Video URL

https://old.simons.berkeley.edu/talks/bayesian-learning-social-networks

Bayesian Learning in Social Networks

(2022). Bayesian Learning in Social Networks. The Simons Institute for the Theory of Computing. https://old.simons.berkeley.edu/talks/bayesian-learning-social-networks

Bayesian Learning in Social Networks. The Simons Institute for the Theory of Computing, Oct. 27, 2022, https://old.simons.berkeley.edu/talks/bayesian-learning-social-networks 

          @misc{ scivideos_22844,
            doi = {},
            url = {https://old.simons.berkeley.edu/talks/bayesian-learning-social-networks},
            author = {},
            keywords = {},
            language = {en},
            title = {Bayesian Learning in Social Networks},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2022},
            month = {oct},
            note = {22844 see, \url{https://scivideos.org/simons-institute/22844}}
          }
Ilan Lobel (NYU)
Source Repository Simons Institute
Subject

Abstract

Abstract   This talk will revisit a 2011 Review of Economic Studies paper written with Daron Acemoglu, Munther Dahleh and Asuman Ozdaglar. We consider the canonical social learning model but where observations of past actions are constrained by a social network. The network is generated stochastically and neighborhoods can have arbitrary distributions. We are interested in what kinds of networks and signal structures lead to asymptotic learning (convergence in probability to the correct action). We prove a necessary and sufficient condition for asymptotic learning if signals are of unbounded strength, as well as network properties that allow learning irrespective of the signal structure.