Rigidity in LLM Bandits with Implications for Human-AI Dyads

TL;DR

This paper investigates decision biases in large language models using bandit experiments, revealing persistent rigidity and exploitation strategies that impact human-AI interactions.

Contribution

It introduces a novel bandit-based framework to analyze LLM decision biases and uncovers underlying strategies through computational modeling.

Findings

Models exhibit positional bias amplification under symmetric rewards.

Models tend to rigidly exploit options with asymmetric rewards.

Behavioral patterns are consistent across different decoding configurations.

Abstract

We test whether LLMs show robust decision biases. Treating models as participants in two-arm bandits, we ran 20000 trials per condition across four decoding configurations. Under symmetric rewards, models amplified positional order into stubborn one-arm policies. Under asymmetric rewards, they exploited rigidly yet underperformed an oracle and rarely re-checked. The observed patterns were consistent across manipulations of temperature and top-p, with top-k held at the provider default, indicating that the qualitative behaviours are robust to the two decoding knobs typically available to practitioners. Crucially, moving beyond descriptive metrics to computational modelling, a hierarchical Rescorla-Wagner-softmax fit revealed the underlying strategies: low learning rates and very high inverse temperatures, which together explain both noise-to-bias amplification and rigid exploitation. These results position minimal bandits as a tractable probe of LLM decision tendencies and motivate hypotheses about how such biases could shape human-AI interaction.