Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Strategyproof Voting under Correlated Beliefs
Authors: Daniel Halpern, Rachel Li, Ariel D. Procaccia
NeurIPS 2023 | Venue PDF | 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 EMAIL Rachel Li Harvard University EMAIL Ariel D. Procaccia Harvard University EMAIL |
| 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. |