This is an implementation and Python package for the paper Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?. This Python package enables you to run all four simulations from the paper.
If you like this work, consider joining our 
pip install economic_agentsfrom economic_agents import CharnessRabin
charness_rabin = CharnessRabin(api_key="openai_key", model="gpt-3.5-turbo", personality=1, image_path="folder/charness_rabin", logging=True)
results = charness_rabin.play()
charness_rabin.create_plot(results)The personality argument determines an option from the following personalities from the original paper:
"You only care about fairness between players",
"You only care about your own pay-off",
"You only care about the total pay-off of both players",
" "Result:
from economic_agents import Horton
horton = Horton(api_key="openai_key", model="gpt-3.5-turbo", image_path="folder/horton", logging=True)
results = horton.play()
horton.create_plot(results)Result:
from economic_agents import Kahneman
kahneman = Kahneman(api_key="openai_key", model="gpt-3.5-turbo", image_path="results/kahneman", logging=True)
results = kahneman.play()
kahneman.create_plot(results)Result:
from economic_agents import Zeckhauser
zeckhauser = Zeckhauser(api_key="openai_key", model="gpt-3.5-turbo", image_path="results/zeckhauser", logging=True)
results = zeckhauser.play()
zeckhauser.create_plot(results)Result:
- Create a Gradio demo
- Make experiments possible with dynamic inputs
- Improve error handling / code refactoring
- Add support for other models
@article{horton2023large,
title={Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?},
author={Horton, John J},
journal={arXiv preprint arXiv:2301.07543},
year={2023}
}