Strategyproof Voting under Correlated Beliefs
Authors: Daniel Halpern, Rachel Li, Ariel D. Procaccia
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In voting theory, when voters have ranked preferences over candidates, the celebrated Gibbard-Satterthwaite Theorem essentially rules out the existence of reasonable strategyproof methods for picking a winner. ... Our contributions. We begin by presenting various classes of beliefs induced by classic probabilistic social choice models such as the Mallows [15], Thurstone-Mosteller [23, 18], and Placket Luce [20, 12] models. ... Next, we provide a negative result: Among positional scoring rules (where each voter assigns a fixed score to each position in their ranking), plurality is unique in being OBIC when voters have Mallows beliefs. |
| Researcher Affiliation | Academia | Daniel Halpern Harvard University dhalpern@g.harvard.edu Rachel Li Harvard University rachelli@college.harvard.edu Ariel D. Procaccia Harvard University arielpro@seas.harvard.edu |
| Pseudocode | No | The paper is theoretical and mathematical, presenting definitions, lemmas, and theorems. It does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention or provide any links to open-source code for the methodology described. |
| Open Datasets | No | The paper is a theoretical work in voting theory and does not use datasets for training, validation, or testing. |
| Dataset Splits | No | The paper is a theoretical work and does not involve empirical evaluation on datasets that would require training, validation, or test splits. |
| Hardware Specification | No | The paper is a theoretical work and does not describe any computational experiments or the hardware used to run them. |
| Software Dependencies | No | The paper is theoretical and mathematical. It does not list any specific software or library dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe a computational experiment setup, hyperparameters, or training details. |