🤖 AI Summary
This paper examines how a principal should design the timing of monitoring inspections to incentivize agent effort—specifically, whether inspections should be deterministic or stochastic. Method: Building on principal–agent theory and dynamic game modeling, we incorporate incentive compatibility constraints and optimal contract design, and—novelly—classify tasks exogenously by intrinsic nature into “breakthrough-oriented” (e.g., innovation) and “failure-avoidance–oriented” (e.g., risk control). Contribution/Results: We prove that deterministic inspections dominate for breakthrough-oriented tasks, as they reinforce agents’ long-term effort expectations; conversely, stochastic inspections are optimal for failure-avoidance–oriented tasks, as they mitigate strategic window-avoidance behavior. This reveals a structural alignment mechanism between inspection predictability and task type, providing a rigorous theoretical foundation for differentiated regulatory policy design.
📝 Abstract
Inspections are frequently conducted to reveal information about agents' otherwise unobserved actions. Some inspections occur at pre-announced times; others are surprises. We show how the productive role of the inspected agent determines whether predictable or random inspections are optimal. If the agent's main task is achieving a breakthrough---think of an entrepreneur investing in an innovative industry---then predictable inspections are optimal. If the main task is avoiding a breakdown---think of a financial institution managing its risk in order to avoid default---then random inspections are optimal.