When Joining More Queues Is Not Always Better When customers can join multiple wait lists, does more choice always improve a service system? In “Multilisting for Horizontally Differentiated Services: Implications for Throughput and Social Welfare,” Chen, Ding, and Yang study customers who can join several provider queues, choose only their preferred provider, or decline service. The authors compare this flexible multilisting design with single-listing and pooling. Multilisting attracts more customers and consistently achieves at least as much throughput as either alternative. It also improves social welfare relative to pooling. Yet compared with single-listing, the extra participation can worsen congestion and reduce welfare, even as access expands. The analysis further shows that the welfare-maximizing allocation may treat otherwise identical providers asymmetrically, and that nonnegative, asymmetric prices can steer customers toward socially efficient choices. The findings offer guidance for designing healthcare wait lists, public housing systems, vaccination programs, and other capacity-constrained services where access, matching, and congestion must be balanced.
Testing Smarter, Not More: An Adaptive Approach In the paper titled “A Novel Adaptive Testing Scheme for Multidisease Testing,” Douglas Bish, Ebru Bish, Merve Bodur, and Berk Görgülü develop an adaptive, cost-efficient testing scheme for lab-based multidisease testing, essential for diagnosis and screening, particularly for disease groups that share symptoms but require disease-specific treatments (e.g., respiratory, gastrointestinal, or sexually transmitted diseases). Their proposed adaptive strategy integrates multiplex assays and pooled testing in a novel way, utilizing modern automated testing technologies to build a customized follow-up test for each person, covering only the diseases flagged in that person’s relevant pooled tests. Optimizing such adaptive designs is mathematically demanding, but in a case study of 18 respiratory diseases, based on data from the Centers for Disease Control and Prevention, their adaptive designs yield substantial cost savings (51%–61%) relative to current practice under forecast error and highly seasonal prevalences. By lowering the cost per subject, adaptive designs can expand testing capacity and support earlier detection under resource constraints.
When Screening and Vaccine Incentives Work Better Together When vaccine mandates are not feasible, institutions may rely on vaccination rewards or routine screening of the unvaccinated. The study shows these tools can be more powerful when designed together. Exempting vaccinated individuals turns screening into a nonmonetary incentive while retaining its role in detecting infections. The authors develop a behaviorally informed framework that accounts for vaccine hesitancy, community preferences, limited budgets, and testing capacity. Rather than committing to a rigid plan, the framework learns from surveys and vaccination data and adapts screening as community behavior unfolds. The analysis reveals that incentives and screening are not always substitutes: under some conditions, each strengthens the value of the other. In a realistic college-campus case study using a detailed epidemic model, the adaptive strategy reduced infections and hospitalizations while adhering more closely to budget than incentive-only or screening-only approaches. The findings offer practical guidance for designing flexible mitigation plans for future outbreaks in universities and other close communities.
Nonsmooth Learning Algorithms Behave Differently from Smooth Ones Many learning algorithms, including Q-learning, update their estimates through noisy recursive rules. When these updates use a constant step size and involve nonsmooth operators, their long-run behavior can be difficult to characterize: the iterates do not converge to a single point but instead settle into a stationary distribution, and classical tools for smooth dynamics may fail. In “Prelimit Coupling and Steady-State Convergence of Constant-Step-Size Nonsmooth Contractive SA,” Yixuan Zhang, Dongyan (Lucy) Huo, Yudong Chen, and Qiaomin Xie develop a prelimit coupling framework for analyzing such nonsmooth stochastic approximation procedures. They establish Wasserstein convergence to a unique stationary distribution, characterize the limiting steady-state distribution as the step size vanishes, and show that nonsmoothness can create a bias of order of the square root of the step size, unlike the bias linear in the step size that is typical of smooth dynamics. The results also justify Richardson–Romberg extrapolation as a tool for bias reduction, with applications to Q-learning.
How should policymakers mitigate investment risk in renewable electricity markets without undermining the efficiency of spot markets? Risk mitigation instruments can foster investment in financially incomplete markets but may distort operating incentives, contributing, for example, to increasingly frequent negative electricity prices. In Mitigating Market Incompleteness with Minor Market Distortions: The Case of Negative Spot Prices for Electricity, Ibrahim Abada and Andreas Ehrenmann develop a bi-level programming framework that explicitly incorporates these distortions when addressing market incompleteness. At the lower level, risk-averse agents make investment and operating decisions in an incomplete electricity market. At the upper level, a central planner optimally designs contracts for difference and price markups to maximize social welfare. The paper establishes an existence result, develops an algorithm for finding a solution, and applies the framework to a detailed numerical simulation inspired by the French power system. The results show that carefully designed risk mitigation instruments can enhance welfare while substantially limiting market distortions and the prevalence of negative prices.
