23044

Video URL

https://old.simons.berkeley.edu/talks/social-learning-and-sample-herding-networks-homophily

Social Learning and Sample Herding in Networks with Homophily

(2022). Social Learning and Sample Herding in Networks with Homophily. The Simons Institute for the Theory of Computing. https://old.simons.berkeley.edu/talks/social-learning-and-sample-herding-networks-homophily

Social Learning and Sample Herding in Networks with Homophily. The Simons Institute for the Theory of Computing, Dec. 01, 2022, https://old.simons.berkeley.edu/talks/social-learning-and-sample-herding-networks-homophily 

          @misc{ scivideos_23044,
            doi = {},
            url = {https://old.simons.berkeley.edu/talks/social-learning-and-sample-herding-networks-homophily},
            author = {},
            keywords = {},
            language = {en},
            title = {Social Learning and Sample Herding in Networks with Homophily},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2022},
            month = {dec},
            note = {23044 see, \url{https://scivideos.org/simons-institute/23044}}
          }
Matt Jackson (Stanford)
Talk number23044
Source RepositorySimons Institute
Subject

Abstract

Other peoples' experiences serve as primary sources of information about the potential payoffs to various available opportunities. Homophily in social networks affects both the quality and diversity of information to which people have access.On the one hand, homophily provides higher quality information since observing the experiences of another person is more informative as that person is more similar to the decision maker. On the other hand, homophily lowers the variety of actions about which people can learn when a group ends up herding on specific actions about which they have better information. This can lead to inefficiencies and inequalities across groups, as we show. Homophily lowers efficiency and increases inequality in sparse networks, while enhancing efficiency and decreasing inequality in denser networks. We characterize conditions under which groups herd on separate actions, and show how such homophily-induced herding driven by limited scope of information differs from standard forms of herding driven by cascading inferences.