Podcast Interview on Behavioral Investing: Managing the Emotions Behind Our Decisions

Last month I chatted with Tony Roth, Chief Investment Officer at Wilmington Trust, N.A., a subsidiary of M&T Bank (NYSE: MTB) as part of his podcast series, Capital Conversations. For me, it was an interesting conversation to have had for a number of reasons, and three perspectives really captured the direction of my thinking. 

The first perspective was that as a society we have really been under a lot of stress for the past two years, a type of stress that I have not seen in my lifetime. So while investment markets are not currently very volatile, it is a good time for many people to get a fresh start and re-assess their situations.

The third perspective is that people are really different, and sometimes it can matter a lot. We understand some of these differences better than others (such as innumeracy and its impacts). There are other differences (like capability and confidence mismatches relative to new technologies, like cryptocurrency) that are less understood. As another example, the younger generation thinks about finance and life very differently than older generations. How to better address individual behavioral differences and situations will be an ongoing opportunity where people will need help.

The second perspective was that there are so many different behavioral elements at play when we think about different people, the interplay of fast, automatic thinking versus slow, reflective thinking; the digital world, and the numerous challenges of finance. It is unlikely that we can find one silver bullet, behavioral solution to fully address all problems. That said, we can put in place processes to help ensure that we make the best decisions we can for the things that really matter, while also avoiding some of the major obstacles that happen on a regular basis, such as overconfidence,  natural biases in forecasting the future, thinking in narrow frames, and others.

Thanks to Tony Roth and the Wilmington Trust team for hosting me for the podcast.

Good Papers on Machine Learning and Economics

Here are some overview papers that may be of interest to people that are learning about ties between machine learning and economics. Admittedly, it helps to have some formal background on causal inference to read these papers.

For more background on causal inference, I highly recommend the book by Scott Cunningham, Causal Inference.

Recent article: “The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains”

In one of my prior Applied Behavioral Economics lectures, I mentioned the notion of not only looking at individual studies but also finding meta studies (essentially studies of studies) to help inform behavioral perspectives. This article covers a meta study of behavioral architecture interventions: https://www.pnas.org/content/119/1/e2107346118 

In this article, there were two observations that really stuck out to me:

1) Considering a range of domains (health, food, environment, finance, prosocial) where behavioral architecture is applied, there is the highest effect on food choices and lowest effects in the financial domain; effects are potentially moderated by domain because of lower behavioral costs and lower perceived consequences in the former versus higher behavioral costs and higher perceived consequences in the latter. 

2) Decision structure changes (choice architecture) outperforms decision information (information architecture) and decision assistance approaches, potentially because choice architecture approaches require less demand on cognitive information processing, and there is low susceptibility to individual differences and goals. (But remember that we will start to address personalization and individual differences in upcoming classes).

If you are interested in learning more about meta studies and how to do them, I highly recommend the book: Borenstein, Michael, et al. Introduction to meta-analysis. https://www.amazon.com/gp/product/0470057246/ref=dbs_a_def_rwt_bibl_vppi_i2 

What Can User Experience (UX) Designers Learn from the Field of Behavioral Economics?

This post is based on a question that I answered previously on Quora.

Although it’s not exclusively from the realm of behavioral economics, the notion of A/B testing is something that I often try to work with companies to include. On the one hand this includes the capabilities of companies to integrate specific aspects of their product management, software development, UX, data science, and marketing processes. But it also means developing a research mindset that comes from the experimental side of behavioral economics. For example, if one really wants to nail down which aspects of a UX or customer experience affect behavior and outcomes, the gold standard is using randomized assignment, A/B testing, and discipline that between testing conditions only one item is changed. In setting up the A and B test conditions for a behavioral insights based UX isolation test, one can add, subtract, or substitute a single element between two test conditions. If you change more than one element, then your findings will be confounded between the multiple elements changed, and you won’t be able to tell what change worked or didn’t. UX teams should become used to working in worlds that include testing harnesses like Visual Website Optimizer, Optimizely, and the like.

For a little more on A/B testing, see this WSJ article by one of my colleagues. It describes a simple, but extremely powerful A/B test we worked on with a FinTech company’s UX. It’s Time to A/B Test Your Financial Life

If you are interested in other aspects related to the digital UX world and behavioral economics, you might also want to check out a book that was written by two of my colleagues: The Smarter Screen: Surprising Ways to Influence and Improve Online Behavior.

What Does a Chief Behavioral Officer Do?

This post is based on a previous question posed to me on Quora.

The role of a Chief Behavioral Officer (CBOs) varies, but a common theme I’ve seen is that they analyze, plan, innovate, and implement aspects of the business using insights and methods from the behavioral sciences (e.g., behavioral economics, psychology). Some of the companies with CBOs do mostly marketing communications or thought leadership (e.g., research) while others may get involved with bringing insights and designs to product development (e.g., applied research). Some CBOs may directly manage people, such as a team of PhDs, analysts, etc. as well as partnerships (e.g., with academic researchers). The approach of CBOs may also vary in terms of the science. For example, some may leverage pre-existing research. Others may work with big data (e.g., proprietary) and correlational or instrumental variable type analysis. Yet others may take an experimental approach (e.g., A/B testing) and work with product and service teams to directly measure how designs affect behavior and outcomes.

A key aspect of determining the activities of the CBO really come down to setting goals for the larger organization, assessing gaps and resources, and developing a tactical plan to meet the goals over time. As an example, for the past few CBOs I have helped, we often worked to develop 30–60–90 day plans to initially get the organization rolling with longer-term planning and thinking happening in parallel.

Behavioral Science Casebook Project

In my free time I have been developing a course, tentatively called Applied Behavioral Science in the Digital Age to be taught to business school students at either the undergraduate or graduate level. In the course, students will study how the pervasive reach of digital technology into our lives affects our heuristics, biases and other behavioral patterns. In addition to learning about behavioral science theories in the digital age, students will then learn how to apply those key theoretical concepts through discussing actual, corporate case studies and participating in hands-on exercises related to nudging and experimental design. The class will discuss key elements to starting and implementing behavioral science initiatives within a company. The course will be especially geared toward those interested in professional careers within consulting, product development, marketing, services, and technology app (e.g., FinTech) settings.

As related to that course, I have started to develop a short book that will cover specimens and cases based on the real world, such as sample websites, app designs, email campaigns, and customer journeys with ideas about how to evaluate such designs though the lens of behavioral science. If you have interesting examples and specimens for me to consider including (can be disguised or made anonymous as needed), please feel free to correspond with me at sds77@cornell.edu. If the specimen is from your company and you are interested, I can potentially perform a behavioral audit on the materials provided.

Podcast Interview on 401(k) Plans and Behavioral Finance Trends

Thanks to Rick Unser for having me recently on his 401(k) Fridays podcast. This interview is geared toward defined contribution plan sponsors and those closely related with this segment of the market (e.g., advisors, consultants, recordkeepers, investment only). I do draw from some insights and activity that is occurring in other areas of the financial services market (e.g., retirement income, wealth management). The podcast may be found at:

Apple Podcasts – https://apple.co/2EAsw5J
GooglePlay – http://bit.ly/2Hfsgfa
Stitcher – http://bit.ly/2Uj08eT

Additionally, some references that I allude to on the podcast which may be of interest to folks include the following:

When Implementing Behavioral Science, What Is the Role of a Choice Architect?

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.