Professor of Practice of Behavioral Economics
Cornell University highlights a working paper that a number of collaborators and I have been working on to better understand how AI chatbots might complement decsion making or potentially make things go awry, such as reinforcing blindspots that we generally have as humans.
The current working paper can be found here on SSRN.
This post is an answer to a question I was posed on Quora, “How can you avoid giving too much away when selling consulting?” I wanted to repost the answer to this question as I know that there many younger professionals and former students of mine out there that either want to become more involved with business development or try to strike out with their own consulting pursuits.
Here are some thoughts on how I’ve tried to keep sales processes on track:
To recap, make sure that both parties understand the problem statement. Both parties should understand the approach and should appreciate that solving the problem will take both time and work. Offer some value to the client in advance of sale; this does not necessarily have to be much, but you need to establish credibility and trust. Finally, set some expectations on the cadence and timeline to get to a proposal or no-go decision.
A few weeks ago, we covered ethics in my behavioral economics class at Cornell Tech. The case example below strikes me as tipflation + pure deception, which involve ethical issues stacked on top one another and put the end consumer in a terrible place (e.g., stating tip as 25% but providing an actual dollar tip amount that is even larger, say 38%, under the guise that it is actually 25%). First of all, the consumer has to determine what is fair and deserving to leave as a tip, and that is complicated in of itself because they often can’t judge how tips are split among the restaurant operations staff and other. Secondly, both tipflation and deception nudges likely prey on fast thinking psychological processes and may disproportionately affect those with lower numeracy (and possibly socioeconomic status). The nudge to “check your math” is likely moving in the right direction, but it takes reflective, slow thinking and a certain level of math skills and cognitive stamina (which could be additionally challenging if someone is cognitively depleted after a meal).
To recap some items we discussed in class, these include:
https://www.foodandwine.com/tipflation-restaurant-tipping-scams-8642517
Podcast timeline by YoutubeDigest:
As a follow-on post to summer 2023 exploratory work that is happening with the Behavioral Economics Research and Education (BERE) Lab, we’ve started to compile early results. Here are some test result summaries of different AI platforms based on the conjunction fallacy test (Linda problem). Note that platforms vary based on degree of live access to the internet and incorporation of slower System 2 thinking influences (although these characteristics are also confounded with platform implementation). Here we test ChatGPT 3.5, Bing Chat AI (based on GPT 4), and Google Bard.
Interesting questions to reflect on:
– How do AI platforms differ?
– Which gets things right?
– Which do you trust?
– To what extent will AI adoption get impacted by use case, accuracy, and trust?
For the past two summers, I have personally volunteered to spend time helping a limited number of students pursue interests in behavioral economics and build their resume of experiences. For this summer, I will expand my efforts somewhat, although I hope to eventually find a more sustainable and scalable model in terms of funding, operations, and potential synergies with other organizations.
Here’s the natural extension of what I’ll be doing for the summer of 2023.
The Behavioral Economics Research and Education (BERE) Lab by Stephen Shu is an effort geared toward helping college students and young professionals with either the empirical or applied practice of behavioral economics. BERE efforts are in support of open science and the advancement of education. Where possible, students or young graduates may be supported by grants, and BERE welcomes opportunities to help students and young graduates obtain grants or corporate sponsorship.
Students and young graduates may pursue exploratory replication studies, expansion research studies, and corporate-focused research (for use in educational settings). Where possible, students are encouraged to develop empirical or professional skills (e.g., R or Stata statistical software, Python programming, communications, writing).
The research theme for 2023 includes exploratory work around the intersection of generative artificial intelligence (AI) and behavioral economics, such as similarities and differences between AI and human decision making across different platforms.
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.
Below is lightly-edited, partial summary of the podcast as generated by the AI tool, YoutubeDigest which uses ChatGPT technologies.
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.