Data-Driven Monitoring and Deterrence in a Changing Environment

TL;DR

This paper models how a principal uses historical infractions to adapt monitoring strategies in a dynamic environment, revealing that endogenous data collection enhances deterrence and reduces infractions.

Contribution

It introduces a bandit model with a hidden Markov process to analyze endogenous data collection and its impact on monitoring incentives and deterrence.

Findings

Myopic data use makes historical data valueless.

Endogenous incentives motivate persistent monitoring.

Data-driven vigilance lowers infraction rates.

Abstract

We study a dynamic model in which a principal monitors agents based on historical data of infractions. This data informs when and at what intensity to monitor; the monitoring decision, in turn, selects the collected data, shaping the principal's future learning. We analyze this feedback loop using a bandit model in which the underlying monitoring environment evolves according to a hidden Markov process. Because data collection is endogenous, how the principal uses this information is critical: surprisingly, a myopic approach renders historical data completely valueless. By endogenizing the agent's incentives, we demonstrate that the principal's purely informational motive to explore serves as an endogenous commitment device. This inherent drive to gather data compels persistent vigilance, strictly lowering the equilibrium infraction rate and restoring the power of deterrence.