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Reading the Future of Oil: A Noncausal Approach to Supply News Shocks
No full textThis paper proposes a new strategy to identify oil supply news shocks by combining a Bayesian noncausal structural VAR with a Max-Share approach. The framework jointly resolves the problems of non-fundamentalness and recoverability that undermine standard (proxy) SVAR methods. Exploiting non-Gaussianity in a multivariate Student−t specification, we recover structural shocks from a two sided moving average representation and isolate the expectation driven component of oil supply innovations without external instruments. Applied to global oil market data, the model supports a non fundamental representation and detects anticipatory price and inventory movements consistent with rational expectations storage behavior. The identified shocks explain a substantial fraction of real oil price fluctuations, notably in the late 1970s–1980s and during the 2014–15 collapse, while the COVID-19 episode is predominantly demand driven. Decomposing global supply shows that these shocks are primarily OPEC-driven and generate stagflationary responses in output and inflation, underscoring the central role of expectations in oil market dynamics
Three-dimensional Brownian loop soup clusters
No full text38 pages, 2 figuresWe study Brownian loop soup clusters in R^3 for an arbitrary intensity α >0. We show the existence of a phase transition for the presence of unbounded clusters and study its basic properties. In particular, we show that, when α is sufficiently large, almost surely all the loops are connected into a single cluster. Such a phenomenon is not observed in discrete percolation-type models. In addition, we prove the existence of a one-arm exponent and compare the clusters with the finite-range system obtained by imposing lower and upper bounds on the diameter of the loops. Finally, we provide a toolbox concerning the Brownian loop measure in R^d , d ≥ 3. In particular, we derive decomposition formulas by rerooting the loops in specific ways and show that the loop measure is conformally invariant, generalising results of [Lup18] in dimension 1 and [LW04] in dimension 2
Large-time optimal observation domain for linear parabolic systems
No full textInternational audienceGiven a well-posed linear evolution system settled on a domain of , an observation subset and a time horizon , the observability constant is defined as the largest possible nonnegative constant such that the observability inequality holds for the pair .In this article we investigate the large-time behavior of the observation domain that maximizes the observability constant over all possible measurable subsets of a given Lebesgue measure. We prove that it converges exponentially, as the time horizon goes to infinity, to a limit set that we characterize.The mathematical technique is new and relies on a quantitative version of the bathtub principle
Vers une évaluation hyper-moderne des politiques publiques
No full textNational audienceDepuis son origine en contexte public, l’évaluation des politiques a connu différentes formes. D’abord, une évaluation qualifiée d’« administrative », inscrite dans une logique hiérarchique et centrée sur la conformité procédurale. Ensuite, une évaluation « managériale » portée par les principes du New Public Management (NPM), valorisant la performance, la responsabilisation individuelleet l’usage d’indicateurs quantitatifs. Enfin, une évaluation plus « collaborative et ouverte », intégrant la réflexivité, la participation et la prise en compte du contexte social et territorial de l’action publique, en intégrant des approches post-NPM, plus récentes. Pourtant, malgré ces évolutions, l’évaluation des politiques publiques est actuellement critiquée, que cela soit sur le plan conceptuel ou sur celui plus pratique. Une évaluation renouvelée semble alors nécessaire !
Regime-aware time weighting for physics-informed neural networks
No full textInternational audienceWe introduce a novel method to handle the time dimension when Physics-Informed Neural Networks (PINN) are used to solve time-dependent differential equations; our proposal focuses on how time sampling and weighting strategies affect solution quality. While previous methods proposed heuristic time-weighting schemes, our approach is grounded in theoretical insights derived from the Lyapunov exponents, which quantify the sensitivity of solutions to perturbations over time. This principled methodology automatically adjusts weights based on the stability regime of the system — whether chaotic, periodic, or stable. Numerical experiments on challenging benchmarks, including the chaotic Lorenz system and the Burgers’ equation, demonstrate the effectiveness and robustness of the proposed method. Compared to existing techniques, our approach offers improved convergence and accuracy without requiring additional hyperparameter tuning. The findings underline the importance of incorporating causality and dynamical system behavior into PINN training strategies, providing a robust framework for solving time-dependent problems with enhanced reliability
Estimation in linear high dimensional Hawkes processes: a Bayesian approach
No full textInternational audienceIn this paper we study the frequentist properties of Bayesian approaches in linear high dimensional Hawkes processes in a sparse regime where the number of interaction functions acting on each component of the Hawkes process is much smaller than the dimension. We consider two types of loss function: the empirical L 1 distance between the intensity functions of the process and the L 1 norm on the parameters (background rates and interaction functions). Our results are the first results to control the L 1 norm on the parameters under such a framework. They are also the first results to study Bayesian procedures in high dimensional Hawkes processes
Cooking for Transitioning : A Study of Solidarity Restaurants Scaling Process
No full textInternational audienc
Le pouvoir discrétionnaire de l'administration à l'épreuve de l'intégration des IA
No full textRepenser le management à l'ère de l'IA.Face à l’avènement de l’intelligence artificielle générative, le monde du management est à un tournant. Doit-il être repensé ?Comment cette technologie redéfinit-elle les règles du jeu ?Quels sont les enjeux pour l’enseignement, l’entreprise et les services publics ?Cet ouvrage novateur propose une analyse multidimensionnelle et approfondie de l’impact de l’IA générative sur le management.Trois angles sont abordés :• L’enseignement du management : la transformation des compétences managériales, les modes d’apprentissage et les rôles futurs des formateurs et des étudiants.• Les performances des organisations : l’influence de l’IA sur la stratégie d’entreprise, le marketing, la logistique, l’entrepreneuriat, les gains d’efficacité et d’innovation.• Le management public : les défis éthiques, organisationnels et de responsabilité que l’IA soulève pour les institutions de l’État.Fruit des travaux de recherche d’enseignants-chercheurs du Laboratoire de recherche en sciences de gestion de l’Université Paris Panthéon-Assas (LARGEPA), ce livre offre une perspective éclairée pour tous les professionnels, chercheurs et étudiants désireux de comprendre les implications profondes de l’IA générative sur le management d’aujourd’hui et de demain
A synthetic approach to comparison principles for variational problems, with applications to optimal transport
No full textWe develop a synthetic, variational framework for deriving comparison principles in infinite-dimensional Banach spaces. Unlike traditional approaches that rely on the regularity of minimizers and Euler--Lagrange equations, our method exploits the order-theoretic structure of the energy. Central to our analysis is the notion of submodularity and its convex dual, substitutability, which we extend here to the infinite-dimensional setting. We prove a duality theorem establishing that a convex functional is submodular if and only if its conjugate is substitutable. We apply these results to problems in optimal transport, and derive comparison principles for Kantorovich potentials in standard, entropic, and unbalanced settings without requiring regularity assumptions on the cost or domain. Finally, we prove that general transport costs are substitutable, yielding comparison principles for JKO schemes driven by internal energies
