Engagement Maximization

We consider the problem of a rational, Bayesian agent receiving signals over time for the purpose of taking an action. The agent chooses when to stop and take an action based on her current beliefs, and prefers (all else equal) to act sooner rather than later. However, the signals received by the agent are determined by a principal, whose objective is to maximize engagement (the total attention paid by the agent to the signals). We show that engagement maximization by the principal minimizes the agent’s welfare; the agent does no better than if she gathered no information at all. Relative to a benchmark in which the agent chooses which signals to acquire, engagement maximization leads to excessive information acquisition and to more extreme beliefs. We show that an optimal strategy involves “suspensive signals” that lead the agent’s belief to change while keeping it “less certain than the prior” and “decisive signals” that lead the agent’s belief to jump to the stopping region.