In this paper we examine a representative agent forecasting prices in a first-order self-referential overlapping generations model. We first consider intermediate stage learning, where agents update the forecasting rule every periods. We show that, in theory and simulations, the learning rule...

This paper investigates a dynamic agency problem that includes the AI-style agent and principal. We develop a quantitative bionic approach to dynamic contracting based on calibrating the incentive properties of a workhorse contracts model in which self-awareness and memory are endowed. We...

We study the effects of overconfidence in a two-period investment-decision agency setting. Under common priors, agent risk aversion implies inefficiently low first-period investment. In our model, principal and agent disagree about the profitability of the investment decision conditional on a...

In this paper the impact of ability and learning potential on incentive contracts is analyzed. A central feature of the model is that the true ability will not be revealed. The learning potential of an agent is modeled as the magnitude of impact on the agent's expected ability that...

We analyze a repeated principal-agent setting in which the principal cares about the agent's verifiable effort as well as an extra profit that can be generated only if the agent is talented. The agent is overconfident about his talent and updates beliefs using Bayes' rule. An exploitation...

In this paper the impact of ability and learning potential on incentive contracts is analyzed. A central feature of the model is that the true ability will not be revealed. The learning potential of an agent is modeled as the magnitude of impact on the agent's expected ability that...