TY - JOUR
T1 - Optimal Nudging for Cognitively Bounded Agents
T2 - A Framework for Modeling, Predicting, and Controlling the Effects of Choice Architectures
AU - Callaway, Frederick
AU - Hardy, Mathew
AU - Griffiths, Thomas L.
N1 - Publisher Copyright:
© 2023 American Psychological Association
PY - 2023
Y1 - 2023
N2 - People’s decisions often deviate from classical notions of rationality, incurring costs to themselves and 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 nudges can have dramatic effects on behavior and are increasingly popular in public policy, health care, 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 approach builds on recent work modeling human decision making as adaptive use of limited cognitive resources, an approach called resource-rational analysis. In our framework, nudges change the metalevel problem the agent faces—that is, the problem of how to make a decision. This changes the optimal sequence of cognitive operations an agent should execute, which in turn influences their behavior. We show that models based on this 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.
AB - People’s decisions often deviate from classical notions of rationality, incurring costs to themselves and 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 nudges can have dramatic effects on behavior and are increasingly popular in public policy, health care, 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 approach builds on recent work modeling human decision making as adaptive use of limited cognitive resources, an approach called resource-rational analysis. In our framework, nudges change the metalevel problem the agent faces—that is, the problem of how to make a decision. This changes the optimal sequence of cognitive operations an agent should execute, which in turn influences their behavior. We show that models based on this 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.
KW - choice architecture
KW - nudging
KW - resource-rational analysis
UR - http://www.scopus.com/inward/record.url?scp=85181625741&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85181625741&partnerID=8YFLogxK
U2 - 10.1037/rev0000445
DO - 10.1037/rev0000445
M3 - Article
C2 - 37917444
AN - SCOPUS:85181625741
SN - 0033-295X
VL - 130
SP - 1457
EP - 1491
JO - Psychological Review
JF - Psychological Review
IS - 6
ER -