Social network learning efficiency in the principal-agent relationship

Under the bounded rationality assumption, a principal rarely provides an optimal contract to an agent. Learning from others is one way to improve such a contract. This paper studies the efficiency of social network learning (SNL) in the principal-agent framework. We first introduce the Cobb-Douglas production function into the classic Holmstrom and Milgrom (1987) model with a constant relative risk-averse agent and work out the theoretically optimal contract. Algorithms are then designed to model the SNL process based on profit gaps between contracts in a network of principals. Considering the uncertainty of the agent's labor output, we find that the principals can reach a consensus that tends to result in overcompensation compared to the optimal contract. Then, this study examines how network attributes and model parameters impact learning efficiency and posits several summative hypotheses. The simulation results validate these hypotheses, and we discuss the relevant economic implications of the observed changes in SNL efficiency.