在日常协作实验中比较人工智能和人类决策机制.
Linghao Wang1, Zheyuan Jiang2, Chenke Hu2
1Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China.
iScience
|June 16, 2025
概括
大型语言模型 (LLM) 在日常决策中显示出类似人类的学习,但在多个代理合作中扎. 对于复杂的群体互动,人工智能决策需要改进.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 游戏理论 游戏理论
背景情况:
- 人工智能 (AI) 的能力日益增强,特别是大型语言模型 (LLM),引发了关于它们有可能取代人类决策的能力的问题.
- 了解人工智能在复杂的协作环境中的表现对于评估其在现实世界中的应用性至关重要.
研究的目的:
- 在模拟的多天通勤游戏中比较人类,LLM和强化学习 (RL) 代理人的决策表现.
- 分析AI在动态,相互依存的决策场景中学习,融合和协作的能力.
主要方法:
- 一个为期几天的通勤决策游戏旨在模拟具有相互依存结果的协作选择.
- 绩效指标包括系统级结果,融合率,个人决策动态和人类,LLM和RL代理人的决策机制.
主要成果:
- 从历史数据来看,LLM证明了类似人类的学习,并在个人通勤决策中实现了趋同.
- 在协作环境中,LLM在感知他人的行为和整合物理知识方面表现出局限性.
结论:
- 在模拟个人决策任务时,LLM显示出模仿人类学习的前景,但在有效的多代理合作中需要取得重大进展.
- 需要进一步的研究来增强LLM的社会认知,并为复杂的群体决策建立在物理现实中的基础.
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