TY - GEN
T1 - Reaching consensus via non-bayesian asynchronous learning in social networks
AU - Feldman, Michal
AU - Immorlica, Nicole
AU - Lucier, Brendan
AU - Weinberg, S. Matthew
N1 - Publisher Copyright: © Moran Feldman and Rani Izsak.
PY - 2014/9/1
Y1 - 2014/9/1
N2 - 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 update their declarations over time. Supposing that individuals conform with the majority report of their neighbors, we ask whether the population will eventually arrive at consensus on the ground truth. We show that the answer depends on the network structure: there exist networks for which consensus is unlikely, or for which declarations converge on the incorrect opinion with positive probability. On the other hand, we prove that for networks that are sparse and expansive, the population will converge to the correct opinion with high probability.
AB - 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 update their declarations over time. Supposing that individuals conform with the majority report of their neighbors, we ask whether the population will eventually arrive at consensus on the ground truth. We show that the answer depends on the network structure: there exist networks for which consensus is unlikely, or for which declarations converge on the incorrect opinion with positive probability. On the other hand, we prove that for networks that are sparse and expansive, the population will converge to the correct opinion with high probability.
KW - Expander Graphs
KW - Information Cascades
KW - Non-Bayesian Asynchronous Learning
KW - Social Networks
KW - Stochastic Processes
UR - http://www.scopus.com/inward/record.url?scp=84920179361&partnerID=8YFLogxK
U2 - https://doi.org/10.4230/LIPIcs.APPROX-RANDOM.2014.192
DO - https://doi.org/10.4230/LIPIcs.APPROX-RANDOM.2014.192
M3 - منشور من مؤتمر
T3 - Leibniz International Proceedings in Informatics, LIPIcs
SP - 192
EP - 208
BT - Leibniz International Proceedings in Informatics, LIPIcs
A2 - Jansen, Klaus
A2 - Rolim, Jose D. P.
A2 - Devanur, Nikhil R.
A2 - Moore, Cristopher
T2 - 17th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2014 and the 18th International Workshop on Randomization and Computation, RANDOM 2014
Y2 - 4 September 2014 through 6 September 2014
ER -