Prophet Inequalities for Bayesian Persuasion
Authors: Niklas Hahn, Martin Hoefer, Rann Smorodinsky
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study an information-structure design problem (i.e., a Bayesian persuasion problem) in an online scenario. Inspired by the classic gambler s problem, consider a set of candidates who arrive sequentially and are evaluated by one agent (the sender)... We show an optimal prophet inequality for online Bayesian persuasion, with a 1/2-approximation when the instance satisfies a satisfactory-status-quo assumption. Without this assumption, there are instances without any finite approximation factor. We extend the results to combinatorial domains and obtain prophet inequalities for matching with multiple hires and multiple receivers. |
| Researcher Affiliation | Academia | Niklas Hahn1 , Martin Hoefer1 and Rann Smorodinsky2 1 Goethe University Frankfurt, Germany 2 Technion, Israel |
| Pseudocode | Yes | Algorithm 1. Simple Scheme for SSQ Input: Distributions (Di)i [n], factors d = (di)i [n], online sequence of θi drawn from Di for rounds i = 1 to n do Upon seeing the draw θi from Di: W. prob. 1 di: Signal NOT, go to next round. Otherwise, w. prob. x iθi/qiθi: Signal HIRE now and NOT in all remaining rounds Otherwise: Signal NOT, go to next round |
| 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 experimentation with datasets, training, validation, or testing splits. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experimentation with datasets, training, validation, or testing splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe software implementations with specific version numbers for dependencies. While it mentions Linear Programming (LP), it does not specify a particular solver or its version. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments that would require specific experimental setup details like hyperparameters or training configurations. |