Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Private Bayesian Persuasion with Sequential Games
Authors: Andrea Celli, Stefano Coniglio, Nicola Gatti1886-1893
AAAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,958 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,845 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate private persuasion games with multiple receivers interacting in a sequential game, and study the continuous optimization problem of computing a private signaling scheme which maximizes the sender s expected utility. We introduce the notion of ex ante persuasive signaling scheme, and formalize its differences from ex interim persuasive schemes. Then, we show that ex ante persuasiveness can provide the sender with a utility that can be arbitrarily larger than that provided by ex interim persuasiveness. Moreover, we show that this result cannot be extended to settings with more than two receivers, as the problem of computing an optimal ex ante signaling scheme becomes NP-hard. |
| Researcher Affiliation | Academia | 1Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, Italy 2University of Southampton, University Road SO17 1BJ, Southampton, United Kingdom |
| Pseudocode | Yes | Algorithm 1 Separation plan search for (θ, z) |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available or links to a code repository for the methodology described. |
| Open Datasets | No | This paper is theoretical in nature, focusing on algorithmic complexity and mathematical proofs rather than empirical evaluation on a dataset. Therefore, it does not mention or provide access information for a dataset. |
| Dataset Splits | No | This paper is theoretical in nature and does not describe experiments that would require train/validation/test dataset splits. |
| Hardware Specification | No | This paper focuses on theoretical contributions (algorithms, complexity proofs) and does not describe experimental implementations that would require specific hardware specifications. |
| Software Dependencies | No | This paper is theoretical and does not mention specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | This paper is theoretical and focuses on algorithm design and complexity analysis. It does not describe an experimental setup with specific hyperparameters or training settings. |