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..
A mathematical theory of cooperative communication
Authors: Pei Wang, Junqi Wang, Pushpi Paranamana, Patrick Shafto
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Computational simulations support and elaborate our theoretical results, and demonstrate fit to human behavior. |
| Researcher Affiliation | Academia | Pei Wang Rutgers University Newark EMAIL Junqi Wang Rutgers University Newark EMAIL Pushpi Paranamana Rutgers University Newark EMAIL Patrick Shafto Rutgers University Newark EMAIL |
| Pseudocode | No | The paper describes algorithmic processes such as Sinkhorn scaling, but it does not include a dedicated pseudocode block or algorithm box. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | Human data are measured based on Fig.2 of Goodman and Stuhlmüller [2013]. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | No | The paper does not mention any specific hardware (e.g., GPU/CPU models, cloud instances, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or library version numbers (e.g., Python 3.8, PyTorch 1.9) required to replicate the experiments. |
| Experiment Setup | Yes | Assume a uniform prior on D and λ = 1. Shared matrix M and prior over H are sampled from symmetric Dirichlet distribution with hyperparameter α = 0.15. Sample size is 10^6 per plotted point. |