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..
The Distortion of Binomial Voting Defies Expectation
Authors: Yannai A. Gonczarowski, Gregory Kehne, Ariel D. Procaccia, Ben Schiffer, Shirley Zhang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is the design and analysis of a novel and intuitive rule, binomial voting, which provides strong distribution-independent guarantees for both expected distortion and expected welfare. |
| Researcher Affiliation | Academia | Department of Economics and Paulson School of Engineering and Applied Sciences, Harvard University | E-mail: EMAIL. Department of Computer Science, University of Texas at Austin | E-mail: EMAIL. Paulson School of Engineering and Applied Sciences, Harvard University | E-mail: EMAIL. Department of Statistics, Harvard University | E-mail: EMAIL. Paulson School of Engineering and Applied Sciences, Harvard University | E-mail: EMAIL. |
| Pseudocode | No | The paper describes voting rules and their theoretical properties but does not provide pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not mention using or providing access to any specific datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details or hyperparameters. |