Persuading Voters: It’s Easy to Whisper, It’s Hard to Speak Loud
Authors: Matteo Castiglioni, Andrea Celli, Nicola Gatti1870-1877
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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 resort to the model recently introduced by Arieli and Babichenko (2019) (i.e., without inter-agent externalities), and focus on, as illustrative examples, k-voting rules 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 contributions applicable to general settings beyond voting. 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 in 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. |
| Researcher Affiliation | Academia | Matteo Castiglioni, Andrea Celli, Nicola Gatti Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133, Milan, Italy name.surname@polimi.it |
| Pseudocode | No | The paper contains mathematical formulations and proofs, but no clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository links, explicit release statements) for source code related to the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use datasets for empirical training. It defines abstract model components like 'states of nature' and 'voters' but does not refer to external or publicly available empirical datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments that would require training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementation details with specific version numbers for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not conduct empirical experiments requiring an experimental setup with hyperparameters or training configurations. |