A Tight Analysis of the Parallel Undecided-State Dynamics with Two Colors

Authors Andrea Clementi, Mohsen Ghaffari, Luciano Gualà, Emanuele Natale, Francesco Pasquale, Giacomo Scornavacca

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Andrea Clementi
  • Università Tor Vergata di Roma, Italy
Mohsen Ghaffari
  • ETH Zürich, Switzerland
Luciano Gualà
  • Università Tor Vergata di Roma, Italy
Emanuele Natale
  • Max Planck Institute for Informatics, Germany
Francesco Pasquale
  • Università Tor Vergata di Roma, Italy
Giacomo Scornavacca
  • Università degli Studi dell'Aquila, Italy

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Andrea Clementi, Mohsen Ghaffari, Luciano Gualà, Emanuele Natale, Francesco Pasquale, and Giacomo Scornavacca. A Tight Analysis of the Parallel Undecided-State Dynamics with Two Colors. In 43rd International Symposium on Mathematical Foundations of Computer Science (MFCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 117, pp. 28:1-28:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


The Undecided-State Dynamics is a well-known protocol for distributed consensus. We analyze it in the parallel PULL communication model on the complete graph with n nodes for the binary case (every node can either support one of two possible colors, or be in the undecided state). An interesting open question is whether this dynamics is an efficient Self-Stabilizing protocol, namely, starting from an arbitrary initial configuration, it reaches consensus quickly (i.e., within a polylogarithmic number of rounds). Previous work in this setting only considers initial color configurations with no undecided nodes and a large bias (i.e., Theta(n)) towards the majority color. In this paper we present an unconditional analysis of the Undecided-State Dynamics that answers to the above question in the affirmative. We prove that, starting from any initial configuration, the process reaches a monochromatic configuration within O(log n) rounds, with high probability. This bound turns out to be tight. Our analysis also shows that, if the initial configuration has bias Omega(sqrt(n log n)), then the dynamics converges toward the initial majority color, with high probability.

Subject Classification

ACM Subject Classification
  • Theory of computation → Distributed algorithms
  • Distributed Consensus
  • Self-Stabilization
  • PULL Model
  • Markov Chains


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