Formal Verification of Bayesian Mechanisms

Authors: Munyque Mittelmann, Bastien Maubert, Aniello Murano, Laurent Perrussel

AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical In this paper, for the first time, we study the formal verification of Bayesian mechanisms through strategic reasoning. We rely on the framework of Probabilistic Strategy Logic (PSL)... We take advantage of the recent results on the decidability of PSL model checking under memoryless strategies, and reduce the problem of formally verifying Bayesian mechanisms to PSL model checking. We show how to encode Bayesian-Nash equilibrium and economical properties, and illustrate our approach with different kinds of mechanisms.
Researcher Affiliation Academia Munyque Mittelmann1, Bastien Maubert1, Aniello Murano1, Laurent Perrussel2 1 Universit a degli Studi di Napoli Federico II , Italy 2 IRIT Universit e Toulouse Capitole, France {munyque.mittelmann, laurent.perrussel}@ut-capitole.fr, {bastien.maubert, nello.murano}@gmail.com
Pseudocode No The paper does not contain any sections explicitly labeled 'Pseudocode' or 'Algorithm', nor are there structured steps formatted like code blocks.
Open Source Code No The paper does not contain any explicit statements about the release of source code or links to a code repository.
Open Datasets No The paper is theoretical and does not involve empirical training or the use of datasets for experimentation. Therefore, no information on publicly available training datasets is provided.
Dataset Splits No The paper is theoretical and does not describe empirical experiments involving data, thus it does not provide information on training/test/validation dataset splits.
Hardware Specification No The paper is theoretical and does not describe computational experiments, therefore no hardware specifications are mentioned.
Software Dependencies No The paper is theoretical and focuses on formal verification and logic, without describing any software implementation details or dependencies with version numbers.
Experiment Setup No The paper is theoretical and presents a formal framework, thus it does not provide details on experimental setup, hyperparameters, or system-level training settings.