"Les fameux 75 % qu’avait inventĂ©s M. Hollande, c’était 75 % de papier parce qu’ils avaient imaginĂ© un dispositif qui serait censurĂ© par le Conseil constitutionnel". @montebourg đŸ“șExtrait de #cdanslair spĂ©cial "Dette : un scandale français", le 9 novembre Ă  21h05 sur France 5

Bienvenue Ă  l’Orchestre national d’IsraĂ«l ce jeudi Ă  la @philharmonie. Rien ne justifie un appel au boycott de ce moment de culture, de partage et de communion. La libertĂ© de crĂ©ation et de programmation est une valeur de notre #RĂ©publique. Aucun prĂ©texte Ă  l’antisĂ©mitisme !

Sudan Militia, Armed With Drones, Hunts Down Black Population of Darfur. That headline says it all. Its pretty shocking that more attention isnt being paid to people actually being hunted down in broad daylight. wsj.com/world/africa/sudan-m


I crossed an interesting threshold yesterday, which I think many other mathematicians have been crossing recently as well. In the middle of trying to prove a result, I identified a statement that looked true and that would, if true, be useful to me. 1/3

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.

MĂȘme si les choix sont politiques, ils doivent ĂȘtre informĂ©s par des donnĂ©es et des arbitrages dont la prĂ©sentation est, par nature, technique. Or, dĂšs lors qu’une proposition s’appuie sur une description politique des donnĂ©es, cela devient problĂ©matique. La dĂ©cision politique du citoyen doit ĂȘtre Ă©clairĂ©e. Or, lorsque des Ă©conomistes prĂ©sentent des graphiques de taux de prĂ©lĂšvements obligatoires dont la mĂ©thodologie implique que recevoir une aide sociale ou un crĂ©dit d’impĂŽt *augmente* le taux d’imposition des classes populaires et donne une apparence rĂ©gressive au systĂšme fiscal, il y a de toute Ă©vidence un souci. On prive alors le citoyen de sa capacitĂ© Ă  faire un choix Ă©clairĂ©, en le traitant comme un abruti. Que le service public de l’audiovisuel dĂ©ploie de telles ressources pour participer Ă  cette entreprise constitue Ă©galement un problĂšme.

Sur la taxe Zucman, je ne comprends plus rien. On est parti d’un trou dans la raquette pour les gens qui ont plus de 100 millions d’euros de patrimoine. MĂȘme les chercheurs les plus idĂ©ologues s’accordent pour reconnaĂźtre qu’hormis ce trou prĂ©cis dans la raquette, notre systĂšme est trĂšs progressif pour les autres ! Je rĂ©pĂšte, exceptĂ© ce problĂšme trĂšs prĂ©cis et circonscrit, il n’y a AUCUN problĂšme de justice fiscale. Les riches payent Ă©normĂ©ment. Avec la taxe Zucman « Light », c’est du dĂ©lire, on crĂ©e en fait un super ISF (3% !) sur les patrimoines 10 fois plus modestes, dont le rendement peut tout Ă  fait ĂȘtre Ă  peu prĂšs nul. C’est une expropriation en quelques annĂ©es. Au secours.