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Jump-Start Books for Consulting Project Classes

I teach a couple of consulting projects-type courses at Cornell. One is a Grand Challenges capstone-class for Dyson undergraduates in helping companies and organizations address one or more of the 17 UN Sustainable Development Goals. The other is a flagship-project class that is part the Masters in International Management program as part of the global CEMS Alliance, and it involves student collaborations and exchanges with 33 other top business schools and universities.

Each of these cohorts has different team compositions, problem statements, and situations to address for their client. Projects can involve diverse topics like addressing sustainability of the food supply chain and manufacturing capacity, promoting economic development and greater social equality in another continent, customer and market fit of a new product, workforce evolution given Gen Z, or marketing, branding, and product strategy for an international company.

Although projects are diverse and hard to find common bases of foundational knowledge, I have found it helpful to have bite-sized jump start material to help students get grounded. As part of the core, I have provided excerpts from my own book on bread and butter consulting concepts (for free), references to The So What Strategy for consultative communications, and a breathtaking short pocket reference to Scrum. These are all quite short books as evidenced by the spine thickness. People don’t have a lot of time to read given the heft and time pressures of projects.

Given that quite a few projects involve customer discovery, market fit, and/or diverse constituent interviews, I have decided to also add “Talking to Humans” as part of the reference books that I’ll draw from for these courses. It is a quick read book that can be completed in 1-2 hours. And it can be a great book to go back to for quick reference before doing any qualitative customer / product research.

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Tao of Chao Podcast on Behavioral Insights and Decision-Making

Below is lightly-edited, partial summary of the podcast as generated by the AI tool, YoutubeDigest which uses ChatGPT technologies.

00:01 – 25:36

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.

25:39 – 50:49

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.

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Reflections on Hong Kong UST DBA Program Session

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.

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Applied Behavioral Economics Rule of Thumb #6: Tap Into the Power of Thinking Architecture Tools To Help People With Decisions

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:

  1. Write down your guess for the ball cost (e.g., $0.10).
  2. Add $1 to the ball cost and write that number down as the bat cost (e.g., $1.10).
  3. Add the ball and bat cost (e.g., $1.20)
  4. 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:

  1. Addressing complexity (e.g., aviation pre-flight checklists for pilots)
  2. 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)
  3. Expanding the thinking (e.g., are there other potential ways of realizing goals during retirement)
  4. 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.

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Howard Marks’ Memo Spotlights Major Shift in Investing Approach, Raises Behavioral Considerations

Howard Marks has written a quite a remarkable memo that will be impossible for me to do the proper justice on. However, here are few key highlights from his memo:

  • He shares perspectives having been in investing for 53 years.
  • He has seen two major sea changes during that time.
  • One sea change included the evolution of the bond market in the late 70s and most importantly, a shift in investor mindset to thinking about risk and return (which was not the way investors thought about investments back then).
  • The second sea change had to do with macroeconomic policy and use of interest rate controls to not only control inflation but also feed a market fueled by declining interest rates (and turbo charged by leverage) resulting in four decades of 10.3% growth when looking at the S&P 500.
  • The third sea change he sees is where we are now. While I definitely oversimplify (you need to read the memo), the fuel of declining interest rates are unlikely to be a tailwind at our backs as compared to the prior 40 years. If you believe some of these perspectives, it seems as though many investors will need new investment strategies (e.g., rebalancing of portfolios from equity to credit instruments).

As a behavioral finance person, I see some perils of using fast, autonomous thinking and the need to try to use more slow, reflective thinking. I also see the role of inertia. Like many other people, my portfolio is heavily tilted to equities. How can people both re-think and maneuver? At the same time, how can they leverage behavioral principles and avoid biases of anchoring, such as to the past? Potentially we can use behavioral tools like whiteboard exercises to re-imagine paths to go forward. We can potentially use behavioral tools to address issues associated with forecasting, prediction, and risk. There is also the need for personalized solutions. And if there is a sea change (or if we at least need to prepare for one), there is also a need to think about how to distribute solutions to the masses. There are behavioral implications lurking. How will individual investors manage? How will the finance community and its distribution networks address such behavioral considerations? There are definitely behavioral issues to consider and address, and it will be interesting to participate in the debates and also work on block-and-tackle solutions.

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Update on Behavioral Economics Advisory

A short update separate from my academic work: I expect to have a somewhat rare opening in 2023-2026 to take on a behavioral economics advisory relationship with a new client in retirement, wealth management, or investment management. Please feel free to contact me to discuss further.

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Working Draft of Artificial Intelligence (AI) Notice for University Classes

Based on the recent ChatGPT events that have rocked the world, here’s a baseline working draft of an AI notice that I will use in some of my classes at Cornell. It presumes that AI will be used more and more over time and that as teachers we’ll have to figure out how to balance the performance and educational integrity tradeoffs.

“Students may use artificial intelligence (AI) technologies for support purposes with their writing assignments and projects (e.g., fine-tuning language) but not for writing fundamental ideas (e.g., constructing major portions of a paper). Students are required to disclose (in an appendix) use of any artificial intelligence (AI) technologies in their writing assignments and final projects, including enumerating any key steps, substantive queries used, and the purpose of key steps and queries. Students may be subject to disciplinary action if found to have violated this code of conduct.”

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