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Selection and influence in cultural dynamics*

Published online by Cambridge University Press:  26 January 2016

DAVID KEMPE ,
JON KLEINBERG ,
SIGAL OREN  and
ALEKSANDRS SLIVKINS Open the ORCID record for ALEKSANDRS SLIVKINS [Opens in a new window]
DAVID KEMPE
Affiliation:
Department of Computer Science, University of Southern California, Los Angeles CA 90089-0781, USA (e-mail: dkempe@usc.edu)
JON KLEINBERG
Affiliation:
Department of Computer Science, Cornell University, Ithaca NY 14853, USA (e-mail: kleinber@cs.cornell.edu)
SIGAL OREN
Affiliation:
Department of Computer Science, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel (e-mail: sigalo@cs.huji.ac.il)
ALEKSANDRS SLIVKINS
Affiliation:
Microsoft Research, New York, NY 10011, USA (e-mail: slivkins@microsoft.com)
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Abstract

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One of the fundamental principles driving diversity or homogeneity in domains such as cultural differentiation, political affiliation, and product adoption is the tension between two forces: influence (the tendency of people to become similar to others they interact with) and selection (the tendency to be affected most by the behavior of others who are already similar). Influence tends to promote homogeneity within a society, while selection frequently causes fragmentation. When both forces act simultaneously, it becomes an interesting question to analyze which societal outcomes should be expected.

To study this issue more formally, we analyze a natural stylized model built upon active lines of work in political opinion formation, cultural diversity, and language evolution. We assume that the population is partitioned into “types” according to some traits (such as language spoken or political affiliation). While all types of people interact with one another, only people with sufficiently similar types can possibly influence one another. The “similarity” is captured by a graph on types in which individuals of the same or adjacent types can influence one another. We achieve an essentially complete characterization of (stable) equilibrium outcomes and prove convergence from all starting states. We also consider generalizations of this model.

Keywords

social networksselectioninfluenceopinion formation
Type
Research Article
Information
Network Science , Volume 4 , Issue 1 , March 2016 , pp. 1 - 27
Copyright
Copyright © Cambridge University Press 2016 

Footnotes

*

A one-page abstract of this work has appeared in ACM Conf. on Electronic Commerce, 2013.

References

Abrams, D. M. & Strogatz, Steven H. (2003). Modelling the dynamics of language death. Nature, 424, 900.Google Scholar
Anagnostopoulos, A., Kumar, R., & Mahdian, M. (2008). Influence and correlation in social networks. In Proceedings of the 14th ACM sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 7–15.Google Scholar
Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences USA, 106 (51), 21544–21549.Google Scholar
Axelrod, R. (1997). The dissemination of culture. Journal of Conflict Resolution, 41 (2), 203–226.Google Scholar
Bakshy, E., Rosenn, I., Marlow, C. A., & Adamic, L. A. (2012). The role of social networks in information diffusion. In Proceedings of the 21st International World Wide Web Conference.Google Scholar
Ben-Naim, E., Krapivsky, P., & Redner, S. (2003). Bifurcations and patterns in compromise processes. Physica D, 183 (3), 190–204.Google Scholar
Bramoullé, Y., Djebbari, H., & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics, 159, 41–55.Google Scholar
Brandes, U., & Erlebach, T. (eds). (2005). Network analysis: Methodological foundations. Berlin/Heidelberg: Springer-Verlag.Google Scholar
Castellano, C., Fortunato, S., & Loreto, V. (2009). Statistical physics of social dynamics. Reviews of Modern Physics, 81, 591–646.Google Scholar
Centola, D., Gonzalez-Avella, J. C., Eguiluz, V. M., & San Miguel, M. (2007). Homophily, cultural drift, and the co-evolution of cultural groups. Journal of Conflict Resolution, 51 (6), 905–929.Google Scholar
Cohen, J. M. (1977). Sources of peer group homogeneity. Sociology in Education, 50(Oct.), 227–241.Google Scholar
Deffuant, G., Neau, D., Amblard, F., & Weisbuch, G. (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, 3, 87–98.Google Scholar
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486, 75–174. Eprint arXiv: 0906.0612.Google Scholar
Handcock, M. S., Raftery, A. E., & Tantrum, J. M. (2007). Model-based clustering for social networks (with discussion). Journal of the Royal Statistical Society, Series A, 170, 301–354.Google Scholar
Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence: Models, analysis and simulation. Journal of Artificial Societies and Social Simulation, 5 (3), 1–24.Google Scholar
Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97, 1090–1098.Google Scholar
Kandel, D. B. (1978). Homophily, selection, and socialization in adolescent friendships. American Journal of Sociology, 84 (2), 427–436.Google Scholar
Kurtz, T. G. (1970). Solutions of ordinary differential equations as limits of pure jump markov processes. Journal of Applied Probability, 7 (1), 49–58.Google Scholar
LaFond, T., & Neville, J. (2010). Randomization tests for distinguishing social influence and homophily effects. InProceedings of the 19th International World Wide Web Conference, pp. 601–610.Google Scholar
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415–444.Google Scholar
Pariser, E. (2011). The filter bubble: What the internet is hiding from you. New York, NY: Penguin Press.Google Scholar
Patriarca, M., & Leppanen, T. (2004). Modeling language competition. Physica A, 338 (1–2), 296–299.Google Scholar
Poole, K., & Rosenthal, H. (1991). Patterns of congressional voting. American Journal of Political Science, 35 (1), 228–278.Google Scholar
Schaeffer, S. E. (2007). Graph clustering. Computer Science Review, 1 (1), 27–64.Google Scholar
Shalizi, C. R., & Thomas, A. C. (2011). Homophily and contagion are generically confounded in observational social network studies. Sociological Methods and Research, 40, 211–239.Google Scholar
Snijders, T. A. B., Steglich, C., & Schweinberger, M. (2007). Modeling the co-evolution of networks and behavior. In van Montfort, K., Oud, H., & Satorra, A. (Eds.), Longitudinal models in the behavioral and related sciences (pp. 41–71). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Spector, D. (2000). Rational debate and one-dimensional conflict. Quarterly Journal of Economics, 115 (1), 181–200.Google Scholar
Stauffer, D., Castello, X., Eguiluz, V. M., & Miguel, M. S. (2007). Microscopic Abrams-Strogatz model of language competition. Physica A, 374 (2), 835–842.Google Scholar
Steglich, C., Snijders Tom, A.B., & Pearson, M. (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology, 40, 329–393.Google Scholar
Sunstein, C. R. (2009). Republic.com 2.0. Princeton, NJ: Princeton University Press.Google Scholar
Wormald, N. C. (1999). The differential equation method for random graph processes and greedy algorithms. In Karonski, M., & Proemel, H. (Eds.), Lectures on approximation and randomized algorithms (pp. 73–155). PWN.Google Scholar