用AI模拟员工反应，帮企业预测人力转型影响。
Toward an AI-Powered Computational Testbed for Workforce Policy
- 构建动态员工代理模型，融合心理与行为数据。
- 可模拟组织变革中员工多日认知情绪变化轨迹。
- 适合政策制定者与人力资源管理者参考使用。
劳动力转型难以预测且误判成本高昂。当前人工智能在知识工作中的应用已影响全球大量员工，但缺乏工具预测个体在心理和行为上的反应。本文结合大语言模型驱动的生成代理与管理科学、组织行为学研究成果，提出动态员工代理模型。在同意参与的群体中，该模型可基于人力资源记录、心理测量数据及数字活动数据，模拟员工在组织变革期间连续工作日的认知、情绪与行为演变过程。文章详细阐述了构建此仿真平台所需的计算架构，并定义了隐私保护、准确性与代表性保障措施，强调建立前瞻性预测基础设施是应对全球劳动力因AI重构的关键技术需求。
原文摘要 · Abstract (English)
Workforce transformations are difficult to forecast and costly to mismanage. In particular, the integration of artificial intelligence into knowledge work currently affects a substantial share of the global workforce, yet this transition proceeds without tools to forecast how individual employees will respond psychologically and behaviorally. We combine recent advances in LLM-powered generative agents with foundational management science and organizational behavior research to propose dynamic employee agents. Among consenting populations, these agents can be seeded with HR records, validated psychometric measures, and digital activity data to simulate employees' cognitive, emotional, and behavioral trajectories across successive workdays during planned organizational changes. In this article, we detail the computational architecture required to construct this simulation platform and define the privacy, accuracy, and representativeness safeguards necessary for responsible deployment. We argue that establishing this prospective forecasting infrastructure is a critical technical requirement for managing the current global workforce realignment around AI.
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