Rigidity in LLM Bandits with Implications for Human-AI Dyads

This repository accompanies the paper “Rigidity in LLM Bandits with Implications for Human-AI Dyads” .It provides complete experimental data, model outputs, and analysis scripts used to evaluate how large language models (LLMs) exhibit decision rigidity and bias amplification under controlled two-armed bandit paradigms. The dataset quantify how LLMs (DeepSeek, GPT-4.1, Gemini-2.5) adapt, or fail to adapt, across different decoding regimes defined by temperature and top-p sampling.By treating LLMs as participants in reinforcement-learning tasks, the study identifies systematic tendencies toward stubborn exploitation and reduced exploration, modeled via hierarchical Rescorla–Wagner fits in Stan.