Knowledge that Transforms

Problem Definition: This paper addresses the critical challenge of delivering humanitarian aid to points of distribution (PoDs) after a disaster under uncertainty, particularly when some PoDs become isolated due to road/bridge damages. Motivated by the Eta and Iota hurricanes in Honduras, we introduce a new Last-mile Humanitarian Logistics Planning problem that jointly determines: the location and capacity of staging areas (SAs), the fleet-sizes of heterogeneous mobile units-ground and aerial, the allocation of mobile units to SAs, and routing decisions ensuring all PoDs are served within a target delivery time. Mobile units can perform multiple trips within this window, enabling efficient fleet-size optimization. Methodology/Results: We formulate this problem as a Parallel Drone–Vehicle Routing Problem with Location and Fleet-Size Decisions, modeled as a route-based mixed-integer program that captures uncertainty in demand and travel times via a unified chance constraint framework. This framework accommodates multiple uncertainty modeling approaches—standard chance-constraints, CVaR-based constraints, and distributionally robust chance-constraints along with a deterministic benchmark, allowing decision-makers to control robustness levels under varying data availability. To solve this complex problem exactly, we develop a tailored Branch-and-Price algorithm, where the nonlinear pricing subproblem is reformulated as a Shortest-Path Problem with Chance-Constraints, efficiently solved by a customized dynamic programming approach with new dominance conditions. Notably, the complexity of the uncertainty model remains comparable to the deterministic counterpart, making our approach practical and scalable. Managerial Implications: A case study and synthetic instances demonstrate our framework’s versatility and practical relevance. Our results uncover critical trade-offs between robustness and investment, the effects of delivery time targets and isolation on network structure and fleet-size, and differences across uncertainty-modeling approaches. They also highlight the role of aerial fleet composition on operational efficiency and economic performance. These analyses provide actionable guidance on network configuration, fleet-sizing, and preferred modeling approaches under limited data and resources typically seen in humanitarian response.

Problem definition: The dedicated docket was introduced by the Biden Administration to reform the immigration system. It creates a separate queue for immigration proceedings where judges are supposed to issue a decision for each case within a target timeframe. The administration announced the docket with the goals of speed, accuracy, and fairness. Though the program meets its first goal, legal advocacy groups report that this comes at the expense of the last. Referring to it as a “Denial of justice”, they find that cases on the dedicated docket routinely fail to access legal representation, and have a much lower asylum grant rate. Against this backdrop, we aim to understand the operational implication of the dedicated docket. Methodology/results: We develop a stylized queueing model wherein a policy maker (PM) routes asylees to either the regular or the dedicated docket, and sets a delay target for the dedicated one. Constrained by the target, the court allocates its limited capacity to minimize the average delay. Immigration lawyers schedule their time between dockets to maximize the rate of successful asylum cases. Compared to a single docket, we show that the dedicated-docket system can Pareto-improve both the speed and accuracy. However, if the PM sets its decision poorly, the system may be dominated by a single docket. Moreover, we prove that the dedicated docket system can satisfy three natural fairness rules only if it is dominated by the single docket. Managerial implications: Our analysis can inform both policy makers and legal advocacy groups. We prove that the lack of fairness is a fundamental design flaw of the new program. Nonetheless, through delay differentiation, the program also enables a surprising efficiency gain. Policy makers and legal advocacy groups should be aware of such tradeoffs: the same delay differentiation that enables greater efficiency also creates unfairness between dockets.

Problem definition: In online-marketplace bargaining, how fast to respond to a buyer’s offer is a question many sellers face. Whereas a fast response signals the seller’s eagerness to sell, a slow response signals the seller’s insincerity or irresponsibility. In this paper, we use a unique dataset from eBay’s best-offer platform to study the effect of the seller’s response time on the buyer’s concession. Methodology/results: We first use a multiple instrumental variable (IV) regression to study the average effect of the seller’s response time. We find that the effect of the seller’s response time on the buyer’s concession first increases and then decreases (i.e., following an inverted-U shape). We then develop a new multi-treatment IV forest approach, which incorporates a multiple IV regression into a forest, to study the heterogeneous and nonlinear effects of the seller’s response time. We find that the effect of the seller’s response time varies widely across different items. Finally, we use the results from our empirical analyses to derive the best response time for the seller. We find that using the best response time leads to a sizeable increase in the buyer’s concession. Managerial implications: Our results are helpful to online marketplaces, because we find that the effect of the seller’s response time is nonlinear and heterogeneous across different items. Our results are also helpful to the seller, because we find the best response time that increases the buyer’s concession. Finally, our results are helpful to academia, because we develop a new causal machine learning method for analyzing the heterogeneous effects of multiple treatments with endogeneity issues.

Problem definition: This paper examines the influence of an artificial intelligence (AI) application – facial recognition technology at airports – on flight on-time performance. While facial recognition at airports has the potential to save time during check-in and boarding procedures, flight departures could be delayed due to recognition errors and system inaccuracies of this immature technology. Therefore, the impacts of facial recognition on flight on-time performance remain uncertain and require rigorous empirical investigation. Methodology/results: In this study, we exploit the first terminal-wide implementation of facial recognition in the U.S. and examine its impact on flight on-time performance. Our analysis of flight-level data reveals approximately a 16% reduction in departure delays and a 6% reduction in arrival delays but no increase in early departures or early arrivals. Interestingly, the improvement in on-time performance is smaller for flights to destinations in Asia and Africa, which tend to have a higher proportion of non-Caucasian passengers, but more pronounced for flights with larger seat capacity. Managerial implications: These findings demonstrate that implementing AI tools such as facial recognition can enhance operational efficiency and reduce flight delays on average. However, the magnitude of benefits varies across flight destinations and aircraft sizes. These findings offer valuable insights for firms that are considering the deployment of AI technologies for operational efficiencies.

Problem definition: This paper examines whether widely used online learning algorithms used in pricing can independently reach competitive outcomes or whether they may instead foster tacit collusion. This issue has drawn considerable attention from competition regulators, because algorithmic pricing is increasingly common in digital markets. Understanding when such algorithms lead to equilibrium prices or to supra-competitive prices is critical for buyers, sellers, and policymakers. Methodology/results: We study the behavior of multiarmed bandit algorithms in repeated price competition. These algorithms only observe profits from the prices actually chosen, making them realistic models of automated pricing. Using formal analysis, we show that an important class of online learning algorithms, called mean-based algorithms, reliably converges to the Nash equilibrium in Bertrand competition. This finding is notable because, in general, online learning algorithms do not guarantee convergence to equilibrium. In addition, we run extensive numerical experiments with different widely used bandit algorithms. The experiments confirm that most of them, including those that are not mean based, also converge to equilibrium. We observe supra-competitive prices only in special cases where all sellers implement the same symmetric version of certain algorithms, such as upper confidence bound. Even then, supra-competitive pricing vanishes as the number of competing sellers increases. Managerial implications: Our results highlight that the risk of algorithmic collusion in competitive pricing markets is often overstated. For most practical implementations of bandit algorithms, sellers’ prices converge to competitive levels. Only under very specific and symmetric setups do prices remain above competitive benchmarks, and this effect diminishes with more competitors. These insights provide reassurance to regulators concerned with consumer welfare, as well as to managers considering algorithmic pricing tools. They suggest that, although vigilance is warranted, fears of widespread algorithm-driven collusion may be exaggerated.

Problem definition: Pivoting is a significant shift from the current product under development to an alternative opportunity. Pivoting is strongly advocated in the popular press and commonly used in entrepreneurial product development. However, a high fraction of startups that pivot still fail subsequently, since many entrepreneurs chase pivots that are ultimately not fruitful. We explore how an entrepreneur’s level of experience influences their decision to pivot. Experienced entrepreneurs, possessing a clearer perception of potential obstacles and opportunities, tend to exhibit reduced overconfidence. Methodology/results: We develop a decision-theoretic model to analyze the entrepreneurial choice regarding pivoting, while accounting for the overconfidence, and market and pivot opportunity uncertainties. We show that lack of experience, i.e., a higher level of overconfidence, can systematically influence the pivot decisions. The effect of overconfidence on pivot decision depends on the market uncertainty of the pivot opportunity relative to that of the already initiated product. More specifically, we show that novice (more overconfident) entrepreneurs are likely to make radical risky pivots, whereas experienced (less overconfident) entrepreneurs are more likely to make safe pivots. We find that the dominant development errors for inexperienced founders relate to pivoting and persisting when faced with risky and safe pivots, respectively. Furthermore, we show that if pivoting can be postponed until after the product launch, novice founders are much more likely to postpone safe pivots than experienced founders. We also explore the effects of different types of experience on the entrepreneur’s pivoting decisions. Managerial implications: Our results can benefit entrepreneurs in deciding how to set the duration of their supply chain contracts, choose the types of employees to hire, and select appropriate mentors, depending on the experience level of the founders and their likely pivot behavior. For startup incubators and accelerators, our results indicate what type of training programs to provide for entrepreneurs when accounting for their experience level.

Problem definition: Digital educational technologies have the potential to address educational inequality by providing affordable and accessible learning resources. However, it remains unclear whether access to a wide range of learning resources through digital technologies corrects or exacerbates existing disparities, in both the short and the long run, among children from different socioeconomic backgrounds. Methodology/results: Using data from a unique large-scale field study conducted by a digital reading app for K-12 children, we apply a staggered difference-in-differences design to identify the causal effect of subsidizing a broad scope of digital reading resources. Our results show that providing free access to a broad scope of digital reading materials leads to an immediate increase of 428% in daily reading time, with the largest short-term effects observed among children from less developed cities. However, this initial boost in engagement declines sharply over time, particularly for children in less developed areas. We find suggestive evidence that the long-term difference in reading patterns reflects differing levels of parental involvement in the education of children of different socioeconomic status. The long-term contrast between children from poor and rich cities is stronger during the weekend and holidays when parents are more likely to be present. Compared with children in rich cities, children from poor cities perform worse in selecting the “right difficulty” content to read and are less able to sustain long reading sessions, especially with materials that demand more cognitive resources and parental support. Managerial implications: Our findings provide operational insights for EdTech firms and policy recommendations for policymakers, highlighting the critical role of parental involvement in fostering children’s learning persistence and their long-term participation in subsidized educational programs

Problem Definition. Rapid medical response is critical for out-of-hospital cardiac arrest (OHCA) cases. Using drones to deliver Automated External Defibrillators (AEDs) can significantly enhance the chances of survival by reducing delivery time. This paper aims to optimize the strategic deployment of drones and disposable AEDs within a budget-constrained Emergency Medical Services (EMS) system, using incomplete OHCA data. Unlike previous research, we focus on maximizing the number of timely AED deliveries within a critical window, rather than improving average or tail delivery time metrics. Methodology/Results. We frame this problem as a Modular Capacitated Maximal Covering Location Problem (MC-MCLP), incorporating a time constraint for AED delivery within a narrow therapeutic window. Our model can help alleviate resource imbalances across diverse service regions. We address demand variability using a distributionally robust optimization approach, which enhances decision resilience amid real-world uncertainties. Extensive testing reveals the impact of key parameters on the model, highlighting trade-offs between operational efficiency and both reliability and fairness. A case study of OHCA incidents in Virginia Beach demonstrates our model’s effectiveness in significantly increasing the number of patients reached within the critical time period. Managerial Implications. Our framework ensures prompt assistance to OHCA cases within the vital intervention window while promoting equitable resource allocation across regions. This approach addresses the primary challenges in EMS planning by improving response times within the crucial timeframe and establishing backup emergency resources. Our proposed methodology will enhance OHCA survival rates and optimize EMS resource distribution.

Problem Definition: Growing environmental awareness is prompting consumers to consider reusable alternatives to disposable packaging, driving firms in the takeaway food and beverage sector to explore reusable packaging models. In addition to encouraging consumers to use their personal reusable packaging, some firms now offer firm-owned reusable packaging as an alternative reuse option. This paper examines how price incentives and convenience enhancements shape consumers’ packaging choices and the resulting environmental and profitability implications of reusable packaging models. Methodology/Results: Using a game-theoretical model, we analyze a firm’s pricing and reuse-program decisions when consumers choose among disposable packaging, consumer-owned reusable packaging, and firm-owned reusable packaging. Our key findings are as follows. First, a more eco-conscious market does not necessarily strengthen the firm’s incentive to introduce a firm-owned reusable packaging program. When disposable packaging is inexpensive, the program’s main value lies in price discrimination rather than market expansion; as the market becomes more eco-conscious, this price-discrimination benefit weakens. Second, when disposable packaging is costly, the introduction of firm-owned reusable packaging can increase packaging waste, as the firm may strategically reduce the price discount for consumer-owned reuse. Finally, convenience improvements have asymmetric effects. Improving the convenience of consumer-owned reuse generally reduces packaging waste. However, making the return process more convenient for firm-owned reuse can increase packaging waste by shifting some consumers from consumer-owned to firm-owned reuse, which remains subject to non-return risk. Managerial Implications: Firms in the takeaway food and beverage sector should jointly manage firm-owned and consumer-owned reusable packaging. Price incentives and convenience design should be evaluated based on how they shift consumers across packaging options and affect the tension between firm profitability and packaging-waste reduction.

Problem Definition: Shared decision-making processes, in which doctors and patients work together to choose among treatment options, have gained substantial support from clinicians, policymakers, and health systems. Shared decision-making personalizes care by combining health outcome predictions from doctors with preference-based input from patients. Despite this broad advocacy, important challenges remain: outcome predictions are noisy, patients often misinterpret trade-offs, and it is unclear when personalization improves or worsens outcomes. Methodology/Results: We develop a stylized analytical model to characterize how personalizing outcome predictions and incorporating patient preferences affect patient utility under bounded rationality. We show that personalizing outcome predictions can backfire when doctors overweight noisy signals or when underlying health-outcome heterogeneity is low relative to doctor error. With patient participation, the interaction between doctor and patient errors becomes critical: when patients have strong preferences, this interaction reduces—and can even reverse—the value of personalizing outcome predictions; in contrast, when patients have weak preferences, the same interaction enhances its value. We also uncover a counterintuitive non-monotonic effect: utility losses from personalizing outcome predictions peak not at the lowest, but at moderate levels of health-outcome heterogeneity relative to doctor error. Finally, we show that preference-based personalization can reduce utility when treatments appear similar across patient types and patients misinterpret the associated trade-offs. Managerial implications: Contrary to the common belief that advocates for increasing personalization uniformly, our results show the trade-offs between personalization and standardization that arise from limitations in prediction accuracy and human cognition. Our results describe conditions under which personalization can backfire.