Communication complexity of approximating voting rules
Julie A. Adams
Abstract
Julie A. Adams
Abstract
This paper considers the communication complexity of approximating common voting rules. Both upper and lower bounds are presented. For n voters and m alternatives, it is shown that for all e e (0, 1), the communication complexity of obtaining a 1 - e approximation to Borda is O(log (1/e)nm). A lower bound of Ω(nm) is provided for fixed small values of e. The communication complexity of computing the true Borda winner is Ω(nm log(m)) [5]. Thus, in the case of Borda, one can obtain arbitrarily good approximations with less communication overhead than is required to compute the true Borda winner.For other voting rules, no such 1±e approximation scheme exists. In particular, it is shown that the communication complexity of computing any constant factor approximation, ρ, to Bucklin is Ω(nm/ρ2). Conitzer and Sandholm [5] show that the communication complexity of computing the true Bucklin winner is O(nm). However, we show that for all δ e (0, 1), the communication complexity of computing a mδ approximate winner in Bucklin elections is O(nm1 - δ log(m)). For δ e (1/2, 1), a lower bound of ω(nm1-2δ) is also provided.Similar lower bounds are presented on the communication complexity of computing approximate winners in Copeland elections.
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This paper considers the communication complexity of approximating common voting rules. Both upper and lower bounds are presented. For n voters and m alternatives, it is shown that for all e e (0, 1), the communication complexity of obtaining a 1 - e approximation to Borda is O(log (1/e)nm). A lower bound of Ω(nm) is provided for fixed small values of e. The communication complexity of computing the true Borda winner is Ω(nm log(m)) [5]. Thus, in the case of Borda, one can obtain arbitrarily good approximations with less communication overhead than is required to compute the true Borda winner.For other voting rules, no such 1±e approximation scheme exists. In particular, it is shown that the communication complexity of computing any constant factor approximation, ρ, to Bucklin is Ω(nm/ρ2). Conitzer and Sandholm [5] show that the communication complexity of computing the true Bucklin winner is O(nm). However, we show that for all δ e (0, 1), the communication complexity of computing a mδ approximate winner in Bucklin elections is O(nm1 - δ log(m)). For δ e (1/2, 1), a lower bound of ω(nm1-2δ) is also provided.Similar lower bounds are presented on the communication complexity of computing approximate winners in Copeland elections.
Key concepts: Communication complexity, Upper and lower bounds, Computational complexity theory, Voting, Binary logarithm, Overhead (engineering), Log-log plot, Computer science