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..
Learning Strategic Network Emergence Games
Authors: Rakshit Trivedi, Hongyuan Zha
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we demonstrate that MINE learns versatile payoff mechanisms that: highly correlates with the ground truth for a synthetic case; can be used to analyze the observed network structure; and enable effective transfer in specific settings. |
| Researcher Affiliation | Academia | Rakshit Trivedi Georgia Institute of Technology EMAIL Hongyuan Zha AIRS and Chinese University of Hong Kong, Shenzhen EMAIL |
| Pseudocode | Yes | Algorithm 1 in Appendix B outlines complete training procedure. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the methodology described. |
| Open Datasets | Yes | We consider Australian bank dataset [37] (Figure 2(a)), a small network of strategic confiding relationships between branch personnel representing hierarchy among the employees. |
| Dataset Splits | No | The paper mentions splitting data into train and test sets, but does not explicitly describe a separate validation set or its usage for hyperparameter tuning or model selection. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific version numbers for software dependencies or libraries used in the implementation. |
| Experiment Setup | No | We provide more details on experimental setup in Appendix C and dataset statistics in Table 2(c). |