Bias-Adjusted LLM Agents for Human-Like Decision-Making via Behavioral Economics
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918130635243520 |
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| author | Kitadai, Ayato Fukasawa, Yusuke Nishino, Nariaki |
| author_facet | Kitadai, Ayato Fukasawa, Yusuke Nishino, Nariaki |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_18600 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bias-Adjusted LLM Agents for Human-Like Decision-Making via Behavioral Economics Kitadai, Ayato Fukasawa, Yusuke Nishino, Nariaki Computer Science and Game Theory Multiagent Systems General Economics Economics 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. |
| title | Bias-Adjusted LLM Agents for Human-Like Decision-Making via Behavioral Economics |
| topic | Computer Science and Game Theory Multiagent Systems General Economics Economics |
| url | https://arxiv.org/abs/2508.18600 |