Faster CoVaR Estimation via a Decoupled Approach CoVaR is a widely used measure of systemic financial risk, capturing the risk of a portfolio conditional on another portfolio being under distress. In the paper “Efficient Nested Estimation of CoVaR: A Decoupled Approach,” Nifei Lin, Yingda Song, and L. Jeff Hong address two central challenges in CoVaR estimation: The conditioning event has zero probability, and portfolio losses often require costly simulation-based repricing. Their key insight is that, in nested simulation, the computational cost of outer simulations is negligible compared with that of inner simulations. The proposed approach decouples the nested estimation task by first learning approximations of portfolio loss functions using smoothing techniques and then evaluating the learned functions over a large outer-level sample to handle the zero-probability event. The framework is plug-and-play: Different smoothing techniques can be incorporated, and when their [Formula: see text]approximation rates are available, the paper’s theory directly yields the convergence rate of the resulting CoVaR estimator. The authors also establish smoothness of portfolio loss functions, a key foundation for efficient learning. Under suitable conditions, the estimator achieves the favorable near-square-root convergence rate [Formula: see text], where [Formula: see text]is the inner-level simulation budget.
Convergence of Optimistic Learning in Games and the Role of Forgetfulness Online learning algorithms solve games by repeatedly updating players’ strategies. A natural hope is that the latest strategy improves at a predictable rate. This paper shows that this intuition can fail for optimistic follow-the-regularized-leader methods, including the widely used optimistic multiplicative weights algorithm. In two-player zero-sum games, the authors separate three notions of nonergodic performance: last-iterate, random-iterate, and best-iterate convergence. They prove that no instance-independent last-iterate rate exists for the broad algorithmic family and establish strong lower bounds for random iterates. Yet, a useful positive result remains; in 2 × 2 games, optimistic multiplicative weights achieve a uniform best-iterate rate. The analysis traces the slowdown to a lack of “forgetfulness”; accumulated past losses can keep the dynamics moving away from equilibrium long after they reach its neighborhood. The results suggest that practitioners should distinguish carefully between the latest strategy, a randomly selected strategy, and the best observed strategy when evaluating learning dynamics.
Online Rack Placement in Large-Scale Data Centers: Online Sampling Optimization and Deployment Data centers have grown into major components of global supply chains. This paper develops, deploys, and assesses an optimization algorithm to improve how large-scale data centers place incoming server racks dynamically while balancing space, power, cooling, and reliability constraints. Poor placement decisions can leave valuable resources stranded, leading to high costs and reduced operating resilience. This paper formulates a large-scale online discrete optimization model and develops a new online sampling optimization (OSO) algorithm that anticipates future demand by repeatedly simulating future arrivals and reoptimizing decisions over time. Theoretical results provide performance guarantees, and computational results show its benefits against state-of-the-art reoptimization methods. The system was implemented as a decision-support tool and deployed across Microsoft’s global fleet of data centers. Using postdeployment data, the paper shows that adoption of the tool reduced power stranding by one to three percentage points. At Microsoft’s scale, these improvements translate into substantial financial savings and meaningful reductions in greenhouse gas emissions, demonstrating the real-world impact of optimization in cloud infrastructure management.
Many systems traditionally use a basic first-come, first-served scheduling policy. It is simple to implement and perceived as fair to arriving jobs. In recent years, Nudge policies were introduced, showing that large delays occur less frequently by allowing small changes in the order in which jobs are served—often improving all delay quantiles. In “Tail Optimality and Performance Analysis of the Nudge*(M) Scheduling Algorithm,” Charlet and Van Houdt introduce the Nudge*(M) policy and show that this policy is optimal among a broad class of Nudge-like policies. A key feature of these policies is that they do not exploit arrival time information, only the arrival order and job labels. The authors provide an explicit formula for the asymptotic tail improvement ratio and show how to numerically find the waiting and response time distributions.
No-Regret Bayesian Recommendation to Homogeneous Users We introduce and study the online Bayesian recommendation problem for a recommender system platform. The platform has the privilege to privately observe a utility-relevant state of a product at each round and uses this information to make online recommendations to a stream of myopic users. This paradigm is common in a wide range of scenarios in the current internet economy. The platform commits to an online recommendation policy that utilizes its information advantage on the product state to persuade self-interested users to follow the recommendation. Because the platform does not know users’ preferences or beliefs in advance, we study the platform’s online learning problem of designing an adaptive recommendation policy to persuade users while gradually learning users’ preferences and beliefs en route. Specifically, we aim to design online learning policies with no Stackelberg regret for the platform, that is, against the optimal benchmark policy in hindsight under the assumption that users will correspondingly adapt their responses to the benchmark policy. Our first result is an online policy that achieves double logarithmic regret dependence on the number of rounds. We also present an information-theoretic lower bound showing that no adaptive online policy can achieve regret with better dependency on the number of rounds. Finally, by formulating the platform’s problem as optimizing a linear program with membership oracle access, we present our second online recommendation policy that achieves regret with polynomial dependence on the number of states but logarithmic dependence on the number of rounds.
Simplicity Meets Optimality in Online Resource Allocation Online platforms frequently manage sequential allocation decisions where outcomes are uncertain, such as selecting which products to display to a user. While it is often assumed that complex “adaptive” algorithms—those reacting to real-time feedback like user choices—are necessary for optimal performance, Rajan Udwani’s paper, “Optimality of Nonadaptive Algorithms in Online Submodular Welfare Maximization with Stochastic Outcomes,” challenges this assumption. The paper introduces a general framework and a “lifting” technique that translates established results from deterministic settings to those involving stochastic outcomes. Using this framework, Udwani demonstrates that nonadaptive Greedy-like algorithms, which remain oblivious to specific outcome realizations, achieve the best possible competitive ratios across diverse settings and arrival models. The findings suggest that for a broad class of objectives, including submodular functions, adaptivity offers no theoretical advantage. This allows for the use of simpler, more robust algorithms in environments where outcomes may be delayed or difficult to monitor.
Mutually Fair Redistricting Even When Parties Disagree Congressional redistricting is the process of partitioning a state into districts, each of which elects a representative to Congress. Several recent high-profile redistricting efforts aim to increase the political power of a party. This raises the question of whether “fair” redistricting plans exist. In “You Can Have Your Cake and Redistrict It Too,” Benadè, Procaccia, and Tucker-Foltz propose a new theoretical model for redistricting inspired by classical cake-cutting models. In this model, it shown that is always possible to find redistricting plans that satisfy a particular notion of fairness, called the geometric target, simultaneously for both parties, even when the parties disagree about voter preferences. On real-world data, they find that this fairness constraint can be satisfied in all instances evaluated; moreover, requiring fairness comes at little cost in terms of traditional redistricting objectives. This suggests it is possible and practical to guarantee mutual fairness even in a climate of extreme partisanship.
Every representative democracy must specify a mechanism under which voters choose their representatives. The most common mechanism in the United States-winnertake-all single-member districts-both enables substantial partisan gerrymandering and constrains fair redistricting, preventing proportional representation in legislatures. We study the design of multimember districts (MMDs), in which each district elects multiple representatives, potentially through a non-winner-take-all voting rule. We carry out largescale empirical analyses for the U.S. House of Representatives under MMDs with different social choice functions and algorithmically generated maps optimized for either partisan benefit or proportionality. Doing so requires efficiently incorporating predicted partisan outcomes-under various multiwinner social choice functions-into an algorithm that optimizes over an ensemble of maps. We find that, with three-member districts using single transferable vote, fairness-minded independent commissions would be able to achieve proportional outcomes in every state up to rounding, and advantage-seeking partisans would have their power to gerrymander significantly curtailed. Simultaneously, such districts would preserve geographic cohesion. Through simulation, we find that the insights are robust to cross-party voting. In the process, we advance a rich research agenda at the intersection of social choice and computational gerrymandering.
Smarter Community Health Visits Improve Diabetes Outcomes Diabetes takes a severe toll in developing countries, where high blood glucose contributes more than half of premature deaths. Community health workers (CHWs) offer a culturally tailored lifeline, but deploying them efficiently requires balancing the screening of new patients against managing those already in treatment. In this issue, researchers introduce an innovative optimization framework that personalizes CHW visit plans to maximize community-wide glycemic control. Uniquely, the model explicitly factors in patients’ motivational states—predicting their likelihood of enrolling in or dropping out of care—to guide intervention strategies and reduce attrition. Applied to operational data from urban slums in India, the approach delivers remarkable results; optimized visit plans reduced fasting blood glucose levels by up to 25% compared with the best baseline methods using identical capacity. The model also proved to be robust under imperfect information, offering a powerful, practical tool for global health resource allocation.
