The Distortion of Binomial Voting Defies Expectation
Authors: Yannai A. Gonczarowski, Gregory Kehne, Ariel D. Procaccia, Ben Schiffer, Shirley Zhang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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: yannai@gonch.name. Department of Computer Science, University of Texas at Austin | E-mail: gkehne@utexas.edu. Paulson School of Engineering and Applied Sciences, Harvard University | E-mail: arielpro@seas.harvard.edu. Department of Statistics, Harvard University | E-mail: bschiffer1@g.harvard.edu. Paulson School of Engineering and Applied Sciences, Harvard University | E-mail: szhang2@g.harvard.edu. |
| 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. |