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Reaching Consensus via Non-Bayesian Asynchronous Learning in Social Networks
2014
International Workshop on Approximation Algorithms for Combinatorial Optimization
We study the outcomes of information aggregation in online social networks. Our main result is that networks with certain realistic structural properties avoid information cascades and enable a population to effectively aggregate information. In our model, each individual in a network holds a private, independent opinion about a product or idea, biased toward a ground truth. Individuals declare their opinions asynchronously, can observe the stated opinions of their neighbors, and are free to
doi:10.4230/lipics.approx-random.2014.192
dblp:conf/approx/FeldmanILW14
fatcat:hisdkir27zfjlfjeyvc4uhjyj4