在模拟婴儿配方短缺期间,基于行为理论的与非基于理论的AI代理的现实主义
Linda Desens1, Brandon Walling1, Rhys O'Neill1
1The MITRE Corporation, McLean, VA, United States.
Frontiers in artificial intelligence
|February 25, 2026
概括
行为科学框架在危机模拟中显著提高了人工智能 (AI) 驱动的代理人的现实性. 这项研究表明,基于理论的人工智能代理更可信,提高了对人工智能的信任,以支持决策.
科学领域:
- 人工智能的人工智能
- 行为科学 行为科学
- 计算社会科学 计算社会科学
背景情况:
- 人工智能驱动的数字双胞胎和自主的人工智能代理越来越多地用于模拟危机期间的人类行为.
- 将行为科学框架纳入人工智能代理设计是一个新兴但有前途的方法来提高模拟现实性.
研究的目的:
- 在受控实验环境中,评估由行为科学理论告知的人工智能代理人的现实性.
- 在模拟中建立一个可重复的方法来评估人工智能代理的行为忠实性.
主要方法:
- 一个模拟的婴儿配方奶粉短缺场景在达拉斯县南部被采用.
- 两种代理条件进行了比较:基于理论的代理与没有理论依据的代理.
- 在这两种条件下,人类评级者评估了代理决策的感知现实性.
主要成果:
- 结合行为科学框架的代理人获得了明显更高的现实评分.
- 这些发现支持这样一个假设,即行为理论在模拟中增强了代理现实主义.
结论:
- 这项研究提供了一个早期的,基于证据的评估行为理论在AI模拟框架中的作用.
- 该研究提供了一种基于理论的策略,用于增强人工智能代理的现实性,这对于建立对人工智能驱动的决策支持系统的信任至关重要,用于高风险的场景.
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