Distributed Protocols for Leader Election: A Game-Theoretic Perspective

被引:11
|
作者
Abraham, Ittai [1 ]
Dolev, Danny [2 ]
Halpern, Joseph Y. [3 ]
机构
[1] VMWARE Res, Herliya, Israel
[2] Hebrew Univ Jerusalem, Sch Comp Sci & Engn, Jerusalem, Israel
[3] Cornell Univ, Comp Sci Dept, Ithaca, NY 14853 USA
关键词
Leader election; ex post Nash equilibrium; ALGORITHM; COMMUNICATION; TALK;
D O I
10.1145/3303712
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
We do a game-theoretic analysis of leader election, under the assumption that each agent prefers to have some leader than no leader at all. We show that it is possible to obtain a fair Nash equilibrium, where each agent has an equal probability of being elected leader, in a completely connected network, in a bidirectional ring, and a unidirectional ring, in the synchronous setting. In the asynchronous setting, Nash equilibrium is not quite the right solution concept. Rather, we must consider ex post Nash equilibrium; this means that we have a Nash equilibrium no matter what a scheduling adversary does. We show that ex post Nash equilibrium is attainable in the asynchronous setting in all the networks we consider, using a protocol with bounded running time. However, in the asynchronous setting, we require that n > 2. We show that we can get a fair ex post epsilon-Nash equilibrium if n = 2 in the asynchronous setting under some cryptographic assumptions (specifically, the existence of a one-way functions), using a commitment protocol. We then generalize these results to a setting where we can have deviations by a coalition of size k. In this case, we can get what we call a fair k-resilient equilibrium in a completely connected network if n > 2k; under the same cryptographic assumptions, we can a get a k-resilient equilibrium in a completely connected network, unidirectional ring, or bidirectional ring if n > k. Finally, we show that under minimal assumptions, not only do our protocols give a Nash equilibrium, they also give a sequential equilibrium, so players even play optimally off the equilibrium path.
引用
收藏
页数:26
相关论文
共 50 条
  • [1] Distributed Protocols for Leader Election: A Game-Theoretic Perspective
    Abraham, Ittai
    Dolev, Danny
    Halpern, Joseph Y.
    [J]. DISTRIBUTED COMPUTING, 2013, 8205 : 61 - 75
  • [2] Game-Theoretic Fairness Meets Multi-party Protocols: The Case of Leader Election
    Chung, Kai-Min
    Chan, T-H Hubert
    Wen, Ting
    Shi, Elaine
    [J]. ADVANCES IN CRYPTOLOGY - CRYPTO 2021, PT II, 2021, 12826 : 3 - 32
  • [3] A Game-Theoretic Analysis for Distributed Honeypots
    Li, Yang
    Shi, Leyi
    Feng, Haijie
    [J]. FUTURE INTERNET, 2019, 11 (03)
  • [4] Distributed Game-Theoretic Vertex Coloring
    Chatzigiannakis, Ioannis
    Koninis, Christos
    Panagopoulou, Panagiota N.
    Spirakis, Paul G.
    [J]. PRINCIPLES OF DISTRIBUTED SYSTEMS, 2010, 6490 : 103 - +
  • [5] CALCULUS OF CONSENT - A GAME-THEORETIC PERSPECTIVE
    SCHWEIZER, U
    [J]. JOURNAL OF INSTITUTIONAL AND THEORETICAL ECONOMICS-ZEITSCHRIFT FUR DIE GESAMTE STAATSWISSENSCHAFT, 1990, 146 (01): : 28 - 54
  • [6] Workload Factoring: A Game-Theoretic Perspective
    Nahir, Amir
    Orda, Ariel
    Raz, Danny
    [J]. IEEE-ACM TRANSACTIONS ON NETWORKING, 2015, 23 (06) : 1998 - 2009
  • [7] A Game-Theoretic Perspective on Coalition Formation
    Dimitrov, Dinko
    [J]. JOURNAL OF INSTITUTIONAL AND THEORETICAL ECONOMICS-ZEITSCHRIFT FUR DIE GESAMTE STAATSWISSENSCHAFT, 2008, 164 (04): : 778 - 780
  • [8] A Game-Theoretic Perspective on Advance Reservations
    Simhon, Eran
    Starobinski, David
    [J]. IEEE NETWORK, 2016, 30 (02): : 6 - 11
  • [9] A Game-theoretic Perspective on Communication for Omniscience
    Ding, Ni
    Chan, Chung
    Liu, Tie
    Kennedy, Rodney A.
    Sadeghi, Parastoo
    [J]. 2016 AUSTRALIAN COMMUNICATIONS THEORY WORKSHOP (AUSCTW), 2016, : 95 - 100
  • [10] Distributed Recurrent Neural Networks for Cooperative Control of Manipulators: A Game-Theoretic Perspective
    Li, Shuai
    He, Jinbo
    Li, Yangming
    Rafique, Muhammad Usman
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 28 (02) : 415 - 426