Persuading Voters in District-based Elections
Matteo Castiglioni, Nicola Gatti
摘要
We focus on the scenario in which an agent can exploit his information advantage to manipulate the outcome of an election. In particular, we study district-based elections with two candidates, in which the winner of the election is the candidate that wins in the majority of the districts. District-based elections are adopted worldwide (e.g., UK and USA) and are a natural extension of widely studied voting mechanisms (e.g., k-voting and plurality voting). We resort to the Bayesian persuasion framework, where the manipulator (sender) strategically discloses information to the voters (receivers) that update their beliefs rationally. We study both private signaling, in which the sender can use a private communication channel per receiver, and public signaling, in which the sender can use a single communication channel for all the receivers. Furthermore, for the first time, we introduce semi-public signaling in which the sender can use a single communication channel per district. We show that there is a sharp distinction between private and (semi-)public signaling. In particular, optimal private signaling schemes can provide an arbitrarily better probability of victory than (semi-)public ones and can be computed efficiently, while optimal (semi-)public signaling schemes cannot be approximated to within any factor in polynomial time unless P = NP. However, we show that reasonable relaxations allow the design of multi-criteria PTASs for optimal (semi-)public signaling schemes. In doing so, we introduce a novel property, namely comparative stability, and we design a bi-criteria PTAS for public signaling in general Bayesian persuasion problems beyond elections when the sender's utility function is state-dependent.
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引用它的顶会 Paper8
- Multi-Receiver Online Bayesian PersuasionMatteo Castiglioni, Alberto Marchesi, Andrea Celli, Nicola GattiICML 2021 · 被引用 36 次
- Optimal Rates and Efficient Algorithms for Online Bayesian PersuasionMartino Bernasconi, Matteo Castiglioni, Andrea Celli, Alberto Marchesi 等ICML 2023 · 被引用 26 次
- Online Bayesian PersuasionMatteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola GattiNeurIPS 2020 · 被引用 26 次
- Sequential Information Design: Learning to Persuade in the DarkMartino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti 等NeurIPS 2022 · 被引用 19 次
- Signaling in Posted Price AuctionsMatteo Castiglioni, Giulia Romano, Alberto Marchesi, Nicola GattiAAAI 2022 · 被引用 14 次
它引用的顶会 Paper3
- Persuading Voters: It's Easy to Whisper, It's Hard to Speak LoudMatteo Castiglioni, Andrea Celli, Nicola GattiAAAI 2020 · 被引用 31 次
- Election Control in Social Networks via Edge Addition or RemovalMatteo Castiglioni, Diodato Ferraioli, Nicola GattiAAAI 2020 · 被引用 27 次
- On the Tractability of Public Persuasion with No ExternalitiesHaifeng XuSODA 2020 · 被引用 22 次
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