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..
Multi-Sender Persuasion: A Computational Perspective
Authors: Safwan Hossain, Tonghan Wang, Tao Lin, Yiling Chen, David C. Parkes, Haifeng Xu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Broadly, our theoretical and empirical contributions are of interest to a large class of economic problems. |
| Researcher Affiliation | Academia | 1Harvard University 2University of Chicago. Correspondence to: Safwan Hossain, Tonghan Wang, Tao Lin <EMAIL>. |
| Pseudocode | No | The paper describes algorithmic steps (e.g., extra-gradient updates) but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper describes generating synthetic problems and scenarios, but does not provide concrete access information (link, DOI, repository, or citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper mentions collecting a dataset and training networks but does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning into train/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer (Kingma & Ba, 2014) but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For each problem instance, we collect a dataset comprising 50,000 randomly selected samples and train the networks for 30 epochs using the Adam optimizer (Kingma & Ba, 2014) with a learning rate of 0.01. For extra-gradient, we initiate the optimization process from a set of 300 random starting points. For each starting point, we run 20 iterations of extra-gradient updates with the Adam optimizer and a learning rate of 0.1. |