Online Bayesian Persuasion Without a Clue
Authors: Francesco Bacchiocchi, Matteo Bollini, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study online Bayesian persuasion problems in which an informed sender repeatedly faces a receiver with the goal of influencing their behavior through the provision of payoff-relevant information. ... We design an algorithm that achieves sublinear in the number of rounds regret with respect to an optimal signaling scheme, and we also provide a collection of lower bounds showing that the guarantees of such an algorithm are tight. |
| Researcher Affiliation | Academia | Francesco Bacchiocchi Politecnico di Milano francesco.bacchiocchi@polimi.it Matteo Bollini Politecnico di Milano matteo.bollini@polimi.it Matteo Castiglioni Politecnico di Milano matteo.castiglioni@polimi.it Alberto Marchesi Politecnico di Milano alberto.marchesi@polimi.it Nicola Gatti Politecnico di Milano nicola.gatti@polimi.it |
| Pseudocode | Yes | Algorithm 1 Learn-to-Persuade-w/o-Clue |
| Open Source Code | No | The paper does not mention releasing any source code for its methodology. |
| Open Datasets | No | The paper is theoretical and does not use or mention any datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup or hyperparameters. |