Correlated Voting
Authors: Debmalya Mandal, David C. Parkes
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that when the preferences of voters are positively correlated according to the Kendall-Tau distance, the probability that any scoring rule is not ex post incentive compatible (EPIC) goes to zero exponentially fast with the number of voters, improving over the previously known rate of 1/pn for independent preferences. Motivated by rank-order models from machine learning, we introduce two examples of positively-correlated models, namely Conditional Mallows and Conditional Plackett-Luce. Conditional Mallows satisfies Kendall-Tau correlation and fits our positive result. We also prove that Conditional Plackett-Luce becomes EPIC exponentially quickly. |
| Researcher Affiliation | Academia | Debmalya Mandal dmandal@g.harvard.edu Harvard University David C. Parkes parkes@eecs.harvard.edu Harvard University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention any open-source code for the methodology described. |
| Open Datasets | No | The paper does not involve empirical evaluation on datasets; it focuses on theoretical proofs and mathematical models. Therefore, no dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper does not describe any computational implementation details that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail an experimental setup, hyperparameters, or system-level training settings. |