Sful parties, I was sure that our most expensive venue yet, premium drinks, and an impressive DJ lineup would make our next event our best. Instead, only around 10 to 15 people came from a list of roughly 400. At first, I was confused. We had improved everything that seemed to make an event better, yet people were not showing up. Then I realized that we had built the party around what looked premium to us, not what our customers valued. Earlier events had succeeded because they were intimate and personal alternatives to overcrowded parties where people felt lost in the noise. In trying to upgrade the experience, we had abandoned the reason people came. That failure left me with a question more interesting than why the party flopped: how could the evidence we trusted lead us so far from what people wanted? Our WhatsApp list looked like demand, but we had never asked what it actually measured. I began wondering why organizations trust certain numbers, overlook inconvenient signals, and become confident in assumptions they have never tested. I would design Beyond the Numbers: The Economics of Organizational Decision-Making, a Spring Term course that uses economics and quantitative analysis as its backbone, with psychology and organizational behavior helping students interpret what numbers miss. Its central question would be: How can organizations make rigorous decisions while accounting for the human realities their metrics cannot fully capture? The course would focus on product, financial, and strategic choices: what organizations offer, how they infer what customers value, and how they decide where to invest limited resources. Students would learn not to reject data in favor of intuition, but to ask better questions of it. Our main tool would be a Decision Audit. For each case, teams would identify the decision’s stated objective, the metrics used to judge it, and the assumptions connecting those metrics to the decision. Then they would test those assumptions against behavioral evidence and consider who benefited, who bore the costs, and what a stronger alternative might have looked like. A high sign-up count, for example, might measure casual interest rather than commitment; a profitable investment might still shift costs onto employees, customers, or a community. The audit would make the course’s argument practical: data is powerful, but only when we understand what it represents and what it leaves out. The itinerary would be ambitious but structured around three different ecosystems pursuing enterprise success under different conditions. We would begin with two days on campus learning the Decision Audit and examining public case materials. We would then spend five days in Richmond, six in Silicon Valley, and eight in São Paulo, with three travel days between sites. Four days on campus would frame the course, synthesize findings, and allow teams to present their final audits. Jackson Ward would be the course’s starting point because it complicates a narrow definition of business success. Students would study its history as a center of Black-owned banks, insurance companies, businesses, and cultural institutions—not as a simple success story, but as an example of people building economic independence under exclusion. They would also examine how public decisions reshaped that achievement: highway construction and urban-renewal projects divided the neighborhood, displaced residents and business owners, and disrupted its streets. Jackson Ward would show that the consequences of economic and policy decisions can last far beyond the original balance sheet. In Silicon Valley, students would study how technology firms use experimentation, customer data, and venture capital to develop and scale products. In São Paulo’s Faria Lima district, they would examine investment and strategic decisions within one of Latin America’s major business and financial centers. Rather than treating these locations as equivalent, the course would compare how resources, institutions, and history influence the choices organizations can make—and the ways they define success. Learning would come through site-based observation, conversations with local practitioners where available, and analysis of public records and business cases. Students would keep field notes on the evidence they encounter and the assumptions it challenges. Their final project would be a Decision Audit comparing one decision from each setting. I would pursue this course because I want to become a more careful decision-maker in my own ventures. I want to know how to identify the right metric before making a major choice, combine quantitative analysis with behavioral insight, and recognize when financial success is hiding costs for customers, employees, or communities. My failed event taught me that a good-looking decision can still be the wrong one. This course would help me understand why—and how to make better ones.