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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

Information

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.

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