Persuading Farsighted Receivers in MDPs: the Power of Honesty
Authors: Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Mirco Mutti
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Formally, we design an algorithm that computes an optimal and ϵ-persuasive history-dependent signaling scheme in time polynomial in 1/ϵ and in the instance size. Indeed, differently from most of the MDPs settings, we prove that Markovian signaling schemes are not optimal, and general history-dependent signaling schemes should be considered. |
| Researcher Affiliation | Academia | Martino Bernasconi Bocconi University martino.bernasconi@unibocconi.it Matteo Castiglioni Politecnico di Milano matteo.castiglioni@polimi.it Alberto Marchesi Politecnico di Milano alberto.marchesi@polimi.it Mirco Mutti Technion mirco.m@technion.ac.il |
| Pseudocode | Yes | Algorithm 1 Sender-receiver interaction, Algorithm 2 From histories to promises, Algorithm 3 Approximation scheme, Algorithm 4 Approximate oracle |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of open-source code for the described methodology. |
| Open Datasets | No | The paper presents theoretical results and uses a constructed example in Appendix A to illustrate a proof. It does not utilize or refer to any publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper describes theoretical algorithms and proofs without specifying any software dependencies with version numbers required for implementation or execution. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, so no experimental setup details like hyperparameters or training configurations are provided. |