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..
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. |