基于智能体建模研究人类情感与认知的演化

段琴; 罗思阳

doi:10.16719/j.cnki.1671-6981.20250603

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心理科学 ›› 2025, Vol. 48 ›› Issue (6) : 1314-1332. DOI: 10.16719/j.cnki.1671-6981.20250603

计算建模与人工智能

基于智能体建模研究人类情感与认知的演化

段琴, 罗思阳^**

作者信息 +

Using Agent-Based Modeling to Study the Adaptation and Evolution of Human Emotions and Cognition^*

Duan Qin¹, Luo Siyang¹

Author information +

文章历史 +

摘要

智能体建模（agent-based modeling,ABM）在研究人类情感和认知演化中的应用与潜力正在受到广泛关注。与传统的情感与认知研究方法（如理论框架、案例分析和实证数据）不同,ABM通过模拟个体智能体的行为与互动,能够在微观层面揭示情感与认知的演化过程,并观察这些过程如何在宏观层面上涌现为群体行为、社会规范和文化实践。ABM能够捕捉社会互动中的随机性和复杂性,为情感（如共情、愤怒、恐惧等）和认知（如决策、学习、问题解决等）过程的演化提供新的视角,特别是情感与认知之间的相互作用。例如,通过模拟囚徒困境等经典博弈模型,ABM展示了情感与认知如何在群体中推动合作行为的演化,比如个体通过反复互动积累经验并学习合作策略的过程以及共情通过促进个体对他人情感状态的理解与反应从而增强合作的重要作用。但是,目前ABM在情感与认知演化研究中的应用也面临若干挑战：情感与认知的复杂性使得模型建立和参数化变得困难,尤其是如何将生物学、心理学和社会因素综合进模型;如何对难以获取真实世界数据的ABM模型所生成的结果进行验证仍然是一个重要问题;模型复杂度的增加也使得ABM的计算需求和可扩展性问题也愈加突出,尤其是在模拟大规模群体或长期演化过程时,计算资源的消耗是一个重要的制约因素。随着科学技术的发展,ABM在解析人类情感与认知演化进程中的应用有很多潜在发展方向。例如,将神经生物学与心理学的数据深度融合,以增强模型的生物学与心理学基础;在复杂社会网络中实施更为精细的模拟,以捕捉社会互动的微妙与多样性;结合人工智能与机器学习的最新成果,尤其是大语言模型赋能的智能体演化模型,将为ABM注入新的活力以及推动模型在现实世界场景中的广泛应用,验证其实际效用与价值。综上所述,ABM为研究人类情感与认知的演化提供了独特的视角,不仅能够揭示微观层面的个体行为和互动,还能展示这些行为如何在宏观层面上演化为社会现象。尽管在模型复杂性、结果验证与计算资源等方面存在挑战,但随着技术的进步,ABM有望推动其公共政策、教育和公共健康等领域的实际应用,进而深化对人类行为演化机制的理解。

Abstract

This review explores the use of agent-based modeling (ABM) within the framework of study human emotion and cognition in the context of its ability to simulate complex social interactions, adaptive changes, and evolutionary processes. By representing agents and their defined environments with probabilistic interactions, ABM allows the assessment of the effects of individual behavior at the micro level on the greater social phenomena at the macro level. The review looks into the applications of ABM in portraying some of the key components of emotions and cognition—empathy, cooperation, decision making, and emotional transmission—and analyzes the problems including scalability, empirical validation, and description of sensitive emotional states. The most important conclusion is that merging ABM with information neurobiological data and artificial intelligence (AI) techniques would allow for deepening the interactions within the system and enhancing its responsiveness to stimuli. This review highlights approaches that aim to exploit the ABM methodology more fully and integrates methods from biology, neuroscience, and engineering. This integration could contribute to our understanding of the human behavior evolution and adaptation within systems relevant to policymaking, healthcare, and education.

导出引用

段琴, 罗思阳. 基于智能体建模研究人类情感与认知的演化[J]. 心理科学. 2025, 48(6): 1314-1332 https://doi.org/10.16719/j.cnki.1671-6981.20250603

Duan Qin, Luo Siyang. Using Agent-Based Modeling to Study the Adaptation and Evolution of Human Emotions and Cognition^*[J]. Journal of Psychological Science. 2025, 48(6): 1314-1332 https://doi.org/10.16719/j.cnki.1671-6981.20250603

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