Abstract
People's judgments and decisions often deviate from classical notions of rationality, incurring costs to both themselves and to society. Previous research has proposed that the cost of these biases can be reduced by redesigning decision problems based on theories of human decision making. These modifications-or nudges-can have dramatic results and have been successfully applied to variety of domains. However, the formal underpinning of nudge theory is limited, and it is not always clear what the effect of a nudge will be before it is implemented. As a result, designing nudges can be time consuming and error-prone. In this paper, we propose an automatic method for deriving optimal nudges. The method is based on a resource-rational model, which assumes that people make decisions in a way that achieves a near-optimal tradeoff between the cost and benefits of deliberation. We then frame nudges as modifications to the costs of different cognitive operations, encouraging the cognitively frugal decision maker to consider some problem features over others. As a proof of concept, we apply the method to the Mouselab process-tracing paradigm, finding that optimal nudges lead participants to make better decisions with less cognitive effort.
Original language | English (US) |
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Pages | 2348-2354 |
Number of pages | 7 |
State | Published - 2020 |
Event | 42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020 - Virtual, Online Duration: Jul 29 2020 → Aug 1 2020 |
Conference
Conference | 42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020 |
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City | Virtual, Online |
Period | 7/29/20 → 8/1/20 |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Science Applications
- Human-Computer Interaction
- Cognitive Neuroscience
Keywords
- decision making
- decision support
- nudging
- resource rational analysis