NUDGES Checklist (iNcentives, Understand mappings, Defaults, Give feedback, Expect error, Structure complex choices)
Six principles for designing any decision environment effectively
The NUDGES checklist is a practical mnemonic for the six core principles of effective choice architecture. Each letter stands for a design principle that addresses a specific way humans predictably struggle with decisions. Together, they form a complete toolkit for auditing and improving any decision environment.
iNcentives asks whether people face the right incentives and whether those incentives are salient and visible at the moment of choice. Understand mappings asks whether people can translate abstract options into concrete outcomes they care about. Defaults addresses the most powerful tool in choice architecture: what happens when someone does nothing. Give feedback ensures people know how their choices are playing out. Expect error builds tolerance for the inevitable mistakes humans make. Structure complex choices provides strategies for simplifying overwhelming option sets.
Applied systematically, this checklist can transform any confusing, friction-filled decision process into one that naturally guides people toward better outcomes while maintaining their freedom.
- Make the costs and benefits of each choice visible and salient at the moment of decision
- Help people translate options into outcomes they can understand and compare
- Choose defaults that serve the interests of the typical chooser
- Provide clear, timely signals about whether current choices are working
- Design systems that are forgiving of inevitable human errors
- Audit iNcentivesExamine whether the people making the choice bear the full costs and benefits of their decisions, and whether those incentives are visible at the moment of choice. Often the person choosing is not the person paying, or the costs are delayed and invisible. Make the true cost of each option salient.Pro tipAsk who uses, who chooses, who pays, and who profits. When these are different people, incentive misalignment is likely and the need for nudging is greater.
- Improve Understanding of mappingsDetermine whether people can map the options to outcomes they actually experience. For technical or abstract choices, translate into concrete, personal terms. Instead of megapixels, show print sizes. Instead of deductible amounts, show expected annual out-of-pocket costs for common scenarios.Pro tipThe RECAP (Record, Evaluate, and Compare Alternative Prices) approach works well here: require providers to disclose usage data in a standardized, machine-readable format so third parties can build comparison tools.
- Set smart DefaultsFor every choice point, determine the best default for someone who does not actively choose. Consider whether the status quo default or a back-to-zero default serves people better. In some cases, forced active choosing (requiring a deliberate selection) is the best approach, but only when the choice is important enough and simple enough to justify the effort.WarningNever use random defaults for consequential decisions. Maine's intelligent assignment approach for Medicare drug plans dramatically outperformed the random assignment used by other states.
- Give feedback on current choicesDesign systems that tell people how their current path is working out. Feedback should be timely, clear, and actionable. Good examples include the paint that changes color to warn your walls are getting too hot before a fire starts, or credit card statements that show how long it will take to pay off a balance at the minimum payment rate.
- Expect error and design for itAssume that people will make mistakes: they will forget steps, misunderstand instructions, and fail to follow through. Build systems that catch errors before they cause harm. ATMs that return your card before dispensing cash prevent the common post-completion error of leaving cards behind. Prescription bottles with color-coded caps for each family member prevent taking the wrong medication.Pro tipLook for 'post-completion errors' specifically: mistakes that happen after the main goal is achieved, when attention drops.
- Structure complex choicesWhen people face many options with multiple attributes, provide tools to simplify. Use collaborative filtering (recommendations based on similar people's choices), elimination strategies (filter by must-have features first), or tiered disclosure (start simple, allow drilling down). Avoid presenting all options at maximum detail simultaneously.Pro tipThe Netflix recommendation approach is ideal: rather than asking people to browse thousands of movies, surface personalized suggestions based on patterns.
Early ATMs dispensed cash before returning the card. After the main goal (getting cash) was achieved, attention dropped and many people walked away without their cards. Designers restructured the sequence so the card must be retrieved before cash is dispensed.
The original fuel economy sticker showed MPG numbers that were difficult for consumers to translate into meaningful cost differences. The EPA redesigned the label to prominently display estimated annual fuel cost in dollars and show where the vehicle falls within its class range.
Thaler and Sunstein developed this framework as a practical distillation of their broader choice architecture theory. They recognized that while the underlying behavioral science was well-established, practitioners needed a memorable, actionable tool they could apply across domains. The mnemonic was designed to be self-referencing: a nudge about how to nudge.
Each principle in the checklist emerged from specific research findings and real-world failures. The 'Expect error' principle, for example, was partly inspired by the 'post-completion error' research showing that people routinely forget to complete the final step of a process, like leaving their original document on a copy machine or their card in an ATM.