Why meaning matters for belief diffusion in social networks

It is well known that human beings preferentially adopt beliefs that are consistent with what they already know (1). At its best, this process helps knowledge cumulate, and at its worst facilitates motivated reasoning and pseudoscience. Recent research in social contagion shows that the tendency to treat new information in light of what is already known creates interdependence in the diffusion patterns of simultaneously diffusing beliefs, and that this is sufficient to generate societal polarization and competing worldviews (2-8). This paper explains the mechanisms by which interdependence between beliefs can lead to fundamentally different patterns of adoption than would have occurred under traditional assumptions of independent diffusion. First, when beliefs facilitate one another's adoption, they spread to more individuals than any could have reached spreading on its own. Secondly, as individuals become more alike, they increase their likelihood of exchanging beliefs in the future, and of forming around themselves a faction of like-minded peers. These mechanisms explain why the most popular beliefs tend to be related to one another, and how polarization may spontaneously emerge in homogeneous and well-connected populations. Simulations in this paper make a direct comparison between interdependent and independent diffusion, explaining why the mechanisms of interdependent diffusion reverse many predictions of standard (independent) diffusion models. For example, while independently diffusing beliefs can make a population more homogenous, interdependent diffusion leads the same population to polarize. While the most successful independent beliefs are those with central network positions, interdependent beliefs become popular by facilitating the diffusion of related beliefs.

Name thesis:degree_name

Master

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Houghton, James P. Ph.D. Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Hazhir Rahmandad.

• Sloan School of Management.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/124581

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/124581