This brief note considers the problem of learning with dynamic-optimizing principal-agent setting, in which the agents are allowed to have global perspectives about the learning process, i.e., the ability to view things according to their relative importances or in their true relations based-on some aggregated information shared by the principal. Whereas, the principal, which is exerting an influence on the learning process of the agents in the aggregation, is primarily tasked to solve a high-level optimization problem posed as an empirical-likelihood estimator under conditional moment restrictions model that also accounts information about the agents' predictive performances on out-of-samples as well as a set of private datasets available only to the principal. In particular, we present a coherent mathematical argument which is necessary for characterizing the learning process behind this abstract principal-agent learning framework, although we acknowledge that there are a few conceptual and theoretical issues still need to be addressed.

翻译：本简要说明探讨了动态优化委托-代理框架下的学习问题，其中代理被允许对学习过程持有全局视角，即能够基于委托方共享的聚合信息，依据相对重要性或真实关联性来审视学习过程。而委托方在聚合过程中对代理的学习施加影响，其主要任务是解决一个高层优化问题，该问题被表述为条件矩限制模型下的经验似然估计器，同时兼顾代理在样本外预测表现的信息以及仅委托方可访问的私有数据集。特别地，我们提出了一个连贯的数学论证，这对于刻画这一抽象委托-代理学习框架背后的学习过程是必要的，尽管我们承认仍有若干概念性和理论性问题有待解决。