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].
Probabilistic Verification for Obviously Strategyproof Mechanisms
Authors: Diodato Ferraioli, Carmine Ventre
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To this aim, we deļ¬ne a model of probabilistic veriļ¬cation wherein agents are caught misbehaving with a certain probability, and show how OSP mechanisms can implement every social choice function at the cost of either imposing very large ļ¬nes or verifying a linear number of agents. We prove that, in this setting, it is possible to obtain an OSP mechanism for every speciļ¬c problem of interest; we essentially show that we can always deļ¬ne veriļ¬cation probabilities and ļ¬nes to make any lie obviously dominated. On the technical level, we show that there is a trade-off between the kind of veriļ¬cation device needed (i.e., the veriļ¬cation probabilities) and the amount of ļ¬nes imposed to lying agents that are caught. Our results imply that we can set the ļ¬nes so that only a constant number of agents is veriļ¬ed in expectation. |
| Researcher Affiliation | Academia | Diodato Ferraioli University of Salerno, Italy EMAIL Carmine Ventre University of Essex, UK EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training or evaluation. The public project problem is discussed as a theoretical model, not an empirical dataset. |
| Dataset Splits | No | The paper is theoretical and does not specify dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |