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..
Persuading Farsighted Receivers in MDPs: the Power of Honesty
Authors: Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi, Mirco Mutti
NeurIPS 2023 | Venue PDF | 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 EMAIL Matteo Castiglioni Politecnico di Milano EMAIL Alberto Marchesi Politecnico di Milano EMAIL Mirco Mutti Technion EMAIL |
| 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. |