Abstract
Scientists are generally subject to social pressures, including pressures to conform with others in their communities, that affect achievement of their epistemic goals. Here we analyze a network epistemology model in which agents, all else being equal, prefer to take actions that conform with those of their neighbors. This preference for conformity interacts with the agents’ beliefs about which of two (or more) possible actions yields the better result. We find a range of possible outcomes, including stable polarization in belief and action. The model results are sensitive to network structure. In general, though, conformity has a negative effect on a community’s ability to reach accurate consensus about the world.
Keywords
Conformity Epistemic networks Small worlds False beliefsNotes
Acknowledgements
This paper is partially based upon work supported by the National Science Foundation under Grant No. 1535139. We are grateful to Jeff Barrett, Aydin Mohseni, and Mike Schneider for helpful conversations related to this manuscript.
References
- Asch, S. E., & Guetzkow, H. (1951). Effects of group pressure upon the modification and distortion of judgments. In H. Guetzkow (Ed.), Groups, leadership, and men (pp. 222–236). Pittsburgh: Carnegie Press.Google Scholar
- Bala, V., & Goyal, S. (1998). Learning from neighbors. Review of Economic Studies, 65(3), 595–621.CrossRefGoogle Scholar
- Banerjee, A. V. (1992). A simple model of herd behavior. The Quarterly Journal of Economics, 107(3), 797–817.CrossRefGoogle Scholar
- Baron, R. S., Vandello, J. A., & Brunsman, B. (1996). The forgotten variable in conformity research: Impact of task importance on social influence. Journal of Personality and Social Psychology, 71(5), 915.CrossRefGoogle Scholar
- Berger, S., Feldhaus, C., & Ockenfels, A. (2018). A shared identity promotes herding in an information cascade game. Journal of the Economic Science Association, 4(1), 63–72.CrossRefGoogle Scholar
- Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992–1026.CrossRefGoogle Scholar
- Bikhchandani, S., Hirshleifer, D., & Welch, I. (1998). Learning from the behavior of others: Conformity, fads, and informational cascades. The Journal of Economic Perspectives, 12(3), 151–170.CrossRefGoogle Scholar
- Bond, R., & Smith, P. B. (1996). Culture and conformity: A meta-analysis of studies using Asch’s (1952b, 1956) line judgment task. Psychological Bulletin, 119(1), 111.CrossRefGoogle Scholar
- Borg, A., Frey, D., Šešelja, D., & Straßer, C. (2017). Examining network effects in an argumentative agent-based model of scientific inquiry. In International workshop on logic, rationality and interaction (pp. 391–406). Berlin: Springer.Google Scholar
- Bramson, A., Grim, P., Singer, D. J., Berger, W. J., Sack, G., Fisher, S., et al. (2017). Understanding polarization: Meanings, measures, and model evaluation. Philosophy of Science, 84(1), 115–159.CrossRefGoogle Scholar
- Carter, K. C. (2017). Childbed fever: A scientific biography of Ignaz Semmelweis. London: Routledge.CrossRefGoogle Scholar
- Colombo, L., Femminis, G., & Pavan, A. (2014). Information acquisition and welfare. The Review of Economic Studies, 81(4), 1438–1483.CrossRefGoogle Scholar
- Condorcet, M. D. (1785). Essai sur l’application de l’analyse à la probabilité des décisions rendues à la pluralité des voix.Google Scholar
- Egebark, J., & Ekström, M. (2011). Like what you like or like what others like? conformity and peer effects on Facebook.Google Scholar
- Erdös, P., & Rényi, A. (1959). On random graphs I. Publicationes Mathematicae Debrecen, 6, 290–297.Google Scholar
- Flaxman, S., Goel, S., & Rao, J. M. (2016). Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly, 80(S1), 298–320.CrossRefGoogle Scholar
- Frey, D., & Şeşelja, D. (2018). What is the epistemic function of highly idealized agent-based models of scientific inquiry? Philosophy of the Social Sciences, 48(4), 407–433.CrossRefGoogle Scholar
- Frey, D., & Šešelja, D. (2019). Robustness and idealizations in agent-based models of scientific interaction. The British Journal for the Philosophy of Science. https://doi.org/10.1093/bjps/axy039.
- Gilbert, E. N. (1959). Random graphs. The Annals of Mathematical Statistics, 30(4), 1141–1144.CrossRefGoogle Scholar
- Grundy, I. (1999). Lady Mary Wortley Montagu. Oxford: Clarendon Press.Google Scholar
- Hellwig, C., & Veldkamp, L. (2009). Knowing what others know: Coordination motives in information acquisition. The Review of Economic Studies, 76(1), 223–251.CrossRefGoogle Scholar
- Holman, B., & Bruner, J. (2017). Experimentation by industrial selection. Philosophy of Science, 84(5), 1008–1019.CrossRefGoogle Scholar
- Holman, B., & Bruner, J. P. (2015). The problem of intransigently biased agents. Philosophy of Science, 82(5), 956–968.CrossRefGoogle Scholar
- Imbert, C., Boyer-Kassem, T., Chevrier, V., & Bourjot, C. (2019). Improving deliberations by reducing misrepresentation effects. Episteme, 3, 1–17.Google Scholar
- Kummerfeld, E., & Zollman, K. J. (2015). Conservatism and the scientific state of nature. The British Journal for the Philosophy of Science, 67(4), 1057–1076.CrossRefGoogle Scholar
- Mayo-Wilson, C., Zollman, K. J., & Danks, D. (2011). The independence thesis: When individual and social epistemology diverge. Philosophy of Science, 78(4), 653–677.CrossRefGoogle Scholar
- Mohseni, A., & Williams, C. R. (2017). Truth and conformity on networks (working paper).Google Scholar
- Myatt, D. P., & Wallace, C. (2011). Endogenous information acquisition in coordination games. The Review of Economic Studies, 79(1), 340–374.CrossRefGoogle Scholar
- Newman, M. E. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404–409.CrossRefGoogle Scholar
- O’Connor, C., & Weatherall, J. O. (2017). Scientific polarization. arXiv:1712.04561 [cs.SI].
- O’Connor, C., & Weatherall, J. O. (2019). The misinformation age: How false beliefs spread. New Haven: Yale University Press.CrossRefGoogle Scholar
- Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., et al. (2007). Structure and tie strengths in mobile communication networks. Proceedings of the National Academy of Sciences, 104(18), 7332–7336.CrossRefGoogle Scholar
- Pariser, E. (2011). The filter bubble: How the new personalized web is changing what we read and how we think. New York: Penguin.Google Scholar
- Rosenstock, S., Bruner, J., & O’Connor, C. (2017). In epistemic networks, is less really more? Philosophy of Science, 84(2), 234–252.CrossRefGoogle Scholar
- Semmelweis, I. F. (1983). The etiology, concept, and prophylaxis of childbed fever. No. 2. Madison: University of Wisconsin Press.Google Scholar
- Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.CrossRefGoogle Scholar
- Weatherall, J. O., O’Connor, C., & Bruner, J. (2017). How to beat science and influence people. arXiv:1801.01239 [cs.SI].
- Zollman, K. J. (2007). The communication structure of epistemic communities. Philosophy of Science, 74(5), 574–587.CrossRefGoogle Scholar
- Zollman, K. J. (2010a). The epistemic benefit of transient diversity. Erkenntnis, 72(1), 17.CrossRefGoogle Scholar
- Zollman, K. J. S. (2010b). Social structure and the effects of conformity. Synthese, 172(3), 317–340.CrossRefGoogle Scholar