发表机构
College of Engineering and Network Science Institute, Northeastern University（东北大学工程学院与网络科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出自适应互补性框架，通过信息暴露、委托与战略互依等机制选择人机交互架构，以塑造未来能力状态，并区分强互补性与相对优势，强调交互历史可逆转工作流程排名。
AI 中文摘要
人机交互可以在提升当前绩效的同时，改变未来绩效所依赖的能力与关系。我们提出了自适应互补性（adaptive complementarity）这一框架，用于在考虑这些状态后果的前提下选择交互架构。访问权限、信息暴露、任务分配、时序安排和沟通方式都可能改变哪种安排在未来具有价值；这些设置的调整速度通常快于它们所创造的能力、搜索模式或惯例。三个机制组织了这一论证：信息暴露与集体搜索、委托与能力演化、以及战略相互依赖与信息治理。它们的整合产生了跨机制的含义，包括在何种条件下，专家异质性的丧失会增加维持独立搜索所需的信息差异化。我们将强人机互补性与相对于另一工作流程的优势以及相对于演化中的参考策略的优势区分开来。一个知识覆盖的示例表明，即使在人类能力相同的情况下，不同的交互历史也能逆转当前工作流程的排名。该示例还将这一结果与状态反馈的增量价值区分开来，当精心选择的稳定工作流程预见了学习时，这种增量价值可能很小。该框架将评估导向当前交互所创造的状态、这些状态对未来架构适配性的影响，以及观察和响应这些状态在何种条件下是值得的。
英文摘要
Human-AI interaction can improve current performance while changing the capabilities and relationships on which future performance depends. We develop adaptive complementarity, a framework for choosing interaction architecture with these state consequences in view. Access, information exposure, task allocation, timing, and communication can alter which arrangement will be valuable later; their settings can often be reset faster than the capabilities, search patterns, or conventions they create. Three mechanisms organize the argument: information exposure and collective search, delegation and capability evolution, and strategic interdependence and information governance. Their integration yields cross-mechanism implications, including conditions under which a loss of expertise heterogeneity increases the information differentiation required to preserve independent search. We distinguish strong human-AI complementarity from advantage over another workflow and from advantage over an evolving reference policy. A computational illustration examines scarce human review in a workflow whose success requires several specialized stages. Review develops human expertise and AI capabilities, changing where subsequent review is most valuable. Adaptive allocation improves net output over untailored procedures and the optimal predetermined calendar. An understandable priority rule derived from the adaptive solution retains essentially all of its gain: the procedure stays fixed while assignments respond to the capabilities that interaction creates. The framework directs evaluation toward the states present interaction creates, their consequences for later architectural fit, and the conditions under which observing and responding to them is worthwhile.
