
  44. Steere, A.C., Malawista, S.E., Snydman, D.R., Shope, R.E., Andiman, W.A., Ross, M.R., Steele, F.M. (1977). An epidemic of oligoarticular arthritis in children and adults in three Connecticut communities. Arthritis & Rheumatology, 20 (1), 7–17.CrossRefGoogle Scholar
  45. Steere, A.C., Taylor, E., McHugh, G.L., Logigian, E.L. (1993). The overdiagnosis of Lyme disease. Jama, 269(14), 1812–1816.CrossRefGoogle Scholar
  46. Straubinger, R.K., Straubinger, A.F., Summers, B.A., Jacobson, R.H. (2000). Status of Borrelia burgdorferi infection after antibiotic treatment and the effects of corticosteroids: an experimental study. The Journal of Infectious Diseases, 181(3), 1069–1081.CrossRefGoogle Scholar
  47. Strevens, M. (2003). The role of the priority rule in science. The Journal of Philosophy, 100(2), 55–79.CrossRefGoogle Scholar
  48. Weatherall, J.O., O’Connor, C., Bruner, J. (2018). How to Beat Science and Influence People. The British Journal for the Philosophy of Science. https://arxiv.org/abs/1801.01239.
  49. Zollman, K.J.S. (2007). The communication structure of epistemic communities. Philosophy of Science, 74(5), 574–587.CrossRefGoogle Scholar
  50. Zollman, K.J.S. (2010). The epistemic benefit of transient diversity. Erkenntnis, 72(1), 17.CrossRefGoogle Scholar
  51. Zollman, K.J.S. (2013). Network epistemology: communication in epistemic communities. Philosophy Compass, 8(1), 15–27.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Logic and Philosophy of ScienceUniversity of CaliforniaIrvineUSA

Personalised recommendations