ESSEC Business School (French: École Supérieure des Sciences Economiques et Commerciales) is one of the most prestigious and selective grandes écoles based in Paris, with campuses in Singapore and Morocco. ESSEC is known as one of the Trois Parisiennes (three Parisians), along with ESCP Business School and HEC Paris. It has obtained the triple crown accreditation of AACSB, EQUIS and AMBA.The school is currently headed by Vincenzo Esposito-Vinzi following the appointment of Jean-Michel Blanquer as French Minister of Education in the Philippe Government of President Emmanuel Macron.
In this report, we present the BOBILib, a collection of more than 2600 instances of mixed integer bilevel linear optimization problems (MIBLPs). The goal of this library is to provide a large and well-curated set of test instances freely available for the research community so that new and existing algorithms in bilevel optimization can be tested and compared in a standardized way. The library is sub-divided into instances of different types and also contains different benchmark instance sets. Moreover, we present a new data format for MIBLPs that is less error-prone compared to an older format that will now be deprecated. We provide numerical results for all instances of the library using available bilevel solvers. Based on these numerical results, we select benchmark instance sets, which provide a meaningful basis for experimental comparisons of solution methods in a moderate time. The instances, together with solution files, can be downloaded at https://bobilib.org .
We study community detection in stochastic block models under pure node-level differential privacy, a stringent notion that protects the participation of an individual together with all of their incident edges. This setting is substantially more challenging than edge-private community detection, since modifying a single node can affect linearly many observations. On the algorithmic side, we analyze a node-private estimator based on the exponential mechanism combined with an extension lemma, and show that exact recovery remains achievable. In the standard sparse regime with logarithmic average degree and a fixed number of communities, our results imply that a logarithmic privacy budget suffices to obtain nontrivial recovery guarantees. On the lower bound side, we show that this logarithmic scaling is in fact unavoidable: any pure node-private method must fail to achieve polynomially small exact-recovery error, or polynomially small expected mismatch, unless the privacy budget is at least of this order. Moreover, in the regime of super-logarithmic privacy budgets, our upper and lower bounds yield a matching two-term characterization of the minimax risk, with one term governed by the non-private statistical signal and the other by the privacy budget; these match up to universal constants in the exponents. Taken together, our results identify an inherent logarithmic privacy cost in node-private community detection, absent under edge differential privacy, and provide a precise rate-level characterization of the tradeoff between node privacy and SBM recovery.
This paper examines how changes in firm-level total factor productivity (TFP) depend on the quality of state governance. We find robust evidence that an improvement in the quality of state governance by one standard deviation raises the average firm’s TFP by between 9 and 19 percent. We also show that this effect works through improved productive efficiency rather than technological progress. Further decompositions reveal that the key relevant institutions are government effectiveness, rule of law, and democratic accountability. Moreover, the contribution of state governance to TFP dominates that of corporate governance.
Forecast reconciliation has become key to improving the accuracy and coherence of forecasts for linearly constrained multiple time series, such as hierarchical and grouped series. Yet, comprehensive software that jointly covers cross-sectional, temporal, and cross-temporal reconciliation has so far been lacking. The R packages FoReco and FoRecoML address this gap by offering a comprehensive and unified framework. The packages respectively implement classical and regression-based linear reconciliation approaches, and non-linear approaches based on machine learning for cross-sectional, temporal and cross-temporal frameworks. Designed for accessibility and flexibility, these packages provide sensible default options that allow new users to apply reconciliation methods with minimal effort, while still giving expert users full control to explore state-of-the-art extensions through customized settings. With this dual focus, FoReco and FoRecoML are versatile tools for practitioners and researchers working on forecast reconciliation.
Agentic AI systems - systems that can pursue goals through multi-step planning and tool-mediated action with limited direct supervision - are moving from experimental prototypes to enterprise deployments. This transition introduces tensions in implementation, scaling, and governance: organizations seek scalable autonomy for knowledge and coordination work, yet must preserve accountability, safety, cost control, and responsibility as systems initiate actions, access enterprise data, and evolve through iterative updates. Building on an in-depth qualitative case of a large IT services company's 2025 development and staged rollout of an agentic system integrated with enterprise tools; we show that governance is implemented through concrete architectural and working arrangements that determine what the system is allowed to do, which tools and data it can use, how memory is handled, and how performance improvements are introduced over time. We then distill seven lessons that explain how to build effective governance into agentic AI during operationalization and scaling.
