人类强化学习的学习过程和偏见:计算特征和可能的应用行为公共政策的行为公共政策
1Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale, Paris, France.
Mind & society
|December 22, 2025
概括
本研究引入了一个框架,以了解强化学习 (RL) 中的计算偏差. 它区分实践和认识学偏见,为行为公共政策提供了洞察力.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 行为经济学是一种行为经济学.
背景情况:
- 强化学习 (RL) 模型通过奖励和惩罚来学习代理人.
- 人类RL经常偏离规范标准,表现出系统的偏见.
- 现有的研究突出显示了RL中的计算偏差.
研究的目的:
- 提出一个概念框架和分类学来评估RL中的计算偏差.
- 区分实践偏见 (行动选择错误) 和认识偏见 (表示错误).
- 描述和讨论相对估值和有偏见的更新作为关键的认识 biases.
主要方法:
- 概念框架的发展.
- 为计算偏差创建分类学.
- 文献综述和现有研究的综合.
主要成果:
- 实践和认识论偏见类别的引入.
- 识别相对估值和偏见更新作为主要的认识学偏见.
- 讨论这些偏见的行为特征和适应性作用.
结论:
- 结果为理论和应用RL领域提供了信息.
- 与推动相比,基于强化干预在公共政策中未得到充分利用.
- 倡导将RL整合到行为公共政策设计中,解决历史误解.
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