Persuading Voters in District-based Elections
Authors: Matteo Castiglioni, Nicola Gatti5244-5251
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the efficiency and complexity of signaling in district-based elections with two candidates. First, we compare private, public, and semi-public signaling schemes in terms of efficiency when used to manipulate elections, showing that private signaling schemes perform arbitrarily better than (semi-)public schemes. Then, we show that optimal private signaling schemes can be computed efficiently, while the direct use of the results provided by Castiglioni, Celli, and Gatti (2020a) shows that the problem is inapproximable with (semi-)public signaling. However, we prove that multi-criteria Polynomial-Time Approximation Schemes (PTASs) for public and semi-public signaling schemes are possible when some relaxations are made. |
| Researcher Affiliation | Academia | Matteo Castiglioni, Nicola Gatti Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133, Milan, Italy matteo.castiglioni@polimi.it, nicola.gatti@polimi.it |
| Pseudocode | No | The paper presents linear programming formulations (LP (1), LP (4)) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific statements or links indicating the release of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe empirical experiments involving datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, thus no information on training, validation, or test dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments requiring specific software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no specific details about an experimental setup, such as hyperparameters or training configurations, are provided. |