Let me explain why I believe modern economics is such a powerful tool for understanding the world. I’ll do this by discussing a great paper by Simone Cerreia-Vioglio, @UncertainLars, Fabio Maccheroni, and Massimo Marinacci, “Making Decisions Under Model Misspecification,” published in the Review of Economic Studies a few months ago. Imagine I want to drive from UC San Diego to UCLA, but I’ve never driven that route before. I need to build a “model of the world” to guide me, which we usually call a map. Maps are simplified representations of reality. They can’t include every detail if they’re to be useful. Borges, in his short story On Exactitude in Science, makes this point beautifully. (In practice, I don’t draw the map myself—I use an app—but someone still had to make it.) Because maps simplify, I can’t fully rely on them. Maybe last night’s storm knocked down a tree and closed a street, or there’s construction and the ramp off the highway in LA is shut down. This uncertainty matters. Suppose I’m driving to UCLA for an important talk at 11 a.m. If the ramp is closed, I might need 15 extra minutes. When should I set my alarm to arrive on time, while still getting enough sleep to give a good talk? The problem is that I can’t assign precise probabilities to all these contingencies. How likely is the fallen tree? Or new roadwork? Even the best traffic apps can’t capture every disruption, and some might happen after I’ve already left. In economic terms, my “model of the world” (the map) is misspecified—and no matter how hard I try, I can’t fully fix that. But sitting down and crying about misspecification doesn’t answer my basic question: when do I set the alarm? Too early, and I’m exhausted. Too late, and I’m late. Simone and his co-authors offer a way to think about this. They start from the idea that we often hold several structured models of an economic phenomenon, grounded in theory. For example, a central bank might use a standard New Keynesian model and a search-and-matching model of money. Yet, aware that each model is misspecified by design, the bank adds a protective belt of unstructured models—statistical constructs that help it gauge the consequences of misspecification. The beauty of the paper is that it provides an axiomatic foundation for this protective belt (and even generalizes it to include a Bayesian approach). It shows that if a decision-maker’s preferences meet certain conditions —reflecting both rational and behavioral features— then those preferences can be represented by an augmented utility function that formally accounts for misspecification. Crucially, we don’t assume that augmented utility function; we derive it. We start with general, plausible properties of preferences and prove that they imply such a representation. That’s real progress. Instead of writing endless critiques of expected utility or rational expectations (as many have done for decades, with little to show), we now have a formal way to reason about misspecification—precise definitions, clear boundaries of validity, and awareness of what we still don’t know. Take, for instance, a brilliant Penn graduate student on the market, Alfonso Maselli economics.sas.upenn.edu/peop… His job-market paper pushes this frontier further. He studies cases where a decision-maker not only faces model misspecification but is also unsure which model best fits the data and can’t assign probabilities to them—what we call model ambiguity. In my example, the central bank is unsure whether the New Keynesian or the search-and-matching model fits better, and it worries that both might be incorrect. If you read Simone et al. or Alfonso’s paper, you’ll see how misguided—and, frankly, cartoonish—many of the recent criticisms of economics on X have been. First: the idea that economists don’t understand math or have “physics envy.” The math in these papers is subtle and advanced—utterly different from what physicists do (neither better nor worse, just distinct). An engineer transitioning into economics would find these tools unfamiliar. Second: claims of ideological bias are unfounded. I have no idea about the political views of the authors, and I’d be surprised if anyone could infer them from the analysis—beyond vague guesses about typical academics. Third: This has almost nothing to do with what one learns as an undergraduate, or even in first-year graduate school. If your knowledge of economics stops at an intro textbook, it’s best not to pontificate on the field’s frontiers. Fourth: Is this science? Debating that word’s boundaries is pointless; every definition of “science” breaks down somewhere. The Germans solved this long ago with the idea of Wissenschaft—the systematic pursuit of knowledge, whether of nature, society, or the humanities. By that measure, modern mainstream economics is clearly a Wissenschaft: a disciplined, cumulative, and highly useful effort to understand how the world works. Simone and his co-authors have demonstrated that beyond any reasonable doubt.

⏳ Time to reorganize universities from scratch? I have been thinking a lot about what it means to be a university in 2025, once AI has arrived. My approach to learning new topics has undergone a significant change. Instead of relying only on textbooks or papers, I now integrate LLMs into my self-education. The result? My learning productivity has skyrocketed. Even more striking: I have reduced by 90% my dependence on colleagues and friends when exploring a new subject. Gone are the days of sending an email to a friend at University X asking, “What’s a good way to start learning about Y?” But this raises a deeper question: what is the added value of my lectures? Why should students take my course when LLMs already perform so well in 2025 and will likely perform even better in 2035? The problem is that most universities are not ready to face this challenge. Again and again, I see how vested interests slow down change, while layers of bureaucrats and administrators with limited vision remain in charge. Of course, there are exceptions—great leaders in some institutions—but I could fill a book with anecdotes of creeping bureaucratization that slows innovation, increases costs, and prioritizes meaningless “procedures” over real progress at a place like UPenn. History gives us a warning: think of Kodaks and Polaroids. When a fundamental change in a business model arrives, large organizations face almost insurmountable barriers to adapt. In higher education, these barriers are even greater: the absence of clear metrics for success (how do we measure the value of a new major?), the barriers to entry (accreditation, alumni networks), and the ideological biases of many stakeholders (not only political, but also a lack of responsiveness to change). If it were up to me, a top university should create its own Skunk Works to explore what higher education should look like in 2035. The original Skunk Works at Lockheed was an autonomous unit tasked with developing new technologies without the layers of managerial supervision that typically paralyze large organizations. For decades, Skunk Works produced some of the most innovative aircraft—the F-104, the U-2, the SR-71, the F-117—delivered on time and usually without cost overruns. The model was so successful that many other organizations have tried to replicate it, with varying degrees of success. Creating a Skunk Works inside a university is not without risks. See, for example, the insights of Rebecca M. Henderson and Kim B. Clark in “Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of Established Firms.” But right now, I cannot see another way for a large, inertial institution to move forward. By contrast, I am skeptical that creating entirely new institutions from scratch is the most promising path (for reasons I’ll explain another day). And if you want to know more about the original Skunk Works, I highly recommend “Skunk Works: A Personal Memoir of My Years at Lockheed” by Ben Rich and Leo Janos. A delightful book—especially if, like me, you love planes. ✈️