# Comparing Exploration–Exploitation Strategies of LLMs and Humans

This repository accompanies the paper:

**"Comparing Exploration–Exploitation Strategies of LLMs and Humans: Insights from Standard Multi-armed Bandit Experiments"**

## Overview
 Large language models (LLMs) are increasingly used to simulate or automate human behavior in complex sequential decision-making settings.  
This project investigates whether LLMs exhibit similar exploration–exploitation (E&E) strategies compared to humans, using canonical multi-armed bandit (MAB) experiments from cognitive science and psychiatry.

We provide:
- Code to reproduce stationary and non-stationary MAB experiments.
- Scripts for fitting interpretable choice models.
- Comparative analysis pipelines between humans, LLMs, and algorithmic baselines.

Our results show that enabling thinking in LLMs shifts their behavior toward more human-like exploration patterns in simple environments, but challenges remain in non-stationary settings.

## Experiment Protocal 

The following figure illustrates the prompt design and interaction flow we used in the 2-armed bandit experiments:

![Prompt Example](figures/Human_AI.png)

