This past week I gave a talk to students part of the Hong Kong UST DBA Program regarding implementing behavioral finance initiatives in companies. The talk covered some case studies that varied in different dimensions relative to the degree of integration of science and degree of organizational complexity. I have often emphasized that organizations that want to implement behavioral initiatives need to consider dimensions of Goals, Research, Innovation, and Testing (GRIT) among other behavioral-specific considerations (e.g., choice, information, process, and personalization architecture).
However, one of the most striking parts of the discussion for me surrounded the notion of ethics, which has come up a number of times in my discussion with students.
Although I was only able to touch on two angles in my HKUST talk, for the core classes I teach, I offer at least three different lenses for thinking about behavioral economics and ethics: 1) goal alignment between the company and the end user, 2) nature of behavioral intervention design (e.g., how much control does it exert), and 3) moral foundations and considerations (e.g., care/harm, fairness).
There are clearly other considerations that could come into play (e.g., to what extent comfortable sharing behavioral intervention thinking publicly; legal versus ethical 2×2). However, it is good that students think through ethical considerations. Things aren’t always as black and white as we’d might like, so it’s important to have multiple lenses through which one can evaluate situations.
Based on request from a Cornell student, last night I gave a talk to the Phi Sigma Pi National Honor Fraternity. The talk was entitled, “The Future You” and was themed around different career and leadership lessons that I experienced over more than three decades of work experience from engineer to management consultant to applied behavioral economics expert.
As part of the talk, I posed the question to students as to whether they could predict where they would be or what they would like, say several decades from now.
To set the context as to how well people can predict their tastes, I described a study by Kahneman and Snell. In the study, participants were first given a sample taste of plain yogurt and asked to rate how much they liked it. Participants then committed to eating a full serving of yogurt every day for about a week. They were also asked to predict how much they thought they would like the yogurt over the next week.
During the sampling phase, people had some dislike of the yogurt and predicted that their dislike would get even worse over the course of eating yogurt for a week.
However, what actually happened? People had very strong dislike of eating yogurt on Day 1 and the trend went in the opposite direction than predicted where liking improved over time instead of worsening. By the end of the week, while people were still somewhat negative on liking plain yogurt, by Day 8 their degree of liking was higher, even higher than what they reported during their first sample taste.
If we can’t predict our tastes for something simple like plain yogurt over the course of a short period of time like a week, then what are our chances of predicting things over a long horizon or even more modest time horizons?
My takeaways were the following:
Forecasting is hard, even forecasting the future you.
Interests, preferences, and skills develop and compound over time, so invest in them.
Find environments where you can learn and experiment (e.g., sometimes longer drive tests can be helpful).
To increase the chances of success and minimize the chances of overlooking blindspots, leverage 3rd party perspectives and out-of-the-box thinking tools from time to time.
Reference: Kahneman, Daniel, and Jackie Snell. “Predicting a changing taste: Do people know what they will like?.” Journal of Behavioral Decision Making 5, no. 3 (1992): 187-200.
My level of awareness was heightened this week by correspondence with the brilliant Mac Hodell (former Principal at BCG). We were exchanging ideas about definitive references for management consultants related to the visual aspect of presentations. To cut a long story short, he brought up the powerful idea of maximizing the Insight:Ink ratio on slides.
How might this work? One could add ink to a presentation slide while increasing insight dramatically. For example, instead of using generic slide titles such as “Financial Impact,” it’s more effective to use specific titles that answer the “so what” question. As such, instead of leaving readers to draw their own conclusions, use titles that clearly state the outcomes of the work, such as “Our fall study of three behavioral interventions resulted in adding $250 million in AUM.” On the other hand, subtracting unnecessary ink from a graph on a slide might also work well, such as removing grid lines or merging overlapping legend information into the graph itself. There are also the aspects of substituting, synthesizing, aligning, or even redoing the content completely.
In a study by Mavis and Yoon, they posed a question to participants as to how they would change a Lego structure so that they could put a heavy object on top of it without crushing the figurine, recognizing that each block added would cost another $0.10. What did participants suggest? The title of the journal article, spells out people’s tendencies loud and clear, “Adding is favoured over subtracting in problem solving”. People tend to have an additive bias, and this could inadvertently lead to poorer design.
This concept leads to maximizing impact ratios. In the behavioral world, one technique I teach students is to look at user journeys and touchpoints with users. Is each word needed in this email copy to drive engagement? How can we maximize Impact:Text? What about thinking about the end user and that their attention and time are limited? How to maximize Impact:Time? What about this complicated feature in the product? Can we cut down on the features and maximize the Impact:Features?
In closing, the rule of thumb is to subtract if possible, add if you must, and focus on maximizing the Behavioral Impact Ratio.
Reference: Meyvis, Tom, and Heeyoung Yoon. “Adding is favoured over subtracting in problem solving.” Nature (2021): 189-190.
Yesterday I outlined five primary areas of behavioral architecture that we would cover during the academic semester. Two prominent ones we covered included choice architecture (e.g., how choices are presented, such as with respect to defaults and number of choice options) and information architecture (e.g., how information is presented, such as percent of salary versus pennies for every dollar you earn).
As a third area, I framed thinking architecture as the process by which an architect tries to encourage end users to use more slow, reflective thinking versus fast, intuitive thinking. A classic question that tries to illustrate this is the following:
A bat and ball together cost $1.10. The bat costs $1.00 more than the ball. How much does the ball cost?
It is tempting for many people to think that the ball costs $0.10 (based on fast, intuitive thinking), although the correct answer is $0.05.
One form of thinking architecture could have been to get users to follow a checklist:
Write down your guess for the ball cost (e.g., $0.10).
Add $1 to the ball cost and write that number down as the bat cost (e.g., $1.10).
Add the ball and bat cost (e.g., $1.20)
If the numbers don’t add up to $1.10, repeat step 1.
In my view of thinking architecture, we are essentially trying to slow down both the brain to try to get the mind to follow certain thinking pathways. Whereas neoclassic economics doesn’t account for thinking pathways, path dependency is everything in psychology and affects behavior.
Implementations of thinking architecture space have been less explored. However, the possibilities are endless. Some examples include:
Addressing complexity (e.g., aviation pre-flight checklists for pilots)
Helping to avoid common errors (e.g., blindspots such as forgotten opportunities as to how I might want to use money in retirement or risks of underinsuring myself when younger)
Expanding the thinking (e.g., are there other potential ways of realizing goals during retirement)
Weighing difficult tradeoffs (e.g., should the money be used to save a single life or implement an equipment upgrade)
Thinking architecture requires thoughtful design, and it is not always the easiest to implement. However, the human mind is an amazing wonder. Sometimes we need to really tap into its power.
References: Frederick, Shane. “Cognitive reflection and decision making.” Journal of Economic Perspectives 19, no. 4 (2005): 25-42.
Although I’m no expert in boats, whether you anchor from the bow or stern can have a big impact on outcomes. Do it the right way (based on the design of the boat), you can keep your boat roughly confined to an area. Do it the wrong way, and you could flood, capsize, or sink your boat. When the anchor is up (e.g., removed from use), there are possibilities with a skilled person taking the boat out to new destinations. If you are unskilled, then keeping the boat anchored and at port might make a lot more sense, unless you get someone skilled to help or provide guidance.
Shifting to the world of human behavior, one classic example of the impact of anchoring on judgments was done by Jacowitz and Kahneman, where they asked people to guess the height of the tallest redwood tree. The wrinkle was that for a first group of people, before they were asked to guess the height, they were first asked whether the height of the tallest redwood was more or less than 180 feet. A second group of people followed the same process, except that instead of the 180-foot anchor, a much taller, 1200-foot anchor was used.
Evidence shows that people’s judgments were dramatically affected by the anchor. People who were first asked about the 180-foot anchor guessed the tallest redwood to be 282 feet high. On the other hand, those who first got the 1200-foot anchor guessed the tallest redwood to be 844 feet high, about 3 times higher.
The implications of anchors in real world applications can be large. For example, in another post, my colleague, Prof. Shlomo Benartzi, is covering a case of the defaults displayed during retirement election processes and how they affect outcomes.
One area that I’d like my students to consider has to do with how they elicit information, especially during customer discovery research to determine whether a business is addressing a big problem that people have (e.g., is the business solving the right problem, a common question of startup situations). As an example, here are two ways of eliciting information from customers to evaluate a new product:
On a scale of 1 to 10, to what extent would you consider X?
On a scale of 1 to 10, to what extent would you change your current plans and consider X?
How the questions are elicited can have different consequences. For example, the second question is anchoring on a prospective customer’s current plans. This could be appropriate in some cases because in reality, behavior could be hard to change, and so anchoring on current plans might be a more accurate or at least a conservative strategy for assessing the situation.
Here are some key things to think about:
Anchors can affect judgments (e.g., what do people think, what impressions are formed in their minds).
Anchors can affect decisions (e.g., what do people actually do).
Since they affect both judgments and decisions, it is appropriate to consider the role of anchoring on behavior throughout business processes (e.g., customer discovery, customer validation, go-to-market)
The effect of anchors will likely be dependent on domain (e.g., finance), application (e.g., retirement), and context/conditions (e.g., regular or infrequent decision of customers).
So carefully consider anchors because they are everywhere. Most importantly, they can represent both possibilities and peril.
Photo: Me at home sitting on the floor next to the front door chime enclosure (shaped as an anchor).
Reference: Jacowitz, Karen E., and Daniel Kahneman. “Measures of anchoring in estimation tasks.” Personality and Social Psychology Bulletin 21, no. 11 (1995): 1161-1166.
This Sunday morning, I tried to spend time thinking about how I could help people. Could I help someone to get a job? Perhaps make a valuable introduction for someone? Give them an insight? I wrote some messages to take action. It felt good to apply energy to that type of effort.
Reciprocity is a powerful influence on behavior. The idea is that when you give to someone, they are more likely to be receptive to giving something back in the future.
In a classic study of reciprocity and measuring the percentage of tips after dinner in an upscale restaurant, the server delivered the guest check to diners using four methods:
delivery of guest check normally (a “control condition”)
delivery of guest check with one small piece of candy, which resulted in 3.4% higher tips
delivery of guest check with two small pieces of candy, which resulted in 14.0% higher tips
delivery of guest check with one small piece of candy initially with server returning to provide another piece of candy “just for them”, which resulted in 21.3% higher tips.
In other words, giving candy resulted in a reciprocal response of higher tips. The percentage went up when giving was larger. And unbundling the giving (e.g., 1+1 > 2) by making it unexpected and personalized, results in the largest results.
Moving outside of the upscale dining scenario, just imagine if things were applied in a virtuous circle context? This made me think of the Grand Challenges Project courses at Cornell where companies offer real and safe situations for students to try to solve problems that address any of 17 UN Strategic Development Goals (SDG). Students apply their skills to help. (This picture is of me holding a lapel pin that I received as a gift representing the 17 SDGs. Now I need to do my part.)
Here are some key takeaways:
Incorporate a regular habit of thinking about ways to help others without expectation of anything in return.
The more you give, the more likely reciprocity will occur in the future.
Unexpected, personalized giving can increase the impact of giving.
Apply the principles of reciprocity in a virtuous circle context, such as through projects or initiatives that address global issues.
Embrace reciprocity as a powerful tool for positive change and actively incorporate it into your actions and interactions with others for the benefit of others.
Reference: Strohmetz, David B., Bruce Rind, Reed Fisher, and Michael Lynn. “Sweetening the till: the use of candy to increase restaurant tipping 1.” Journal of Applied Social Psychology 32, no. 2 (2002): 300-309.
Our waitress pleasantly asked, “Would you like small or vacation sized cocktail?”
These were potent words that nudged my wife and I to select 16oz versus 12oz drinks just the other day. We’ve been in the Caymans on vacation, on a “Cay-vay-tion”.
More than thirty years ago, I principally thought like a traditional engineer. 12oz is 1.5 cups. 16 oz is 2 cups.
Then I magically met my wife and started a journey of getting in touch with my feelings. I started to learn about how soft skills and words matter. Sometimes its about cognition and comprehesion of an audience. Other times its about conveying feelings, connections, and story arc.
Returning to lessons from our waitress, the framing of vacation versus small sized is brilliant. One lever with respect to nudge design is being aware of the power of affect. Affect is essentially about immediate feelings that a person experiences (e.g., good or bad), such as based on a nudge. For nudge design, especially be aware of:
Reference points (e.g., small versus large)
Mental associations (e.g., widespread notions like happiness or other mental associations and metaphors taught in marketing and brand management, such transformation or scarcity/exclusivity)
Social (e.g., framing connections to others or others as reference points)
Just-in-time (e.g., proximity at the point of decision)
Our waitress hit the nail on the head with a number of these points. So long as she nudges for good, she’ll likely be a winner.
The bottom line is to go beyond where I was as a “traditional engineer”. Consider affect as part of nudge design. Furthermore, build a team with members that are acutely aware of psychology, mental associations, customer experience, and design.
For Dyson students looking to get a short summary of what is different about my AEM 4000 section, here is short blurb:
The theme for this section is around Behavioral Economics and Human Behavior. This section is designed to introduce students to the field of behavioral economics and how it can help us understand and influence human behavior. Through a combination of lectures, discussions, and hands-on projects, students will learn about key concepts like heuristics and biases and how to apply some of the ideas to real-world situations. Students are not expected to have prior training in behavioral economics. Core training will be delivered through lectures, discussions, and reading assignments. Topics will include heuristics, biases, role of behavioral architecture, and consultative methods. Core training will also cover selected behavioral economics cases that address societal issues, which can enhance creativity and help students to take broader perspective on the application of behavioral principles. In addition to the core training, students will work on sponsored projects. While the problem statement and deliverables for sponsored projects vary by semester, each project should involve applying principles from at least one area of consumer research, behavioral research, behavioral audits, experimental testing, and/or solution design. Students will receive project support to help with skills development, knowledge development, and project navigation (e.g., through coaching, pointers to prior research insights and frameworks).