License: Creative Commons Attribution 3.0 Unported license (CC-BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ICALP.2020.8
URN: urn:nbn:de:0030-drops-124156
URL: https://drops.dagstuhl.de/opus/volltexte/2020/12415/
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Bahrani, Maryam ; Immorlica, Nicole ; Mohan, Divyarthi ; Weinberg, S. Matthew

Asynchronous Majority Dynamics in Preferential Attachment Trees

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LIPIcs-ICALP-2020-8.pdf (0.5 MB)


Abstract

We study information aggregation in networks where agents make binary decisions (labeled incorrect or correct). Agents initially form independent private beliefs about the better decision, which is correct with probability 1/2+δ. The dynamics we consider are asynchronous (each round, a single agent updates their announced decision) and non-Bayesian (agents simply copy the majority announcements among their neighbors, tie-breaking in favor of their private signal). Our main result proves that when the network is a tree formed according to the preferential attachment model [Barabási and Albert, 1999], with high probability, the process stabilizes in a correct majority within O(n log n/log log n) rounds. We extend our results to other tree structures, including balanced M-ary trees for any M.

BibTeX - Entry

@InProceedings{bahrani_et_al:LIPIcs:2020:12415,
  author =	{Maryam Bahrani and Nicole Immorlica and Divyarthi Mohan and S. Matthew Weinberg},
  title =	{{Asynchronous Majority Dynamics in Preferential Attachment Trees}},
  booktitle =	{47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)},
  pages =	{8:1--8:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-138-2},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{168},
  editor =	{Artur Czumaj and Anuj Dawar and Emanuela Merelli},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2020/12415},
  URN =		{urn:nbn:de:0030-drops-124156},
  doi =		{10.4230/LIPIcs.ICALP.2020.8},
  annote =	{Keywords: Opinion Dynamics, Information Cascades, Preferential Attachment, Majority Dynamics, non-Bayesian Asynchronous Learning, Stochastic Processes}
}

Keywords: Opinion Dynamics, Information Cascades, Preferential Attachment, Majority Dynamics, non-Bayesian Asynchronous Learning, Stochastic Processes
Collection: 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)
Issue Date: 2020
Date of publication: 29.06.2020


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