Abstract
We study a Bayesian learning model with heterogeneity in which the agents control both short-term manipulations and long-term efforts aimed at solving the optimal contract in dynamic environments. At the same time, considering the dynamic characteristics of contracts, the paper considers the influence of participants' Bayesian learning on the optimality in a continuous-time principal-agent setting. It is found that to encourage good agents to work hard while excluding harmful agents, contracts need to trade off the incentive costs and benefits of choosing the appropriate incentive intensity, while taking into account the information rent brought by the information advantages of the agents.
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