Official implementation of this ACL 2024 paper.
It's based on Foundation, An Economic Simulation Framework, which is announced by this paper:
Zheng, Stephan, et al. "The ai economist: Improving equality and productivity with ai-driven tax policies." arXiv preprint arXiv:2004.13332 (2020).
Simulate with GPT-3.5, 100 agents, and 240 months (fill openai.api_key in simulate_utils.py):
python simulate.py --policy_model gpt --num_agents 100 --episode_length 240
Simulate with Composite, 100 agents, and 240 months:
python simulate.py --policy_model complex --num_agents 100 --episode_length 240
For RL approaches, i.e., The ai economist, we just follow their training codes and use the trained models for simulations. See appendix in the paper for details.
The simulation was only tested using gpt-3.5-turbo-0613, but this model seems to no longer be accessible and has been replaced by gpt-4o-mini. If gpt_error is significantly greater than 0 (e.g., exceeding 10), meaning GPT generates many unreasonable decisions, please adjust the prompts accordingly, especially the parts related to format instruction:
"Please share your decisions in a JSON format. The format should have two keys: 'work' (a value between 0 and 1 with intervals of 0.02, indicating the willingness or propensity to work) and 'consumption' (a value between 0 and 1 with intervals of 0.02, indicating the proportion of all your savings and income you intend to spend on essential goods)."