A brief note on learning problem with global perspectives

TL;DR

This paper explores a complex principal-agent learning framework where agents have global perspectives and the principal optimizes based on aggregated information, highlighting the need for further theoretical development.

Contribution

It introduces a novel principal-agent learning model incorporating global perspectives and empirical-likelihood estimation under conditional moment restrictions.

Findings

Mathematical characterization of the learning process

Identification of conceptual and theoretical issues

Framework for future research in agent-based learning

Abstract

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.