迈向由人工智能驱动的劳动力政策计算测试平台
Toward an AI-Powered Computational Testbed for Workforce Policy
AI总结：
本文提出了一种动态员工代理，结合LLM生成代理、管理科学和组织行为研究，以预测员工在组织变革中的心理和行为反应，同时定义了隐私、准确性和代表性保障措施。
摘要：
劳动力转型难以预测且管理不当成本高昂。特别是，人工智能在知识工作中的整合目前影响了全球大量劳动力，但这一转变缺乏工具来预测个体员工的心理和行为反应。我们结合最近的LLM生成代理进展与基础管理科学和组织行为研究，提出动态员工代理。在同意的群体中，这些代理可以利用HR记录、验证心理测量和数字活动数据进行播种，以模拟员工在计划组织变革期间连续工作日中的认知、情感和行为轨迹。本文详细说明了构建此模拟平台所需的计算架构，并定义了负责任部署所需的隐私、准确性和代表性保障措施。我们主张建立这种前瞻性预测基础设施是管理当前全球劳动力围绕人工智能重新调整的关键技术要求。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
