A simple population protocol for fast robust approximate majority

We describe and analyze a 3-state one-way population protocol to compute approximate majority in the model in which pairs of agents are drawn uniformly at random to interact. Given an initial configuration of x’s, y’s and blanks that contains at least one non-blank, the goal is for the agents to reach consensus on one of the values x or y. Additionally, the value chosen should be the majority non-blank initial value, provided it exceeds the minority by a sufficient margin. We prove that with high probability n agents reach consensus in O(n log n) interactions and the value chosen is the majority provided that its initial margin is at least $${\omega(\sqrt{n} \,{\rm log}\, n)}$$. This protocol has the additional property of tolerating Byzantine behavior in $${o(\sqrt{n})}$$ of the agents, making it the first known population protocol that tolerates Byzantine agents.

[1]  D. Gillespie Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[2]  G. Grimmett,et al.  Probability and random processes , 2002 .

[3]  T. Kurtz Approximation of Population Processes , 1987 .

[4]  David Siegmund,et al.  The theory of optimal stopping , 1991 .

[5]  Noga Alon,et al.  The Probabilistic Method , 2015, Fundamentals of Ramsey Theory.

[6]  J. Gates Introduction to Probability and its Applications , 1992 .

[7]  D. Gillespie A rigorous derivation of the chemical master equation , 1992 .

[8]  R. A. Doney,et al.  4. Probability and Random Processes , 1993 .

[9]  N. Wormald Differential Equations for Random Processes and Random Graphs , 1995 .

[10]  Achour Mostéfaoui,et al.  From Binary Consensus to Multivalued Consensus in asynchronous message-passing systems , 2000, Inf. Process. Lett..

[11]  Paul D. Ezhilchelvan,et al.  Randomized multivalued consensus , 2001, Fourth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing. ISORC 2001.

[12]  Michael J. Fischer,et al.  Computation in networks of passively mobile finite-state sensors , 2004, PODC '04.

[13]  Ali Esmaili,et al.  Probability and Random Processes , 2005, Technometrics.

[14]  David Eisenstat,et al.  The computational power of population protocols , 2006, Distributed Computing.

[15]  James Aspnes,et al.  An Introduction to Population Protocols , 2007, Bull. EATCS.

[16]  David Eisenstat,et al.  Fast computation by population protocols with a leader , 2006, Distributed Computing.

[17]  David Lindley,et al.  CBMS-NSF REGIONAL CONFERENCE SERIES IN APPLIED MATHEMATICS , 2010 .