{"ID":2877854,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.18600","arxiv_id":"2508.18600","title":"Bias-Adjusted LLM Agents for Human-Like Decision-Making via Behavioral Economics","abstract":"Large language models (LLMs) are increasingly used to simulate human decision-making, but their intrinsic biases often diverge from real human behavior--limiting their ability to reflect population-level diversity. We address this challenge with a persona-based approach that leverages individual-level behavioral data from behavioral economics to adjust model biases. Applying this method to the ultimatum game--a standard but difficult benchmark for LLMs--we observe improved alignment between simulated and empirical behavior, particularly on the responder side. While further refinement of trait representations is needed, our results demonstrate the promise of persona-conditioned LLMs for simulating human-like decision patterns at scale.","short_abstract":"Large language models (LLMs) are increasingly used to simulate human decision-making, but their intrinsic biases often diverge from real human behavior--limiting their ability to reflect population-level diversity. We address this challenge with a persona-based approach that leverages individual-level behavioral data f...","url_abs":"https://arxiv.org/abs/2508.18600","url_pdf":"https://arxiv.org/pdf/2508.18600v1","authors":"[\"Ayato Kitadai\",\"Yusuke Fukasawa\",\"Nariaki Nishino\"]","published":"2025-08-26T02:02:18Z","proceeding":"cs.GT","tasks":"[\"cs.GT\",\"cs.MA\",\"econ.GN\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}