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..
Incentive-Compatible Classification
Authors: Yakov Babichenko, Oren Dean, Moshe Tennenholtz7055-7062
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give upper bounds (impossibilities) and lower bounds (mechanisms) on the worst-case coincidence between the classification of an IC mechanism and the ideal α-classification. We prove bounds which depend on α and on the maximal number of reviews given by a single agent, Δ. Our results show that it is harder to find a good mechanism when α is smaller and Δ is larger. In particular, if Δ is unbounded, then the best mechanism is trivial (that is, it does not take into account the reviews). On the other hand, when Δ is sublinear in the number of agents, we give a simple, natural mechanism, with a coincidence ratio of α. |
| Researcher Affiliation | Academia | Yakov Babichenko, Oren Dean, Moshe Tennenholtz Technion Israel Institute of Technology Haifa, Israel |
| Pseudocode | No | The paper describes proposed mechanisms and their properties but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or include links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe experiments involving datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not mention specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not describe any specific hardware used for running experiments, as it is a theoretical paper. |
| Software Dependencies | No | The paper does not list any specific software dependencies or versions for implementation, as it is a theoretical paper. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, hyperparameters, or training configurations. |