Some Large Language Models Exhibit Consistent Risk Attitudes
As artificial intelligence systems are deployed in open-ended, high-stakes settings, a critical dimension remains unmeasured: how perceived risk is translated into action. We test whether large language models (LLMs) exhibit systematic and consistent risk attitudes under uncertainty. We introduce a cross-domain framework that decouples contextual risk belief from categorical decision, and apply it to six representative LLMs and 100 human participants across spatial navigation, clinical triage, and financial allocation tasks. Using regression models, we extract each agents belief-to-decision mapping and quantify risk sensitivity and risk attitude bias. We find that most tested LLMs exhibit...
| Authors | Bowen Sun, Rui Min, Yuxi Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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