Optimal nudging for cognitively bounded agents: A framework for modeling, predicting, and controlling the effects of choice architectures

People's judgments and decisions often deviate from classical notions of rationality, incurring costs both to themselves and to society. One way to reduce the costs of poor decisions is to redesign the decision problems people face to encourage better choices. While often subtle, these \emph{nudges} can have dramatic effects on behavior and are increasingly popular in public policy, healthcare, and marketing. Although nudges are often designed with psychological theories in mind, they are typically not formalized in computational terms and their effects can be hard to predict. As a result, designing nudges can be difficult and time-consuming. To address this challenge, we propose a computational framework for understanding and predicting the effects of nudges. Our framework builds on recent work modeling human decision-making as adaptive use of limited cognitive resources, an approach called \emph{resource-rational analysis}. Concretely, nudges change the optimal sequence of cognitive operations an agent should execute, which in turn influences the agent's behavior. We first show that our framework can account for known effects of nudges based on default options, suggested alternatives, and information highlighting. In each case, we validate the model's predictions in an experimental process-tracing paradigm. We then show how the framework can be used to automatically construct optimal nudges, and demonstrate that these nudges improve people's decisions more than intuitive heuristic approaches. Overall, our results show that resource-rational analysis is a promising framework for formally characterizing and constructing nudges.