Incentive-Compatible Classification
Authors: Yakov Babichenko, Oren Dean, Moshe Tennenholtz7055-7062
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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. |