https://github.com/yachty66/economicagents

Implementation of the paper "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?"
https://github.com/yachty66/economicagents

artificial-intelligence economics game-theory research simulations

Last synced: 6 months ago
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Implementation of the paper "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?"

• Host: GitHub
• URL: https://github.com/yachty66/economicagents
• Owner: yachty66
• License: mit
• Created: 2023-09-24T09:41:11.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-12T10:50:11.000Z (over 1 year ago)
• Last Synced: 2025-04-14T09:20:05.680Z (6 months ago)
• Topics: artificial-intelligence, economics, game-theory, research, simulations
• Language: Python
• Homepage: https://discord.gg/Qy69gzmpt4
• Size: 804 KB
• Stars: 20
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# EconomicAgents

This is an implementation and Python package for the paper [Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?](https://arxiv.org/abs/2301.07543). This Python package enables you to run all four simulations from the paper. 

If you like this work, consider joining our [![Discord](https://img.shields.io/badge/Discord-7289DA?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/Qy69gzmpt4).

## Installation

```sh

pip install economic_agents

```

## Usage 

### Charness Rabin

```python

from 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:

```txt

"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:**

![Rabin Results](./results/charness_rabin.png)

### Horton

```python

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:**

![Horton Results](./results/horton.png)

### Kahneman

```python

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:**

![Kahneman Results](./results/kahneman.png)

### Zeckhauser

```python

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:**

![Zeckhauser Results](./results/zeckhauser.png)

## Todo 

- [ ] Create a Gradio demo

- [ ] Make experiments possible with dynamic inputs

- [ ] Improve error handling / code refactoring

- [ ] Add support for other models

 

## Citation 

```bibtex

@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}

}

```