Position: Social Environment Design Should be Further Developed for AI-based Policy-Making
Authors: Edwin Zhang, Sadie Zhao, Tonghan Wang, Safwan Hossain, Henry Gasztowtt, Stephan Zheng, David C. Parkes, Milind Tambe, Yiling Chen
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We do not include experimental results here, as the primary purpose of this paper is to propose a future research agenda and illustrate open problems. |
| Researcher Affiliation | Collaboration | 1Harvard University 2Founding 3Oxford University 4Asari AI 5Google Research. |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | We release a core implementation of our framework as a Sequential Social Dilemma Environment along with code; and We release our code in the supplementary material for reproducibility. |
| Open Datasets | No | The paper describes a custom 'Apple Picking Game' environment built with Melting Pot 2.0, but does not provide concrete access information or formal citation for a specific publicly available dataset used for training in the traditional sense. |
| Dataset Splits | No | The paper describes training details for an RL environment but does not specify dataset splits (e.g., train/validation/test percentages or counts) for reproducibility. |
| Hardware Specification | No | The paper mentions training details and hyperparameters but does not provide specific hardware details (e.g., CPU, GPU models, memory, or cloud instances) used for running experiments. |
| Software Dependencies | No | The paper mentions using PPO and GAE algorithms but does not provide specific software dependencies or library version numbers (e.g., Python, PyTorch, TensorFlow versions) for reproducibility. |
| Experiment Setup | Yes | Here we give a detailed breakdown of several key hyperparameters and Training Details within our environment in section 4. Table 1. Hyperparameters for our methods in section 4. Example parameters include: Number of Agents 7, Initial Number of Apples 64, Tax Period 50, Episode Length 1000, Sampling Horizon 200. |