在重复游戏中的决策:从主动推理的见解
Hui Yuan1,2, Ligang Wang1,2, Wenbin Gao1,2
1State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
Behavioral sciences (Basel, Switzerland)
|December 30, 2025
概括
积极推断提供了一个统一的框架,通过最小化自由能量来理解重复游戏中的决策. 这种方法整合了感知,学习和行动,自然地处理社会不确定性和心理.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 重复的游戏涉及复杂的决策与社会不确定性,反映现实世界的互动.
- 传统的模型,如强化学习,难以统一认知过程.
- 积极推断提供了一种基于最小化自由能源的全新,综合的方法.
研究的目的:
- 系统地审查主动推理框架在重复游戏决策中解释认知机制的潜力.
- 突出主动推理能够统一感知,学习,规划和行动的能力.
- 探索其在模拟社会不确定性和心理化中的应用.
主要方法:
- 积极推理框架的系统审查.
- 探索其在变量自由能量最小化中的理论基础.
- 分析它对部分可观测的马尔科夫决策过程的应用.
主要成果:
- 积极推断将认知组件 (感知,学习,规划,行动) 统一在一个单一的生成模型中.
- 信念更新和勘探开发是通过最小化自由能源来管理的.
- 该框架本质上解决了社会不确定性,并支持模拟心智化过程.
结论:
- 积极推理在重复游戏中提供了统一的社会决策描述.
- 它的层次结构和自由能源最小化基础在传统模式上提供了优势.
- 建议通过模拟和行为拟合进行进一步验证.
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