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 [1].
Incentives in Social Decision Schemes with Pairwise Comparison Preferences
Authors: Felix Brandt, Patrick Lederer, Warut Suksompong
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In particular, we settle three open questions raised by Brandt [2017]: (i) there is no Condorcetconsistent SDS that satisfies PC-strategyproofness; (ii) there is no anonymous and neutral SDS that satisfies PC-efficiency and PC-strategyproofness; and (iii) there is no anonymous and neutral SDS that satisfies PC-efficiency and strict PC-participation. All three impossibilities require m 4 alternatives and turn into possibilities when m 3. ... Proof sketch. |
| Researcher Affiliation | Academia | Felix Brandt1 , Patrick Lederer1 , Warut Suksompong2 1Technische Universit at M unchen 2National University of Singapore EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use empirical datasets; thus, no access information for a public dataset is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical data or dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, such as hyperparameters or training settings. |