让大模型代理学会有欲望和情绪地做决策，更像真人。
Emotional Cognitive Modeling Framework with Desire-Driven Objective Optimization for LLM-empowered Agent in Social Simulation
- 构建欲望生成与目标优化框架，模拟完整决策链
- 实验显示其行为更贴近人类，生态有效性显著提升
- 适合研究虚拟人、社会仿真与人机交互的学者
大语言模型（LLMs）使代理能够代表虚拟人类参与社会仿真，但在情感认知方面存在严重缺陷：无法体现有限理性以弥合虚拟与现实服务的鸿沟；缺乏经过实证验证的情感嵌入决策架构机制。本文构建了一种包含欲望生成与目标管理的情感认知框架，旨在实现基于LLM的代理与人类之间的情绪对齐，完整建模代理的决策过程，涵盖状态演化、欲望生成、目标优化、决策生成与动作执行。该研究在自研多智能体交互环境中实现了该框架。实验结果表明，受此框架驱动的代理不仅表现出与其情绪状态一致的行为，且在与其它代理类型对比中，展现出更高的生态有效性，并生成显著更接近人类行为模式的决策结果。
原文摘要 · Abstract (English)
The advent of large language models (LLMs) has enabled agents to represent virtual humans in societal simulations, facilitating diverse interactions within complex social systems. However, existing LLM-based agents exhibit severe limitations in affective cognition: They fail to simulate the bounded rationality essential for bridging virtual and real-world services; They lack empirically validated integration mechanisms embedding emotions within agent decision architectures. This paper constructs an emotional cognition framework incorporating desire generation and objective management, designed to achieve emotion alignment between LLM-based agents and humans, modeling the complete decision-making process of LLM-based agents, encompassing state evolution, desire generation, objective optimization, decision generation, and action execution. This study implements the proposed framework within our proprietary multi-agent interaction environment. Experimental results demonstrate that agents governed by our framework not only exhibit behaviors congruent with their emotional states but also, in comparative assessments against other agent types, demonstrate superior ecological validity and generate decision outcomes that significantly more closely approximate human behavioral patterns.
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