  This work studies the repeated principal-agent problem from an online
learning perspective. The principal's goal is to learn the optimal contract
that maximizes her utility through repeated interactions, without prior
knowledge of the agent's type (i.e., the agent's cost and production
functions). This work contains three technical results. First, learning linear
contracts with binary outcomes is equivalent to dynamic pricing with an unknown
demand curve. Second, learning an approximately optimal contract with identical
agents can be accomplished with a polynomial sample complexity scheme. Third,
learning the optimal contract with heterogeneous agents can be reduced to
Lipschitz bandits under mild regularity conditions. The technical results
demonstrate that the one-dimensional effort model, the default model for
principal-agent problems in economics which seems largely ignored in recent
works from the computer science community, may possibly be the more suitable
choice when studying contract design from a learning perspective.
