On indigenous random consensus and averaging dynamics

Behrouz Touri, Cedric Langbort

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We study indigenously evolving random averaging dynamics, i.e., random averaging dynamics whose evolution depends on the history of the random dynamics itself. Such dynamical processes find applications in, e.g., models of distributed learning of comparative adjectives in Linguistics, asymmetric state-dependent random gossiping in Computer Science, Hegselmann-Krause opinion dynamics with link-failure and/or random observation radius in Social Sciences, to name just a few. We introduce a novel supermartingale technique to analyze such history-dependent random dynamics. Using this new tool, we show that an adapted random averaging dynamics converges under general conditions and provide a characterization for the asymptotic behavior of such dynamics.

Original languageEnglish (US)
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781467357173
StatePublished - Jan 1 2013
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: Dec 10 2013Dec 13 2013

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216


Other52nd IEEE Conference on Decision and Control, CDC 2013

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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