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dc.contributor.advisorHazhir Rahmandad.en_US
dc.contributor.authorHoughton, James P. Ph.D. Massachusetts Institute of Technology.en_US
dc.contributor.otherSloan School of Management.en_US
dc.date.accessioned2020-04-13T18:26:42Z
dc.date.available2020-04-13T18:26:42Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/124581
dc.descriptionThesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, 2019en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 13-14).en_US
dc.description.abstractIt 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.en_US
dc.description.statementofresponsibilityby James P. Houghton.en_US
dc.format.extent37 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectSloan School of Management.en_US
dc.titleWhy meaning matters for belief diffusion in social networksen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Management Researchen_US
dc.contributor.departmentSloan School of Managementen_US
dc.identifier.oclc1149013620en_US
dc.description.collectionS.M.inManagementResearch Massachusetts Institute of Technology, Sloan School of Managementen_US
dspace.imported2020-04-13T18:26:12Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentSloanen_US


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