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 [1].
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. |