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..
Inferring Private Valuations from Behavioral Data in Bilateral Sequential Bargaining
Authors: Lvye Cui, Haoran Yu
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real bargaining data show that our inference approach outperforms baselines. |
| Researcher Affiliation | Academia | School of Computer Science & Technology, Beijing Institute of Technology |
| Pseudocode | Yes | Algorithm 1 Homogeneous Behavior Learning Algorithm; Algorithm 2 K-Loss Clustering Algorithm |
| Open Source Code | Yes | The source code and data are available at: https://github.com/cuilvye/Bargaining-project. |
| Open Datasets | Yes | We also conduct experiments on a large dataset collected from e Bay s Best Offer platform [Backus et al., 2020]. |
| Dataset Splits | Yes | For both synthetic data and real data, we randomly select 80% of all threads for training, 10% for validation, and 10% for testing. |
| Hardware Specification | No | The paper mentions training models and using a GRU but does not specify any hardware details such as GPU/CPU models, memory, or specific computing environments used for experiments. |
| Software Dependencies | No | The paper mentions using a 'gated recurrent unit (GRU)' and the 'Adam optimizer' but does not specify software versions for these or any other libraries/frameworks (e.g., TensorFlow, PyTorch, Python versions). |
| Experiment Setup | Yes | The Adam optimizer with a learning rate of 0.001 is applied for our network training. The epoch num-ber T is set to 500 with a batch size of 64, and the weight factor α is set to 0.6. |