TL;DR: This episode of “The Tao of Chao” podcast features Dr. Stephen Shu, a specialist in behavioral economics. The discussion explores how our decision-making processes are influenced by biases and cognitive frameworks rooted in our primal survival instincts. With the increasing volume of information and opinions available, our brains struggle to process it all, leading to echo chambers and confirmation biases. The conversation highlights the importance of recognizing our fast and slow thinking capabilities and encourages reflective thinking to counteract these biases and make better decisions in an ever-changing world.
TL;DR: A thinking tool called “prospective hindsight” can be used to explore different outcomes by imagining a future event and examining the steps that led to it. Avoidance in decision-making can stem from complexity, trade-offs, or a reluctance to consider negative outcomes. While it is impossible to predict the future accurately, a detailed planning process that considers both logic and emotions can help make more informed decisions. In investing, scenario planning and understanding the transmission mechanisms of events can improve decision-making, but it is essential to be aware of biases and actively seek counterfactual information. The abundance of data does not guarantee better decision-making, and the importance of information depends on the context and the significance of the decision.
A student recently asked me whether design thinking was different from behavioral economics thinking. In a nutshell, I believe the disciplines complement one another and should not be viewed as separate islands. That said, insights from behavioral economics and psychology can help us to become more thoughtful designers of products, customer experiences, etc.
One important behavioral area to consider is the role of memory in judgment and decision-making processes. Last week at Cornell Tech I facilitated a brief discussion of a new startup that was trying to address the issue of helping people to have perfect memory (as opposed to ever forgetting things). To what extent is this the perfect idea? What are some considerations from behavioral science?
To help feed that discussion, I relayed some results from a study by Eric Johnson and colleagues that provides substantiation for one theoretical role of memory in the decision-making process. Their study was conducted in the context of people valuing a commodity item, a coffee mug. In the classic example of the “endowment effect”, which is that people value things more when they possess an item (e.g., are given or endowed with a mug), people endowed with a mug, valued mugs at $6.01. People who were not endowed with a mug, valued mugs less at $3.72.
However, things got more interesting when the researchers manipulated the natural, unguided memory retrieval process. They manipulated the process by reversing the order in which people thought about things. Before having people value the mug, they essentially asked sellers to think about negative aspects of the mug, and they asked buyers to think about positive aspects of the mug. The endowment effect essentially vanished with sellers now valuing the mug at $5.05 and buyers valuing the mug at $4.91.
So memory retrieval order matters. If we go back to the startup example, and we have artificial intelligence (AI) based products remembering stuff so that we can make decisions, how should designers determine what to present to us first, knowing that presentation order may influence our decisions? Could presenting too many memories cause decision paralysis? If one is to pursue a product like the one described, the design choices are not trivial.
The bottom line is that hopefully we can improve our design reasoning by remembering to factor in insights from behavioral science.
Reference: Johnson, E.J., Häubl, G. and Keinan, A., 2007. Aspects of endowment: a query theory of value construction. Journal of experimental psychology: Learning, memory, and cognition, 33(3), p.461.
Let me first preface this post by saying two things: 1) the state of research in this area is very young (e.g., most citations in 2022 and 2023), and 2) my summary will be at risk of oversimplifying things and missing some nuance.
The students I have at Cornell Tech are really sharp and energized. In one class, an interesting question was raised about whether AI could be used as part of the testing process, such as to A/B pre-test interventions. To try to get a better understanding of this space, I sought to do a little research on to what extent AI decision making resembles human decision making. So today I shared findings from a working paper that I recently read (Chen et al., 2023). The paper covers 18 common human biases relevant to operational decision-making (e.g., judgments regarding risk, evaluation of outcomes, and heuristics in decision making, such as System 1 versus System 2 thinking).
Here’s a summary of differences between ChatGPT and humans:
Judgments Regarding Risk – ChatGPT seems to mostly maximize expected payoffs with risk aversion only demonstrated when expected payoffs equal. It does not understand ambiguity. Also, ChatGPT exhibits high overconfidence, perhaps due to its large knowledge base.
Evaluation of Outcomes – ChatGPT is sensitive to framing, reference points, and salience of information. No sensitivity to sunk costs or endowment effect (e.g., may not have physical or psychological ownership concept).
Heuristics in Decision Making – More research needed, although aspects such as confirmation bias present. Additionally, ChatGPT has the ability to generate both classic System 1 responses (incorrect answers by humans typically driven by fast, automatic thinking) and System 2 responses (correct answers, such as those by humans which typically require more slow, reflective thinking).
While the reasoning for these modes of responses is not fully known, it seems as though ChatGPT is extremely logical when it comes to things like maximizing expected value. However, perhaps due to its nature of trying to be conversational and responding to salient information provided by the user, it can be overly sensitive to framing effects.
There are surely a lot things to think about, opportunities to pursue, and research to pursue.
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.
How to Improve the Stability of a Lego Structure?
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.
