Voting with Preference Intensities
Authors: Anson Kahng, Mohamad Latifian, Nisarg Shah
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design near-optimal voting rules which aggregate such preference rankings with intensities using the recently-popular distortion framework. We also show that traditional voting rules, which aggregate preference rankings while ignoring (or not eliciting) intensities, can incur significant welfare loss. |
| Researcher Affiliation | Academia | Anson Kahng1, Mohamad Latifian2, Nisarg Shah2 1 University of Rochester 2 University of Toronto akahng2@cs.rochester.edu, latifian@cs.toronto.edu, nisarg@cs.toronto.edu |
| Pseudocode | No | Not found. The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | Not found. The paper is theoretical and does not mention providing open-source code for its methodology or analysis. |
| Open Datasets | No | Not applicable. The paper is theoretical and does not involve datasets or empirical evaluation. |
| Dataset Splits | No | Not applicable. The paper is theoretical and does not involve datasets or empirical evaluation. |
| Hardware Specification | No | Not applicable. The paper is theoretical and does not describe computational experiments that would require specific hardware. |
| Software Dependencies | No | Not applicable. The paper is theoretical and does not describe computational experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | Not applicable. The paper is theoretical and does not describe any experimental setup or hyperparameters. |