Persuading Voters: It's Easy to Whisper, It's Hard to Speak Loud
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Abstract (arXiv)
We focus on the following natural question: is it possible to influence the outcome of a voting process through the strategic provision of information to voters who update their beliefs rationally? We investigate whether it is computationally tractable to design a signaling scheme maximizing the probability with which the sender's preferred candidate is elected. We focus on the model recently introduced by Arieli and Babichenko (2019) (i.e., without inter-agent externalities), and consider, as explanatory examples, k-voting rule and plurality voting. There is a sharp contrast between the case in which private signals are allowed and the more restrictive setting in which only public signals are allowed. In the former, we show that an optimal signaling scheme can be computed efficiently both under a k-voting rule and plurality voting. In establishing these results, we provide two general (i.e., applicable to settings beyond voting) contributions. Specifically, we extend a well known result by Dughmi and Xu (2017) to more general settings, and prove that, when the sender's utility function is anonymous, computing an optimal signaling scheme is fixed parameter tractable w.r.t. the number of receivers' actions. In the public signaling case, we show that the sender's optimal expected return cannot be approximated to within any factor under a k-voting rule. This negative result easily extends to plurality voting and problems where utility functions are anonymous.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Aug 2019.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 1.
4 papers.
- 2021Multi-Receiver Online Bayesian Persuasioncited by 12
- 2022Sequential Information Design: Learning to Persuade in the Darkcited by 9
- 2024Markov Persuasion Processes: Learning to Persuade from Scratchcited by 8
- 2021Algorithmic Information Design in Multi-Player Games: Possibility and Limits in Singleton Congestioncited by 0
- 2024Markov Persuasion Processes: Learning to Persuade from Scratch5 Feb 2024
- 2022Sequential Information Design: Learning to Persuade in the Dark8 Sep 2022
- 2021Algorithmic Information Design in Multi-Player Games: Possibility and Limits in Singleton Congestion25 Sep 2021
- 2021Multi-Receiver Online Bayesian Persuasion11 Jun 2021