Stephen Shu, PhD

This post is based on an answer I wrote in response to a question posed to me on Quora, “What do choice architects do?” I wanted to repost my answer here because I still feel there is a lack of understanding about what it means to implement nudging and behavioral science within companies, and the role of choice architects are key.

Choice architects essentially use insights from behavioral science to design environments for people that encourage or support some sort of end goals.

For example, suppose there are a layered set of three main goals to encourage people to 1) participate in a retirement savings plan, 2) save enough money, and 3) invest wisely. A choice architect may address behavioral obstacles that may hamper these goals from being met through creating solutions. These solutions could include auto-enrolling people into a retirement plan (versus having them opt-in) to address behavioral obstacles associated with status quo biases that hamper participation in a saving plan. In order to get people to get to healthy saving rates over time, the architect may create a way for people to commit today to savings increasing in the future (a process which addresses psychological biases associated with present bias and hyperbolic discounting). Finally, an architect may default most people into an automatically managed, diversified portfolio that evolves as the person reaches and continues into retirement. This essentially makes a healthy investment choice easy as a default for most people and for most of their money.

So choice architects do the following things:

1. They identify goals of all constituents, any guardrails (e.g., ethical, philosophical, financial), and desired outcome measurements.
2. They look for behavioral obstacles that people face in whatever environment is being addresses or designed (e.g., financial spending, medication adherence, governmental compliance).
3. Architects try to leverage behavioral science research where they can (e.g., to inform the precise nature of obstacles, potential ways to address).
4. They innovate and try to create solutions and interventions to address behavioral obstacles (e.g., website design, text messages, email content, customer outreach, product design, decision tools).
5. Architects also look to measure and perform A/B testing where they can to see how solutions and interventions impact outcomes.

I only recently learned about the term “boosting”. Boosting takes a different worldview of addressing a person’s competencies whereas nudging tends to address immediate behavior. There does appear to be some overlap between boosting and System 2 nudges (where the nudge tries to engage a person’s slow, reflective thinking). There is also overlap between short-term boosting and educational nudges. However, long-term boosting is about building a person’s competencies (e.g., teaching them, giving them tools, getting competencies to persist even beyond the immediate decision point). A boost appears to necessarily require both transparency of the intervention and cooperation of the person who is a target of the boost. Those advancing the concept of boosting admit that boosting may actually be more costly to implement and less effective on affecting immediate behavior as compared to nudges.

For more details on boosting, I recommend starting with the following paper.

In recent study I having been working with Hal Hershfield and Shlomo Benartzi at UCLA, we worked with a FinTech company that had its roots in providing a mobile app to Millennials to get them to save incremental money through rounding up purchases. For example, if you bought a cup of coffee for $4.55, you could round things up to $5.00 and save the incremental $0.45.

We wanted to introduce the concept of a recurring savings feature, where people could save a specified amount of money at regular intervals. As part of that effort, we constructed an experimental design and A/B/C test where during the sign-up process, users were randomly assigned to one of three treatments where they were given an opportunity to save: A) $150 per month, B) $35 per week, or C) $5 per day. At the heart of the design is the notion of presenting essentially equivalent information but using temporal reframing to present the choice option differently. Our hypothesis is that the $5 per day treatment would yield the most success in terms of sign-ups for recurring savings. So we use traditional statistics to show that the difference in sign-ups between these treatment conditions is statistically significant. In this case, we provided evidence that sign-ups were 4x higher using the daily frame and that we could close an income discrimination gap of 3x between the highest and lowest income users in terms of percentage of people saving comparatively between the monthly and daily temporal framing. More details on the study can be found here: Temporal Reframing and Savings: A Field Experiment

In other studies I am involved with related to the different framing of information and savings, I measure not only outcomes like savings rates (i.e., what people do and choose) but also people’s thoughts, perceptions, and mental associations regarding the financial decisions (i.e., the psychology and process). Using statistics to better understand the underlying psychology behind people’s decisions can help inform one in providing better user experiences (e.g., to improve outcomes, reduce confusion, increase confidence).

Statistics can be a very powerful tool to have when trying to analyze messy things like social science processes and human decisions. Companies are starting to ramp-up their data science capabilities a lot more, and while I think much more can be done in terms of incubating behavioral science initiatives, I think the shift to data science will be here to stay for quite awhile.