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In a World of AI, What Value Do We Add as Humans?

I’ve spent the summer with a new lift preparing to teach the core Behavioral Economics and Managerial Decisions course at Cornell (AEM 6140). Each year it gets more challenging to think about how we view AI, education, and the future of both work and life. Crafting a policy on AI has also been challenging. Admittedly, this may be the last year I can keep the policy that I’ve had for the past two years.

My general policy has been around embracing AI, but avoiding using AI for core ideas. I believe that some key learning processes and skills development are lost by delegating to AI in the wrong way.

The larger question I pose though is not around AI policy. Rather, I see the key question as what value do we expect to add as humans? This is the type of education, environment, and support structure that I aspire to build toward.

With that as backdrop, I wrote down five things that I will share with students today in the classroom. We add value by:

  • Orchestrating
  • Thinking holistically and linking ideas
  • Using system-level thinking
  • Persuading
  • Understanding and re-defining what it means to be human

“With AI, not of AI” (term coined with help of ChatGPT to solidify my thinking)

After I drafted my thoughts, I did query Perplexity.ai to try and gather thoughts expressed by others. I see some similarities and differences.

Upon further reflection, I viewed my concept of human value-add as being more organic and aspirational (i.e., something that we’ll need to practice and improve over time as opposed to achieving a milestone).

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The Behavioral Way Summit II Madrid

I’m excited to be part of The Behavioral Way Summit II Madrid!

The second edition of the most important Behavioral Science event in Spain and Latin America will take place on 14th and 15th November. It promises to be an unforgettable experience!

Given my heavy involvement in industry and the application of behavioral economics, I’ll focus on some key things companies should consider as they either start or look to implement the next phase of behavioral initiatives. What have we learned in the past 15 years in the commercial space about taking science to the field? What are some business mindsets that worked when the field was younger but are no longer appropriate? What are some mindsets from business management that need to be added to make applied behavioral science even more effective?

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My Future Self Podcast on Democratizing Nudges


Podcast timeline by YoutubeDigest:

  • 00:15   Exploring the democratization of nudges to enhance organizational awareness and accessibility of behavioral science, shedding light on various models and ethical considerations.
  • 05:02   Initiating a behavioral finance institute and delving into the intersection of psychology and economics, highlighting the importance of understanding behavioral economics in navigating financial decision-making.
  • 09:26   Analyzing the multifaceted aspects of retirement planning, including decomposing the problem, aligning goals, and acknowledging uncertain outcomes, while tracing the emergence of behavioral science from foundational work to its current application in various sectors.
  • 14:04   The expansion of behavioral decision-making groups in academic institutions has led to increased labor in the market and the emergence of boutique consultancies, advocating for the incorporation of behavioral economics principles across various business sectors, suggesting a gradual implementation approach starting with anchor areas to foster organizational learning and maximize effectiveness.
  • 18:41   Addressing retirement preparation as a marathon with potential hazards, emphasizing the importance of simplifying choices, enhancing financial literacy, and reframing savings concepts, while advocating for pension system adaptability to accommodate evolving work dynamics and longevity.
  • 23:27   Advocating for a balanced approach in encouraging smarter savings behaviors, addressing the diverse perspectives on longevity and health, advocating for increased research and collaboration, and fostering leadership that prioritizes sustainability and inclusivity in pension systems.
  • 27:56   AI, like ChatGPT, presents opportunities for automating tasks but requires human oversight to mitigate biases, particularly in decision-making processes where AI may inherit similar biases to humans, highlighting the importance of careful framing and consideration of alternative explanations.
  • 32:39   AI platforms exhibit strengths and weaknesses, offering insights into when to integrate them into decision-making processes while also emphasizing the importance of democratizing access, raising awareness, and simplifying usability to ensure broader adoption and equitable benefits for all.
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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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