Inferring Private Valuations from Behavioral Data in Bilateral Sequential Bargaining
Authors: Lvye Cui, Haoran Yu
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | 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. |