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Because of a growing population and shrinking arable land, the world is facing a global food crisis. One important solution could be to subsidize farmers to sustain their production so that they can produce more food for consumers and earn more money for themselves. An efficient subsidy program should also aim to reduce income inequality among farmers, as measured by the Gini coefficient of farmers' income. In this paper, we examine and compare the effects of input and output farm subsidy programs. The input subsidy reduces the farmers' input purchasing costs, whereas the output subsidy reduces the farmers' output processing costs. By considering a continuum of infinitesimal price-taking farmers who are heterogeneous in their average yield rates, our equilibrium analysis of a game-theoretical model yields three results. First, both subsidy schemes reduce the aggregate income inequality measured by the Gini coefficient. However, they create the following "opposite" effects: the input subsidy decreases the income gap among farmers (under mild conditions), whereas the output subsidy increases it. Second, farmers with low yield rates prefer the input subsidy, whereas farmers with high yield rates prefer the output subsidy. Third, the output subsidy scheme is more effective in improving the total farmer income than the input subsidy scheme, whereas the input subsidy scheme is more effective in reducing income disparities and improving consumer surplus than the output subsidy scheme. Our results provide new insights for policymakers who are crafting subsidy schemes. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71971184, 71972025, and 72032001], the Departmental General Research Fund of the Hong Kong Polytechnic University [Grant P0008761], and the Research Grants Council of Hong Kong [Grants 15504615 and 15500820]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4850. [ABSTRACT FROM AUTHOR]

This paper studies a dynamic principal–agent setting in which the principal needs to dynamically schedule an agent to work or be suspended. When the agent is directed to work and exert effort, the arrival rate of a Poisson process is increased, which increases the principal's payoff. Suspension, on the other hand, serves as a threat to the agent by delaying future payments. A key feature of our setting is a switching cost whenever the suspension stops and the work starts again. We formulate the problem as an optimal control model with switching and fully characterize the optimal control policies/contract structures under different parameter settings. Our analysis shows that, when the switching cost is not too high, the optimal contract demonstrates a generalized control-band structure. The length of each suspension episode, on the other hand, is fixed. Overall, the optimal contract is easy to describe, compute, and implement. This paper was accepted by Ilia Tsetlin, behavioral economics and decision analysis. Funding: The work of P. Cao is financially supported by the National Natural Science Foundation of China [Grants 72122019 and 71771202]. The work of F. Tian is financially supported by the Hong Kong Research Grants Council (RGC) funding (RGC Early Career Scheme 2022/23) [Grant 27500822]. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2023.4845. [ABSTRACT FROM AUTHOR]

Disputes over transactions on two-sided platforms are common and usually arbitrated through platforms' customer service departments or third-party service providers. This paper studies crowd-judging, a novel crowdsourcing mechanism whereby users (buyers and sellers) volunteer as jurors to decide disputes arising from the platform. Using a rich data set from the dispute resolution center at Taobao, a leading Chinese e-commerce platform, we aim to understand this innovation and propose and analyze potential operational improvements with a focus on in-group bias (buyer jurors favor the buyer, likewise for sellers). Platform users, especially sellers, share the perception that in-group bias is prevalent and systematically sways case outcomes as the majority of users on such platforms are buyers, undermining the legitimacy of crowd-judging. Our empirical findings suggest that such concern is not completely unfounded: on average, a seller juror is approximately 10% likelier (than a buyer juror) to vote for a seller. Such bias is aggravated among cases that are decided by a thin margin and when jurors perceive that their in-group's interests are threatened. However, the bias diminishes as jurors gain experience: a user's bias reduces by nearly 95% as experience grows from zero to the sample median level. Incorporating these findings and juror participation dynamics in a simulation study, the paper delivers three managerial insights. First, under the existing voting policy, in-group bias influences the outcomes of no more than 2% of cases. Second, simply increasing crowd size through either a larger case panel or aggressively recruiting new jurors may not be efficient in reducing the adverse effect of in-group bias. Finally, policies that allocate cases dynamically could simultaneously mitigate the impact of in-group bias and nurture a more sustainable juror pool. This paper was accepted by Vishal Gaur, operations management. Funding: S. A. Yang and A. Zhang acknowledge the support of the Hong Kong General Research Fund [Grant "Decentralizing Platform Governance: Innovations from China; Project 17614921]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4818. [ABSTRACT FROM AUTHOR]

The concept of proper equilibrium was established as a strict refinement of perfect equilibrium. This establishment has significantly advanced the development of game theory and its applications. Nonetheless, it remains a challenging problem to compute such an equilibrium. This paper develops a differentiable path-following method with a compact formulation to compute a proper equilibrium. The method incorporates square-root-barrier terms into payoff functions with an extra variable and constitutes a square-root-barrier game. As a result of this barrier game, we acquire a smooth path to a proper equilibrium. To further reduce the computational burden, we present a compact formulation of an ε-proper equilibrium with a polynomial number of variables and equations. Numerical results show that the differentiable path-following method is numerically stable and efficient. Moreover, by relaxing the requirements of proper equilibrium and imposing Selten's perfection, we come up with the notion of perfect d-proper equilibrium, which approximates a proper equilibrium and is less costly to compute. Numerical examples demonstrate that even when d is rather large, a perfect d-proper equilibrium remains to be a proper equilibrium. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms-Continuous. Funding: This work was partially supported by General Research Fund (GRF) CityU 11306821 of Hong Kong SAR Government. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0148) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0148). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

This paper develops a cost-effective sequential route recommender system to provide real-time routing recommendations for vacant taxis searching for the next passenger. We propose a prediction-and-optimization framework to recommend the searching route that maximizes the expected profit of the next successful passenger pickup based on the dynamic taxi demand-supply distribution. Specifically, this system features a deep learning-based predictor that dynamically predicts the passenger pickup probability on a road segment and a recursive searching algorithm that recommends the optimal searching route. The predictor integrates a graph convolution network (GCN) to capture the spatial distribution and a long short-term memory (LSTM) to capture the temporal dynamics of taxi demand and supply. The GCN-LSTM model can accurately predict the pickup probability on a road segment with the consideration of potential taxi oversupply. Then, the dynamic distribution of pickup probability is fed into the route optimization algorithm to recommend the optimal searching routes sequentially as route inquiries emerge in the system. The recursion tree-based route optimization algorithm can significantly reduce the computational time and provide the optimal routes within seconds for real-time implementation. Finally, extensive experiments using Beijing Taxi GPS data demonstrate the effectiveness and efficiency of the proposed recommender system. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the Hong Kong Research Grants Council [Grants CityU 21500220, CityU 11504322] and the National Natural Science Foundation of China [Grant 72201222]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0112) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0112). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

The Impact of the Prohibition of Trade-through In the paper titled "Does the Prohibition of Trade-Through Hurt Liquidity Demanders?" the authors explore the effects of the order protect rule (OPR) in the United States, which prohibits any trade-through in the stock market that does not execute at the best possible price among fast trading venues. The study found that, whereas trade-throughs may offer flexible trading strategies that can benefit liquidity demanders, the benefits are insignificant for most cases, especially for small trades and stocks with fast resilience. The paper suggests that the current separate regulations for fast and slow venues may be extended to differentiate stocks with fast and slow resilience speeds. The results are supported by a case study using the data from the Australian Securities Exchange. Overall, this study supports the regulation of the OPR. The order protect rule (OPR) in the United States generally prohibits any trade-through, that is, a market order that is not executed at the best possible price among fast (electronic and automated) trading venues. By deriving upper and lower bounds for the difference in the execution costs in a dynamic model, we find that, although trade-through allows for flexible trading strategies and may benefit the liquidity demander, the benefit is insignificant in most cases, especially for small trades and stocks with fast resilience. Therefore, considering other benefits of the OPR studied in the literature, this study supports the regulation and suggests that the current separate regulations for fast and slow venues may be extended to differentiate stocks with fast and slow resilience speeds. Funding: P. Gao received financial support from the National Natural Science Foundation of China [Grants 72201234 and 72192805] and the Research Grant Council of Hong Kong (the Collaborative Research Fund) [Grant C6032-21G]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2454. [ABSTRACT FROM AUTHOR]

In jobs in which workers have the flexibility to decide how much work to supply, such as in the gig economy, the effect of a wage change on work supply can be hard to predict. A wage increase offers workers the opportunity to make more money. At the same time, it allows them to make money faster, so they can enjoy more leisure and do not need to work as much. Economic theory alone does not predict which outcome is more likely to occur, and the empirical evidence is also mixed. This paper provides some psychological insights into this economic problem by showing that the short-term effect of a wage change on work supply depends on how the change is framed. Given a current wage of "work l hours to earn $m," a pay-change frame ("work the same l hours and earn $M") facilitates a change in work supply in the same direction as the wage change, whereas a load-change frame ("work L hours and earn the same $m") facilitates the opposite change. Hence, if strategically framed, a wage decrease can elicit the same increase in work supply as a wage increase. A set of studies demonstrate this wage frame effect on both actual work performance and expressed willingness to work. The findings from these studies offer a behavioral perspective on a classic labor economics problem, document a novel framing effect for the judgment and decision-making literature, and suggest a nudge strategy that managers and policy makers can use in incentive designs. This paper was accepted by Yuval Rottenstreich, behavioral economics and decision analysis. Funding: The research of L. Shen was supported by the Research Grants Council, University Grants Committee of Hong Kong [Grant GRF 14501317]. The research of S. D. Hirshman was supported by the Center for Research on Consumer Financial Decision Making, the University of Colorado Leeds School of Business, and The Research Council of Norway (FAIR, project 262675). Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4591. [ABSTRACT FROM AUTHOR]

For a large class of demand models that allow for multiple consumer types, we present performance guarantees for simple nonpersonalized pricing heuristics relative to optimal personalized pricing. Our results demonstrate that in a general setting, the effectiveness of pricing along a positive vector depends on how the price vector aligns with optimal personalized price vectors. We propose two positive direction vectors: the "economic" and "robust" directions. The economic direction is a convex combination of the optimal personalized price vectors and aims to do well on average. The robust direction offers the best worst-case performance guarantee. By judiciously selecting pricing directions, our results also provide performance guarantees of simple pricing strategies relative to more sophisticated pricing strategies. In particular, we provide performance guarantees for nonpersonalized, optimal linear pricing relative to optimal nonlinear, personalized pricing. Our research also examines the performance of common heuristics for bundle pricing relative to optimal, personalized, bundle-size pricing. Our experiments show that performance often improves when consumer types are clustered and each cluster is offered a price direction. We compared the performance of the k-means clustering heuristic and the farthest point first clustering heuristic. Our findings indicated that k-means clustering has significantly superior performance on average. This suggests that businesses could potentially benefit from implementing k-means clustering in their pricing strategies. In conclusion, our study offers valuable insights and performance guarantees for various pricing strategies and their relative effectiveness. These findings could inform pricing decisions and potentially lead to improved outcomes for firms. This paper was accepted by Chung-Piaw Teo, optimization. Funding: This work was supported by Collaborative Research Funding (CRF) Hong Kong [Grant (CRF) C6032-21G], RGC Hong Kong [Grant 16502819], and Hong Kong University of Science and Technology. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.04173. [ABSTRACT FROM AUTHOR]

Modern consumers are concerned about not only their material payoff, but also the fairness of the transaction when making purchasing decisions. In this paper, we investigate how consumers' inequity aversion affects a manufacturer who sources inputs from upstream suppliers. We find that, when the manufacturer sources from a single supplier or when consumers observe the manufacturer's cost, inequity aversion hurts both the supplier's and manufacturer's profits. However, when the manufacturer sources from multiple suppliers and consumers do not observe the manufacturer's cost, inequity aversion reduces both the suppliers' and manufacturer's margins, which significantly alleviates the double marginalization problem, increases consumer demand, and improves channel efficiency. As a result, inequity aversion benefits the suppliers, manufacturer, and consumers alike, leading to a "win–win–win" outcome. By comparing cases in which consumers observe and do not observe the manufacturer's cost, we also find that, when faced with inequity-averse consumers, a manufacturer may find it optimal to withhold its cost information to help secure lower procurement costs from upstream suppliers. This paper was accepted by Dmitri Kuksov, marketing. Funding: This work was supported by the Hong Kong Research Grants Council [Grant 21500920]. [ABSTRACT FROM AUTHOR]

This paper shows that investments based on deep learning signals extract profitability from difficult-to-arbitrage stocks and during high limits-to-arbitrage market states. In particular, excluding microcaps, distressed stocks, or episodes of high market volatility considerably attenuates profitability. Machine learning-based performance further deteriorates in the presence of reasonable trading costs because of high turnover and extreme positions in the tangency portfolio implied by the pricing kernel. Despite their opaque nature, machine learning methods successfully identify mispriced stocks consistent with most anomalies. Beyond economic restrictions, deep learning signals are profitable in long positions and recent years and command low downside risk. This paper was accepted by Kay Giesecke, finance. Funding: D. Avramov acknowledges the Israel Science Foundation (Grant 288/18) for financial support. S. Cheng acknowledges the General Research Fund of the Research Grants Council of Hong Kong [Project 14502318] for financial support. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4449. [ABSTRACT FROM AUTHOR]

For an unbalanced cooperative game, its grand coalition can be stabilized by some instruments, such as subsidization and penalization, that impose new cost terms to certain coalitions. In this paper, we study an alternative instrument, referred to as cost adjustment, that does not need to impose any new coalition-specific cost terms. Specifically, our approach is to adjust existing cost coefficients of the game under which (i) the game becomes balanced so that the grand coalition becomes stable, (ii) a desired way of cooperation is optimal for the grand coalition to adopt, and (iii) the total cost to be shared by the grand coalition is within a prescribed range. Focusing on a broad class of cooperative games, known as integer minimization games, we formulate the problem on how to optimize the cost adjustment as a constrained inverse optimization problem. We prove NP -hardness and derive easy-to-check feasibility conditions for the problem. Based on two linear programming reformulations, we develop two solution algorithms. One is a cutting-plane algorithm, which runs in polynomial time when the corresponding separation problem is polynomial time solvable. The other needs to explicitly derive all the inequalities of a linear program, which runs in polynomial time when the linear program contains only a polynomial number of inequalities. We apply our models and solution algorithms to two typical unbalanced games, including a weighted matching game and an uncapacitated facility location game, showing that their optimal cost adjustments can be obtained in polynomial time. History: Accepted by Area Editor Andrea Lodi for Design & Analysis of Algorithms—Discrete. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72022018 and 72091210]; the Research Grants Council of the Hong Kong SAR, China [Grant 16210020]; Hong Kong Polytechnic University [Grant P0032007]; and the Youth Innovation Promotion Association, Chinese Academy of Sciences [Grant 2021454]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0268. [ABSTRACT FROM AUTHOR]

High-dimensional Simulation Metamodeling Stochastic kriging has been widely employed for simulation metamodeling to predict the response surface of complex simulation models. However, its use is limited to cases where the design space is low-dimensional because the sample complexity (i.e., the number of design points required to produce an accurate prediction) grows exponentially in the dimensionality of the design space. The large sample size results in both a prohibitive sample cost for running the simulation model and a severe computational challenge due to the need to invert large covariance matrices. To address this long-standing challenge, Liang Ding and Xiaowei Zhang, in their recent paper "Sample and Computationally Efficient Stochastic Kriging in High Dimensions", develop a novel methodology — based on tensor Markov kernels and sparse grid experimental designs — that dramatically alleviates the curse of dimensionality. The proposed methodology has theoretical guarantees on both sample complexity and computational complexity and shows outstanding performance in numerical problems of as high as 16,675 dimensions. Stochastic kriging has been widely employed for simulation metamodeling to predict the response surface of complex simulation models. However, its use is limited to cases where the design space is low-dimensional because in general the sample complexity (i.e., the number of design points required for stochastic kriging to produce an accurate prediction) grows exponentially in the dimensionality of the design space. The large sample size results in both a prohibitive sample cost for running the simulation model and a severe computational challenge due to the need to invert large covariance matrices. Based on tensor Markov kernels and sparse grid experimental designs, we develop a novel methodology that dramatically alleviates the curse of dimensionality. We show that the sample complexity of the proposed methodology grows only slightly in the dimensionality, even under model misspecification. We also develop fast algorithms that compute stochastic kriging in its exact form without any approximation schemes. We demonstrate via extensive numerical experiments that our methodology can handle problems with a design space of more than 10,000 dimensions, improving both prediction accuracy and computational efficiency by orders of magnitude relative to typical alternative methods in practice. Funding: We gratefully acknowledge financial support from the Hong Kong Research Grants Council (GRF 17206821). Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2367. [ABSTRACT FROM AUTHOR]

We study an optimal investment and consumption problem with heterogeneous consumption of basic and luxury goods, together with the choice of time for retirement. The optimal heterogeneous consumption strategies for a class of nonhomothetic utility maximizer are shown to consume only basic goods when the wealth is small, to consume basic goods and make savings when the wealth is intermediate, and to consume almost all in luxury goods when the wealth is large. The optimal retirement policy is shown to be both universal, in the sense that all individuals should retire at the same level of marginal utility that is determined only by income, labor cost, discount factor as well as market parameters, and not universal, in the sense that all individuals can achieve the same marginal utility with different utility and wealth. We also show that individuals prefer to retire as time goes by if the marginal labor cost increases faster than that of income. This paper studies an optimal investment and consumption problem with heterogeneous consumption of basic and luxury goods, together with the choice of time for retirement. The utility for luxury goods is not necessarily a concave function. The optimal heterogeneous consumption strategies for a class of nonhomothetic utility maximizer are shown to consume only basic goods when the wealth is small, to consume basic goods and make savings when the wealth is intermediate, and to consume almost all in luxury goods when the wealth is large. The optimal retirement policy is shown to be both universal, in the sense that all individuals should retire at the same level of marginal utility that is determined only by income, labor cost, discount factor and market parameters, and not universal, in the sense that all individuals can achieve the same marginal utility with different utility and wealth. It is also shown that individuals prefer to retire as time goes by if the marginal labor cost increases faster than that of income. The main tools used in analyzing the problem are from a partial differential equation and stochastic control theory including variational inequality and dual transformation. We finally conduct the simulation analysis for the featured model parameters to investigate practical and economic implications by providing their figures. Funding: This work was supported by Hong Kong Research Grants Council General Research Fund [Grants 15202421 and 15202817], the National Research Foundation of Korea [Grant 2021R1C1C1004647], the PolyU-SDU Joint Research Center on Financial Mathematics, the CAS AMSS-PolyU Joint Laboratory of Applied Mathematics, and Hong Kong Polytechnic University, the National Natural Science Foundation of China [Grant 11971409], and the Engineering and Physical Sciences Research Council (UK) [Grant EP/V008331/1]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2328. [ABSTRACT FROM AUTHOR]

Let T=(V,A) be a tournament with a nonnegative integral weight w(e) on each arc e. A subset F of arcs is called a feedback arc set (FAS) if T\F contains no cycles (directed). A collection F of FASs (with repetition allowed) is called an FAS packing if each arc e is used at most w(e) times by the members of F. The purpose of this paper is to give a characterization of all tournaments T=(V,A) with the property that, for every nonnegative integral weight function w defined on A, the minimum total weight of a cycle is equal to the maximum size of an FAS packing. Funding: This work was supported by the National Natural Science Foundation of China [Grants 11801266 and 11971228]; the Chinese Academy of Sciences [Grants XDA27000000 and ZDBS-LY-7008]; the Ministry of Science and Technology of China [Grant 2018AAA0101002]; the Research Grants Council of Hong Kong; the Fundamental Research Funds for the Central Universities [Grant 020314380035]. [ABSTRACT FROM AUTHOR]

In this paper, we consider a container leasing firm that has elementary and premium containers, which are downward substitutable and for use by elementary contract customers (ECCs), premium contract customers (PCCs), as well as walk-in customers (WICs). ECCs can be satisfied by elementary containers or premium ones at discounted prices while PCCs only accept premium containers. WICs can be satisfied by any type of container at different prices. The objective is to maximise the expected total rental revenue by managing its limited capacity. We formulate this problem as a discrete-time Markov Decision Process and show the submodularity and concavity of the value function. Based on this, we show that the optimal policy can be characterised by a series of rationing thresholds, a series of substitution thresholds and a priority threshold, all of which depend on the system states. We further give conditions under which the optimal policy can be simplified. Numerical experiments are conducted to show the impact of the substitution of two items on the revenue, to compare the performance of the optimal policy with those of the commonly used policies and to investigate the influence of arrival rates on the optimal policy. Last, we extend the basic model to consider different rental durations, ECCs' acceptance behaviour and endogenous prices for WICs. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: This work was supported by National Natural Science Foundation of China/Research Grants Council of Hong Kong Joint Research Scheme [Grant 71661167009, N_PolyU531/16], National Natural Science Foundation of China [Grants 72171016, 71831001], and British Academy\Leverhulme Small Research [Grant SRG19\190059]. This work was also supported by Zhejiang Shuren University Research [Grant KXJ0121605] and Beijing Logistics Informatics Research Base. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2022.4425. [ABSTRACT FROM AUTHOR]

In many markets, buyers sign advance contracts before actual decisions on transactions or consumptions are made. Therefore, a buyer may have private information on expected payoff at the contracting stage, and as time moves on, new information on other components of payoff may arrive. However, prior information can be losable, forgettable, or unattended. In this paper, we investigate how limited memory may influence the optimal design of contracts for sequential screening. Despite memory loss, the buyer can make ex post inference about her initially informed type from the chosen contract. As ex ante screening facilitates subsequent retrospection, the chosen contract can serve as a self-reminding instrument. This would yield an endogenous demand for separation in ex ante contract choice. In response, distortions in the optimal contract design can be either mitigated or intensified, leading to improved or undermined social welfare, respectively. As a result, the equilibrium buyer surplus can be higher than that under perfect memory. We also show that the buyer can exhibit the so-called flat-rate bias, even though her preference is time consistent and perfectly predicted. In addition, as memory can be perfectly recovered from the equilibrium contract choice, investing on any other memory-improving instrument is redundant. Moreover, the buyer's demand for screening can induce her to choose dominated refund contract. Nevertheless, when dominance must be obeyed, the seller may offer a menu of refund contracts with two-way distortions. This paper was accepted by Matthew Shum, marketing. Funding: The financial support offered by the Hong Kong RGC General Research Fund [Grant 14500519] is acknowledged. [ABSTRACT FROM AUTHOR]

Problem Definition: This paper considers a setting in which an airline company sells seats periodically, and each period consists of two selling phases, an early-bird discount phase and a regular-price phase. In each period, when the early-bird discount seat is stocked out, an early-bird customer who comes for the discounted seat either purchases the regular-price seat as a substitute (called buy-up substitution) or simply leaves. Methodology/Results: The optimal inventory level of the discounted seats reserved for the early-bird sale is a critical decision for the airline company to maximize its revenue. The airline company learns about the demands for both discounted and regular-price seats and the buy-up substitution probability from historical sales data, which, in turn, are affected by past inventory allocation decisions. In this paper, we investigate two information scenarios based on whether lost sales are observable, and we provide the corresponding Bayesian updating mechanism for learning about demand parameters and substitution probability. We then construct a dynamic programming model to derive the Bayesian optimal inventory level decisions in a multiperiod setting. The literature finds that the unobservability of lost sales drives the inventory manager to stock more (i.e., the Bayesian optimal inventory level should be kept higher than the myopic inventory level) to observe and learn more about demand distributions. Here, we show that when the buy-up substitution probability is known, one may stock less, because one can infer some information about the primary demand for the discounted seat from the customer substitution behavior. We also find that to learn about the unknown buy-up substitution probability drives the inventory manager to stock less so as to induce more substitution trials. Finally, we develop a SoftMax algorithm to solve our dynamic programming problem. We show that the obtained stock more (less) result can be utilized to speed up the convergence of the algorithm to the optimal solution. Managerial Implications: Our results shed light on the airline seat protection level decision with learning about demand parameters and buy-up substitution probability. Compared with myopic optimization, Bayesian inventory decisions that consider the exploration-exploitation tradeoff can avoid getting stuck in local optima and improve the revenue. We also identify new driving forces behind the stock more (less) result that complement the Bayesian inventory management literature. Funding: Z. Luo acknowledges the financial support by the Internal Start-up Fund of The Hong Kong Polytechnic University [Grant P0039035]. P. Guo acknowledges the financial support from the Research Grants Council of Hong Kong [Grant 15508518]. Y. Wang's work was supported by the Research Grants Council of Hong Kong [Grant 15505318] and the National Natural Science Foundation of China [Grant 71971184]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1169. [ABSTRACT FROM AUTHOR]

We consider periodic review perishable inventory systems with a fixed product lifetime. Unsatisfied demand can be either lost or backlogged. The objective is to minimize the long-run average holding, penalty, and outdating cost. The optimal policy for these systems is notoriously complex and computationally intractable because of the curse of dimensionality. Hence, various heuristic replenishment policies are proposed in the literature, including the base-stock policy, which raises the total inventory level to a constant in each review period. Whereas various studies show near-optimal numerical performances of base-stock policies in the classic system with zero replenishment lead time and a first-in-first-out issuance policy, the results on their theoretical performances are very limited. In this paper, we first focus on this classic system and show that a simple base-stock policy is asymptotically optimal when any one of the product lifetime, demand population size, unit penalty cost, and unit outdating cost becomes large; moreover, its optimality gap converges to zero exponentially fast in the first two parameters. We then study two important extensions. For a system under a last-in-first-out or even an arbitrary issuance policy, we prove that a simple base-stock policy is asymptotically optimal with large product lifetime, large unit penalty costs, and large unit outdating costs, and for a backlogging system with positive lead times, we prove that our results continue to hold with large product lifetime, large demand population sizes, and large unit outdating costs. Finally, we provide a numerical study to demonstrate the performances of base-stock policies in these systems. This paper was accepted by Victor Martinez de Albéniz, operations management. Funding: J. Bu was partially supported by a Hong Kong Polytechnic University Start-up Fund for New Recruits [Grant P0039585]. X. Gong was partially supported by a Chinese University of Hong Kong (CUHK) Direct Grant [Grant 4057147] and the Hong Kong Research Grants Council (RGC) General Research Fund [Grant CUHK14500120]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4400. [ABSTRACT FROM AUTHOR]

Online matching platforms require new approaches to market design because firms can now control many aspects of the search and interaction process through various IT-enabled features. Although choice capacity—the number of candidates a user can view and select—is a key design feature of online matching platforms, its effect on engagement and matching outcomes remains unclear. We examine the effect of different choice capacities on market performance by conducting a randomized field experiment in collaboration with an online dating platform. Specifically, we design four treatment groups with different choice capacities in which users can only interact with other users in the same group and randomly assign the users to the treatment groups. We find that providing more choice capacity to male and female users has different effects on choice behaviors and matching outcomes. Although increasing the choice capacity of male users yields the highest engagement, increasing the choice capacity of female users is the most effective method to increase matching outcomes. We empirically demonstrate four mechanisms underlying the effectiveness of different choice capacity designs and generalize our findings by discussing how choice capacity can be designed to increase engagement and matching outcomes. Online matching platforms require new approaches to market design because firms can now control many aspects of the search and interaction process through various IT-enabled features. Although choice capacity—the number of candidates a user can view and select—is a key design feature of online matching platforms, its effect on engagement and matching outcomes remains unclear. We examine the effect of different choice capacities on the number of choices and matches made on a platform by conducting a randomized field experiment in collaboration with an online dating platform. Specifically, we (1) select users who are of a similar age and live in the same geographical location, (2) design four treatment groups with different choice capacities in which users can only interact with other users in the same group, and (3) randomly assign the users to the treatment groups. We find that providing more choice capacity to male and female users has different effects on choice behaviors and matching outcomes. Although increasing the choice capacity of male users yields the highest engagement, increasing the choice capacity of female users is the most effective method to increase matching outcomes. We posit and empirically demonstrate four mechanisms underlying the effectiveness of different choice capacity designs. Furthermore, we generalize our findings to other online matching platforms and discuss how choice capacity can be designed to increase engagement and matching outcomes. History: Ravi Bapna, Martin Bichler, Bob Day, and Wolfgang Ketter, Senior Editors; Thayer Morrill, Associate Editor. This paper has been accepted for the Information Systems Research Special Section on Market Design and Analytics. Funding: The work described in this paper was substantially supported by the Early Career Scheme grant from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. HKUST 26504318]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2021.1028. [ABSTRACT FROM AUTHOR]

Problem definition: A significant percentage of online consumers place consecutive orders within a short duration. To reduce the total order arrangement cost, an online retailer may consolidate consecutive orders from the same consumer. We investigate how long the retailer should hold the consumer's orders before sending them to a third-party logistics provider (3PL) for processing. In this order-holding problem, we optimize the holding time to balance the total order arrangement cost and the potential delay in delivery. Methodology/results: We model the order-holding problem as a Markov decision process. We show that the optimal order-holding decisions follow a threshold-type policy that is straightforward to implement: Hold any pending orders if the holding time is within a threshold or send them to the 3PL otherwise. Whenever the consumer places a new order, the holding time is reset, and the threshold is updated based on a cumulative set of the past consecutive orders in the consumer's shopping journey. Using a consumer's sequential decision model, we personalize the threshold by finding its closed-form expression in the consumer's order features. We determine the model's coefficients and evaluate the threshold-type policy using the data of the 2020 MSOM Data Driven Research Challenge. Extensive numerical experiments suggest that the personalized threshold-type policy outperforms two commonly used benchmarks by having fewer order arrangements or shorter holding times. Furthermore, personalizing the order-holding decisions is significantly more valuable for "enterprise" customers. Managerial implications: Our research suggests a higher threshold for consumers who are more likely to place consecutive orders within a short duration. The consumers' demographic information has a significant effect on the threshold. Specifically, the threshold is higher for "plus" consumers, female consumers, and consumers in the age group of 16–25 years. The threshold for tier 1 cities is lower than that for tier 2 to tier 4 cities but higher than that for tier 5 cities. History: This paper has been accepted as part of the 2020 MSOM Data Driven Challenge. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71931009, 72201237, and 72231009], the Research Grants Council of Hong Kong [Grants 15501920 and 15501221], the Singapore Ministry of Education Academic Research Fund [Tier 1, Grant RG17/21; Tier 2, Grant MOE2019-T2-1-045], the Association of South-East Asian Nations Business Research Initiative Grant [Grant G17C20421], and the Neptune Orient Lines [Fellowship NOL21RP04]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1201. [ABSTRACT FROM AUTHOR]

We show that managerial learning from stock prices can lead to feedback loop vulnerability: corrective actions based on perceived negative market signals reduce the sensitivity of asset payoffs to stock market information. Less sensitivity discourages liquidity provision and increases the price impact of liquidity shocks. Interestingly, overconfident managers who disregard stock price information may be less vulnerable to the adverse price impact of nonfundamental liquidity shocks. Our empirical evidence strongly supports the model's underlying premises and predictions: First, investment decisions of overconfident CEOs are significantly less responsive to stock price fluctuations. Second, the price impact of liquidity shocks, for example, mutual fund fire sales, is substantially smaller for firms with overconfident CEOs. This paper was accepted by Gustavo Manso, finance. Funding: S. Huang gratefully acknowledges the financial support from the Hong Kong General Research Fund [GRF Project Code 17503317]. Supplemental Material: Data and the internet appendix are available at https://doi.org/10.1287/mnsc.2022.4625. [ABSTRACT FROM AUTHOR]

Many warehouses involved in e-commerce order fulfillment use robotic mobile fulfillment systems. Because demand and variability can be high, scheduling orders, robots, and storage pods in interaction with manual workstations are critical to obtaining high performance. Simultaneously, the scheduling problem is extremely complicated because of interactions between decisions, many of which must be taken timely because of short planning horizons and a constantly changing environment. This paper models all such scheduling decisions in combination to minimize order fulfillment time. We propose two decision methods for the above scheduling problem. The models batch the orders using different batching methods and assign orders and batches to pods and workstations in sequence and robots to jobs. Order picking and stock replenishment operations are included in the models. We conduct numerical experiments based on a real-world case to validate the efficacy and efficiency of the model and algorithm. Instances with 14 workstations, 400 orders, 300 stock-keeping units (SKUs), 160 pods, and 160 robots can be solved to near optimality within four minutes. Our methods can be applied to large instances, for example, using a rolling horizon. Because our model can be solved relatively fast, it can be used to take managerial decisions and obtain executive insights. Our results show that making integrated decisions, even when done heuristically, is more beneficial than sequential, isolated optimization. We also find that positioning pick stations close together along one of the system's long sides is efficient. The replenishment stations can be grouped along another side. Another finding is that SKU diversity per pod and SKU dispersion over pods have strong and positive impacts on the total completion time of handling order batches. Funding: This work was supported by National Natural Science Foundation of China [72025103, 72361137001, 71831008, 72071173] and the Research Grants Council of the Hong Kong Special Administrative Region, China [HKSAR RGC TRS T32-707/22-N]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0265. [ABSTRACT FROM AUTHOR]

The ℓ 0 trend filtering ( ℓ 0 -TF) is a new effective tool for nonparametric regression with the power of automatic knot detection in function values or derivatives. It overcomes the drawback of ℓ 1 -TF that is known to have bias issues. To solve the ℓ 0 -TF problem, we propose an alternating minimization induced active set (AMIAS) search method based on the necessary optimality conditions derived from an augmented Lagrangian framework. The proposed method takes full advantage of the primal and dual variables with complementary supports, and decouples the high-dimensional problem into two subsystems on the active and inactive sets, respectively. A sequential AMIAS algorithm with warm start initialization is developed for efficient determination of the cardinality parameter, along with the output of solution paths. Theoretically, the oracle estimator of ℓ 0 -TF is justified to behave like regression splines under the continuous time setting with mild conditions. Our numerical experiments include simulation studies for comparing ℓ 0 -TF to ℓ 1 -TF and free-knot splines on several synthetic examples, and a real data application of time series segmentation on Hong Kong PM2.5 indexes. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms – Continuous. Funding: This work was supported in part by Hong Kong General Research Fund [No. 17306519]. C. Wen's research is partially supported by National Science Foundation of China [12171449] and Fundamental Research Funds for the Central Universities [WK3470000027, YD2040002019]. X. Wang's research is partially supported by National Natural Science Foundation of China [Grants 72171216, 12231017, 71921001, and 71991474], and the National Key R&D Program of China [No. 2022YFA1003803]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2021.0313. The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0313) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0313). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Problem description: In many markets with demand uncertainties, competing retailers may share inventories for common products that they offer consumers. This paper examines how competitors' product sharing affects their inventory and service-quality decisions. The existing literature has mainly focused on inventory sharing among independent retailers who do not compete with each other. Our research aims to fill the gap in this literature by investigating the tradeoffs of inventory sharing between retailers who directly compete for customers based on service quality. Methodology/results: We develop a game-theoretical model in which two retailers selling a common product from the same manufacturer compete for customers by offering differentiated services together with the product. Each retailer faces stochastic demand that increases in its service quality and decreases in the competitor's service quality. When a retailer runs out of stock of the product, it may replenish its inventory directly from the manufacturer and/or request the competitor's excess inventory if they have an inventory-sharing agreement. We find that inventory sharing may soften or intensify service competition, depending on the transfer price for the shared inventory. Specifically, when retailers agree to share inventory, their service levels decrease in the transfer price if their preseason inventory levels are exogenous, but are nonmonotone in the transfer price if the retailers endogenously choose inventory levels. Moreover, our analysis reveals that the retailers' equilibrium inventory levels will increase in the transfer price and can be higher or lower than their levels in the case without inventory sharing. We also find that with exogenous inventory, the retailers prefer to share inventory at the highest nonmoot transfer price, whereas with endogenous inventory, the retailers may prefer not to share inventory, even at the optimal transfer price, when the level of competition and the preorder cost are high. Finally, we show that with service competition, inventory sharing cannot achieve full coordination under any transfer price. Managerial implications: When deciding whether to share inventory with competitors, managers should consider not only the benefits of inventory pooling, but also the strategic effect of sharing on the firms' inventory choices and service levels. Funding: X. Guo has received research support from the Research Grants Council of Hong Kong [RGC Reference 15501820]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2020.0584. [ABSTRACT FROM AUTHOR]

We study the relative merits of different dispute resolution mechanisms in two-sided platforms in the context of disputes involving malicious reviews and blackmail. We develop a game-theoretic model of the strategic interactions between a seller and a (potentially malicious) consumer. In our model, the seller takes into account the impact of consumer reviews on his future earnings; recognizing this, a malicious consumer may attempt to blackmail the seller by purchasing the product, posting a negative review, and demanding ransom to remove it. Without a dispute resolution mechanism in place, the presence of malicious consumers in the market can lead to a significant decrease in seller profit, especially in settings characterized by high uncertainty about product quality. The introduction of a standard centralized dispute resolution mechanism (whereby the seller can report allegedly malicious reviews to the host platform, which then judges whether to remove the review) can restore efficiency to some extent but requires the platform's judgments to be both very quick and highly accurate. We demonstrate that a more decentralized mechanism (whereby the firm is allowed to remove reviews without consulting the platform, subject to ex post penalties for wrongdoing) can be much more effective, while simultaneously alleviating—almost entirely—the need for the platform's judgments to be quick. Our results suggest that decentralization, when implemented correctly, may represent a more efficient approach to dispute resolution. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Funding: S. A. Yang and A. H. Zhang acknowledge the support of the Hong Kong General Research Fund [Grant "Decentralizing Platform Governance: Innovations from China; Project 17614921]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.4655. [ABSTRACT FROM AUTHOR]

We extend the notion of globalized robustness to consider distributional information beyond the support of the ambiguous probability distribution. We propose the globalized distributionally robust counterpart that disallows any (respectively, allows limited) constraint violation for distributions residing (respectively, not residing) in the ambiguity set. By varying its inputs, our proposal recovers several existing perceptions of parameter uncertainty. Focusing on the type 1 Wasserstein distance, we show that the globalized distributionally robust counterpart has an insightful interpretation in terms of shadow price of globalized robustness, and it can be seamlessly integrated with many popular optimization models under uncertainty without incurring any extra computational cost. Such computational attractiveness also holds for other ambiguity sets, including the ones based on probability metric, optimal transport, ϕ-divergences, or moment conditions, as well as the event-wise ambiguity set. Numerical studies on an adaptive network lot-sizing problem demonstrate the modeling flexibility of our proposal and its emphases on globalized robustness to constraint violation. History: Antonio Frangioni, Area Editor for Design & Analysis of Algorithms—Continuous. Funding: Z. Chen was supported by [General Research Fund Grant 9043424, NSFC/RGC Joint Research Scheme N_CityU105/21] from the Hong Kong Research Grants Council. S. Wang was supported by the National Natural Science Foundation of China [Grants 71922020, 72171221, and 71988101, entitled "Econometric Modeling and Economic Policy Studies"] and the Fundamental Research Funds for the Central Universities [Grant UCAS-E2ET0808X2]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0274) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0274). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

A multidepot capacitated vehicle routing problem aims to serve customers' demands using a fleet of capacitated vehicles located in multiple depots, such that the total travel cost of the vehicles is minimized. We study a variant of this problem, the k-depot split delivery vehicle routing problem (or k-DSDVRP in short), for the situation where each customer's demand can be served by more than one vehicle, and the total number of depots, denoted by k ≥ 2 , is a fixed constant. This is a challenging problem with broad applications in the logistics industry, for which no constant ratio approximation algorithm is known. We develop a new approximation algorithm for the k-DSDVRP, ensuring an approximation ratio of (6 − 4 / k) and a polynomial running time for any fixed constant k ≥ 2. To achieve this, we propose a novel solution framework based on a new relaxation of the problem, a cycle splitting procedure, and a vehicle assignment procedure. To further enhance its efficiency for practical usage, we adapt the newly developed approximation algorithm to a heuristic, which runs in polynomial time even when k is arbitrarily large. Experimental results show that this heuristic outperforms a commercial optimization solver and a standard vehicle routing heuristic. Moreover, our newly proposed solution framework can be applied to developing new constant ratio approximation algorithms for several other variants of the k-DSDVRP with k ≥ 2 being a fixed constant. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71971177, 71725001, U1811462], Research Grants Council of Hong Kong SAR, China [Grant 15221619], and Guangdong Basic and Applied Basic Research Foundation [Grant 2023A1515030260]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2021.0193. The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0193) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0193). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

We study adjustable distributionally robust optimization problems, where their ambiguity sets can potentially encompass an infinite number of expectation constraints. Although such ambiguity sets have great modeling flexibility in characterizing uncertain probability distributions, the corresponding adjustable problems remain computationally intractable and challenging. To overcome this issue, we propose a greedy improvement procedure that consists of solving, via the (extended) linear decision rule approximation, a sequence of tractable subproblems—each of which considers a relaxed and finitely constrained ambiguity set that can be iteratively tightened to the infinitely constrained one. Through three numerical studies of adjustable distributionally robust optimization models, we show that our approach can yield improved solutions in a systematic way for both two-stage and multistage problems. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: Financial support by the Early Career Scheme from the Hong Kong Research Grants Council [Project No. CityU 21502820], the CityU Start-Up Grant [Project No. 9610481], the CityU Strategic Research Grant [Project No. 7005688], the National Natural Science Foundation of China [Project No. 72032005], and Chow Sang Sang Group Research Fund sponsored by Chow Sang Sang Holdings International Limited [Project No. 9229076] is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0181), as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0181). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Content-sharing social network platforms rely heavily on user-generated content to attract users and advertisers, but they have limited authority over content provision. We develop an intervention that leverages social interactions between users to stimulate content production. We study social nudges, whereby users connected with a content provider on a platform encourage that provider to supply more content. We conducted a randomized field experiment (N = 993 , 676) on a video-sharing social network platform where treatment providers could receive messages from other users encouraging them to produce more, but control providers could not. We find that social nudges not only immediately boosted video supply by 13.21% without changing video quality but also, increased the number of nudges providers sent to others by 15.57%. Such production-boosting and diffusion effects, although declining over time, lasted beyond the day of receiving nudges and were amplified when nudge senders and recipients had stronger ties. We replicate these results in a second experiment. To estimate the overall production boost over the entire network and guide platforms to utilize social nudges, we combine the experimental data with a social network model that captures the diffusion and over-time effects of social nudges. We showcase the importance of considering the network effects when estimating the impact of social nudges and optimizing platform operations regarding social nudges. Our research highlights the value of leveraging co-user influence for platforms and provides guidance for future research to incorporate the diffusion of an intervention into the estimation of its impacts within a social network. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Funding: H. Dai thanks the University of California, Los Angeles (UCLA) [Hellman Fellowship and Faculty Development Award] for funding support. R. Zhang is grateful for financial support from the Hong Kong Research Grants Council [Grant 16505418]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4622. [ABSTRACT FROM AUTHOR]

General Data Protection Regulation (GDPR)—the European Union's data protection regulation—has two key principles. It recognizes that individuals own and control their personal (but not contractual) data in perpetuity, leading to three critical privacy rights, namely, the rights to (i) explicit consent (data opt-in), (ii) to be forgotten (data erasure), and (iii) portability (data transfer). It also includes data security mandates against privacy breaches through unauthorized access. We study GDPR's equilibrium impact by including these features in a dynamic two-period model of forward-looking firms and consumers. Firms collect consumer data for personalization and price discrimination. Consumers trade off gains from personalization relative to potential losses from privacy breaches and price discrimination in their purchase, data opt-in, erasure, and transfer decisions. Though data security mandates impose fines on firms for privacy breaches, firms can benefit from higher opt-in given lower breach risk. Surprisingly, data security mandates can hurt consumers. The effect of privacy rights is nuanced. Since the right to opt in separates goods exchange from the provision of personal data, it prevents market failure under high breach risk. But it also reduces consumer opt-in and personal data availability. Erasure and portability rights reduce consumers' hold-up concerns by disciplining firms to provide ongoing value by limiting price discrimination and not slacking off on data security; but they also reduce the incentive to offer lower initial prices that encourages opt-in. Overall, privacy rights always benefit consumers in competitive markets, but they can surprisingly hurt consumers under monopoly, as monopolists have less incentives to subsidize consumer opt-in. They raise (reduce) firm profit and social welfare when breach risk is high (low). Finally, privacy rights increase firm profit most at moderate levels of data transferability. This paper was accepted by Dmitri Kuksov, marketing. Funding: T. T. Ke acknowledges financial support from the General Research Fund of the Hong Kong Research Grants Council [Grant 14500421]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/mnsc.2022.4614. [ABSTRACT FROM AUTHOR]

The extended trust region subproblem (ETRS) of minimizing a quadratic objective over the unit ball with additional linear constraints has attracted a lot of attention in the last few years because of its theoretical significance and wide spectra of applications. Several sufficient conditions to guarantee the exactness of its semidefinite programming (SDP) relaxation have been recently developed in the literature. In this paper, we consider a generalization of the extended trust region subproblem (GETRS), in which the unit ball constraint in the ETRS is replaced by a general, possibly nonconvex, quadratic constraint, and the linear constraints are replaced by a conic linear constraint. We derive sufficient conditions for guaranteeing the exactness of the SDP relaxation of the GETRS under mild assumptions. Our main tools are two classes of convex relaxations for the GETRS based on either a simultaneous diagonalization transformation of the quadratic forms or linear combinations of the quadratic forms. We also compare our results to the existing sufficient conditions in the literature. Finally, we extend our results to derive a new sufficient condition for the exactness of the SDP relaxation of general diagonal quadratically constrained quadratic programs, where each quadratic function is associated with a diagonal matrix. Funding: This work was supported by the National Natural Science Foundation of China [Grants 11801087, 12171100, 72161160340]; Hong Kong Research Grants Council [Grants 14213716 and 14202017]; and Natural Science Foundation of Shanghai [Grant 22ZR1405100]. [ABSTRACT FROM AUTHOR]

Government bonds might provide reference entities that reduce corporate bond yields. We study China's 2017 issuance of two U.S. dollar (USD)-denominated sovereign bonds when there were (effectively) no outstanding USD sovereigns. We find that Chinese corporate USD bonds experienced a four- to nine-basis-point decline in yield spreads, whereas corporate renminbi (RMB) bonds did not. The effect was stronger for corporate bonds with maturities similar to those of the USD sovereigns. Further consistent with the reference effect, USD-denominated corporate bonds experienced declines in bid-ask spreads and volatility; corporate bond yield spread changes became more sensitive to sovereign yield innovations; and the less-informed foreign mutual funds significantly increased their holdings of offshore USD Chinese corporate bonds. Limited evidence indicates that new corporate bond maturities shifted toward the sovereign bonds' maturity after their issuance. This paper was accepted by Lukas Schmid, finance. Funding: B. Wang received financial support from Hong Kong Institute for Monetary Research. C. Y. Hong received financial support from the National Natural Science Foundation of China [Grant 72003125]. Supplemental Material: The internet appendix and data are available at https://doi.org/10.1287/mnsc.2022.4499. [ABSTRACT FROM AUTHOR]

We study the effects of implicit government guarantee (IGG) on Chinese state-owned enterprises (SOEs). We find that SOEs reduce their investments by 2.4% of book assets, on average, relative to matched non-SOEs after the first SOE default in China's onshore bond markets in 2015. The investment reduction concentrates among SOEs that are financially constrained, yet SOEs financed by large state banks are hardly affected. Bondholders require more stringent default protection in newly issued SOE bonds. We also find that the investment reduction is more pronounced for SOEs with severe agency problems and that SOEs experienced more positive market reactions to acquisition announcements after 2015. Our results suggest that the reduction in IGG has confounding effects on Chinese firms. Although the weakening of IGG may help mitigate overinvestment, it exacerbates financial constraints of those with limited access to alternative sources of financing. This paper was accepted by Lucas Schmid, finance. Funding: Z. Zhang acknowledges the funding support by Research Grants Council of Hong Kong [Project 11503318]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4483. [ABSTRACT FROM AUTHOR]

Problem definition: A blockchain payment system, such as Bitcoin or Ethereum, validates electronic transactions and stores them in a chain of blocks without a central authority. Miners with computing power compete for the rights to create blocks according to a preset protocol, referred to as hashing or mining, and, in return, earn fees paid by users who submit transactions. Because of security concerns caused by decentralization, a transaction is confirmed after a number of additional blocks are subsequently extended to the block containing it. This confirmation latency introduces an intricate interplay between miners and users. This paper provides approximate system equilibria and studies optimal designs of a blockchain. Methodology/results: The hashing process is essentially a single-server queue with batch services based on a fee-based priority discipline, and confirmation latency adds complexity to the equilibrium behavior and optimal design. We analyze how miners' participation decisions interact with users' participation and fee decisions and identify optimal designs when the goal is to maximize the throughput or social welfare. We validate our model and conduct numerical studies using data from Bitcoin. Managerial implications: By incorporating security issues, we uncover the interdependence of the decisions between users and miners and the driver for nonzero entrance fees in practice. We show that miners and users may end up in either a vicious or virtuous cycle, depending on the initial system state. By allowing the entrance fee to be a design parameter, we are able to establish that it is optimal to simply run a blockchain system at its full capacity and a block size as small as possible. Funding: This work was supported by the Hong Kong Research Grants Council [Grants 16200019, 16200617, 16200821, 16208120, and 16214121]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1197. [ABSTRACT FROM AUTHOR]

We consider a mixed duopoly service system with two service providers (SPs): one private and the other public. The public SP's objective is welfare maximization, whereas the private SP is profit driven. Customers are heterogeneous in terms of their quality tastes and choose from three options, namely, joining the private queue, joining the public queue, and balking (or taking an outside option). We first consider the scenario where the private SP provides a premium service and the public SP provides a regular service. Paradoxically, we find that although the public SP's objective is welfare maximization, welfare in the system can be less than that obtained with only profit-seeking private SPs. We further demonstrate that the maximum social welfare is achieved by partially privatizing the public SP, that is, by including both welfare and profit maximization as arguments in its objective function. In extreme cases in which the public SP's capacity is very small, fully privatizing the public SP can be socially desirable. We then consider the alternate setting where the public SP provides a premium service and the private SP provides a regular service and obtain similar conclusions. These findings, however, rely on the assumption that an outside option exists for balking customers, and one should be cautious in generalizing to no-balking situations. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: W. Zhou was supported by the National Natural Science Foundation of China [Grants 71925002 and 71731006]. W. Huang acknowledges the financial support from the National Natural Science Foundation of China [Grants 71801096 and 72101090] and the Guangdong Basic and Applied Basic Research Foundation [Grant 2022A1515011983]. V. N. Hsu was supported by the General Research Fund of the Hong Kong Research Grants Council [Grant CUHK14526216]. P. Guo received financial support from the Hong Kong Research Grants Council [Grant 15502820]. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2022.4424. [ABSTRACT FROM AUTHOR]

Corporate bonds with large increases in implied volatility over the past month underperform those with large decreases in implied volatility by 0.6% per month. In contrast to existing studies that show implied volatility changes carry information about fundamental news, our evidence suggests that implied volatility changes contain information about uncertainty shocks to the firm. Our results are consistent with the notion that informed traders with new information about firm risk prefer to trade in the option market and the corporate bond market underreacts to this information. This paper was accepted by Haoxiang Zhu, finance. Funding: We thank the Canadian Derivatives Institute for financial support. J. Cao and X. Zhan acknowledge generous financial support of the Research Grant Council of the Hong Kong Special Administrative Region, China [Grants GRF 14501720, 14500919]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4379. [ABSTRACT FROM AUTHOR]

Problem definition: In this paper, we explore how a firm's concern about profit distribution and the size of downstream firms in supply chains affect corporate social responsibility (CSR) investment strategy. Methodology/results: In a supply chain consisting of one supplier and one manufacturer, both players decide whether to invest to reduce CSR violations, and they negotiate over a wholesale price. Distributive comparison behavior makes the manufacturer compare the profit with his equitable payoff, which is determined by the supplier's profit. Advantageous (resp. disadvantageous) inequality occurs when the manufacturer's profit is higher (resp. lower) than the manufacturer's equitable payoff. We compare this supply chain to the one without distributive comparison behavior. We find that when advantageous inequality occurs, or when neither inequality occurs and the manufacturer's sensitivity to the supplier's profit is low, the manufacturer's distributive comparison behavior makes the manufacturer less (resp. supplier more) likely to invest in CSR, which we call negative (resp. positive) impacts of distributive comparison behavior; otherwise, it makes the manufacturer more (resp. supplier less) likely to invest. In most cases, the weak bargaining power of the small manufacturer leads to larger positive or smaller negative impacts of distributive comparison behavior. Also, the low efficiency of the small manufacturer to reduce CSR violations leads to smaller negative impacts of distributive comparison behavior. Managerial implications: Our results show that governments and nongovernmental organizations (NGOs) should investigate firms' distributive comparison behavior in supply chains. When downstream firms show the aversion to lower (resp. higher) profits than ones from upstream firms, the measures to monitor and support upstream (resp. downstream) firms' CSR investments should be taken to avoid CSR violations. In the supply chains with small downstream firms, extra efforts should be made to induce firms' distributive comparison behavior. Funding: M. Wang was supported partially by the National Natural Science Foundation of China [Grants 71931009 and 71671023]; X. Fang is grateful for the support under a Lee Kong Chian Fellowship and Retail Centre of Excellence Research Grant; Z. Wang was supported partially by the National Natural Science Foundation of China [Grants 72010107002, 71671023, and 72171212]; and Y. Chen was supported partially by the Research Grants Council of the Hong Kong Special Administrative Region, China [HKUST C6020-21GF]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1172. [ABSTRACT FROM AUTHOR]

We consider the optimal sourcing problem when the available suppliers are subject to ambiguously correlated supply risks. This problem is motivated by the increasing severity of supply risks and difficulty evaluating common sources of vulnerability in upstream supply chains, which are problems reported by many surveys of goods-producing firms. We propose a distributionally robust model that accommodates (i) multiple levels of supply disruption, not just full delivery or no delivery, and (ii) can use data-driven estimates of the underlying correlation to develop sourcing strategies in situations where the true correlation structure is ambiguous. Using this framework, we provide analytical results regarding the form of a worst-case supply distribution and show that taking such a worst-case perspective is appealing due to severe consequences associated with supply chain risks. Moreover, we show how our distributionally robust model may be used to offer guidance to firms considering whether to exert additional effort in attempt to better understanding the prevailing correlation structure. Extensive computational experiments further demonstrate the performance of our distributionally robust approach and show how supplier characteristics and the type of supply uncertainty affect the optimal sourcing decision. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods and Analysis. Funding: K. Pan was supported in part by the Research Grants Council of Hong Kong [Grant 15501920] and in part by the National Natural Science Foundation of China [Grant 72001185]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1254) or is available from the IJOC GitHub software repository (https://github.com/INFORMSJoC) at http://dx.doi.org/10.5281/zenodo.7129356. [ABSTRACT FROM AUTHOR]

We develop a game-theoretic model to study the incentive for competing manufacturers to share supplier audit information. Based on the audit information, each manufacturer decides whether to source from a common supplier who has uncertain responsibility violation risk or to switch to a backup supplier who has no responsibility violation risk but charges a higher price. When supplier responsibility violation occurs, some consumers boycott the manufacturers involved. Audit information allows a manufacturer to reduce the uncertainty about the risk of the common supplier. We show that audit information sharing may make the manufacturers' sourcing strategies more or less differentiated. As a result, the information-sharing decision is not monotone in the model parameters. We fully characterize the manufacturers' equilibrium audit information-sharing and sourcing decisions and establish conditions under which audit information sharing induces the manufacturers to adopt more or less responsible sourcing strategies. We also show that a manufacturer could be better off when the cost premium of sourcing from the backup supplier or the risk of the common supplier becomes higher or the audit information becomes less accurate. We consider several extensions of the base model and demonstrate that the main insights remain mostly valid. This paper was accepted by Charles Corbett, operations management. Funding: A. Y. Hawas supported by the Wei Lun Foundation. W. Shang was supported by the Research Grants Council of Hong Kong [GRF Project LU 13501415]. Y. Wang was supported by National Natural Science Foundation of China [Grant 71901209]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4358. [ABSTRACT FROM AUTHOR]

Uncertainty may exist about the desirability of trade in bilateral bargaining. For instance, buyers may not know their value perfectly and sellers may not be fully aware of their cost structure. We endogenize the expected surplus of trade by considering information gathering before price negotiation between a seller and a buyer. We show that prebargaining information acquisition can reverse standard findings in canonical bargaining models, regarding how the bargaining primitives may influence the equilibrium expected payoffs and the negotiated price. In particular, a higher bargaining power can result in a lower expected payoff, because the other party's reduced incentive to acquire information would reduce the total pie to be split between the parties. In the same vein, the seller's expected payoff can decrease as its material cost becomes lower or its outside option improves, and the buyer can be hurt by a higher value from the transaction or from its outside option. Similarly, the seller/buyer may become worse off by having more information if that induces the counter party to acquire less information. In addition, the expected negotiated price may decrease with the seller's relative bargaining power, its material/opportunity cost, or the buyer's incremental value. We also examine the robustness of the basic results under joint information acquisition or noncredible communication. Moreover, we show that a shopping intermediary may prefer to decrease the seller's bargaining power or increase the buyer's cost of gathering information. We discuss how our findings can shed light on practice and empirical research. This paper was accepted by Dmitri Kuksov, marketing. Funding: This workwas supported by Hong Kong RGC [DAG Grant]. [ABSTRACT FROM AUTHOR]

We examine the consequences of a regulatory intervention aimed at generalizing tenure voting in French public companies. The 2014 Florange Act departs from the 'one share one vote' principle by automatically granting double-voting rights (DVR) to shares held for at least two years. However, firms can opt out through an annual meeting vote. We document three main findings. First, firms that adopt DVR by default experience a decrease in long-term foreign institutional ownership offset by an increase in insider/family ownership. Second, those firms significantly underperform in terms of stock returns after Florange relative to those that reject DVR. Third, their environmental and social performance deteriorates. Collectively, our evidence casts doubt on the merit of regulation-induced tenure voting as a desirable corporate governance mechanism. This paper was accepted by Brian Bushee, accounting. Funding: This work was supported by Research Grants Council of Hong Kong [16513216]. We also acknowledge the General Research Fund of the Research Grants Council of Hong Kong [Project No. 16505017] for financial support. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2022.4320. [ABSTRACT FROM AUTHOR]

The electricity production of a thermal generator is often constrained by the available fuel supply. These fuel constraints impose a maximum bound on the energy output over multiple time periods. Fuel constraints are increasingly important in electricity markets because of two main reasons. First, as more natural gas-fired generators join the deregulated market, there is often competition for natural gas supply from other sectors (e.g., residential and manufacturing heating). Second, as more environmental and emission regulations are being placed on fossil fuel-fired generators, fuel supply is becoming more limited. However, there are few studies that consider the fuel constraints in the unit commitment problem from the perspective of computational analysis. To address the challenge faced by an independent power producer with a limited fuel supply, we study a fuel-constrained self-scheduling unit commitment (FSUC) problem where the production decisions are coupled across multiple time periods. We provide a complexity analysis of the FSUC problem and conduct a comprehensive polyhedral study by deriving strong valid inequalities. We demonstrate the effectiveness of our proposed inequalities as cutting planes in solving various multistage stochastic FSUC problems. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: K. Pan was supported in part by the Research Grants Council of Hong Kong [Grant 15501920]. F. Qiu was supported in part by the U.S. Department of Energy Advanced Grid Modeling Program [Grant DE-OE0000875]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplementary Information [https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1235] or is available from the IJOC GitHub software repository (https://github.com/INFORMSJoC) at [http://dx.doi.org/10.5281/zenodo.6896199]. [ABSTRACT FROM AUTHOR]

When Service Times Depend on Customers' Delays: A Relationship Between Two Models of Dependence Service times of customers often depend on the delay they experience in queue, as was recently demonstrated empirically in restaurants, call centers, and intensive care units. Two forms of dependence mechanisms in service systems with customer abandonment are studied in this paper: First, the service requirement of a customer may evolve while waiting in queue. Second, customers may arrive to the system with an exogenous service and patience time that are stochastically dependent. Because either dependence mechanism can have significant impacts on a system's performance, it should be identified and taken into consideration for performance evaluation and decision-making purposes. However, identifying the source of dependence from observed data is hard because both the service times and patience times are censored due to customer abandonment. Further, even if the dependence is known to be the latter exogenous one, there remains the difficult task of fitting a joint service-patience times distribution to the censored data. In "When Service Times Depend on Customers' Delays: A Relationship Between Two Models of Dependence", Wu, Bassamboo, and Perry provide a solution to address these statistical challenges. As empirically observed in restaurants, call centers, and intensive care units, service times needed by customers are often related to the delay they experience in queue. Two forms of dependence mechanisms in service systems with customer abandonment immediately come to mind: First, the service requirement of a customer may evolve while waiting in queue, in which case the service time of each customer is endogenously determined by the system's dynamics. Second, customers may arrive (exogenously) to the system with a service and patience time that are stochastically dependent, so that the service-time distribution of the customers that end up in service is different than that of the entire customer population. We refer to the former type of dependence as endogenous and to the latter as exogenous. Because either dependence mechanism can have significant impacts on a system's performance, it should be identified and taken into consideration for performance-evaluation and decision-making purposes. However, identifying the source of dependence from observed data is hard because both the service times and patience times are censored due to customer abandonment. Further, even if the dependence is known to be exogenous, there remains the difficult problem of fitting a joint service-patience times distribution to the censored data. We address these two problems and provide a solution to the corresponding statistical challenges by proving that both problems can be avoided. We show that, for any exogenous dependence, there exists a corresponding endogenous dependence, such that the queuing dynamics under either dependence have the same law. We also prove that there exist endogenous dependencies for which no equivalent exogenous dependence exists. Therefore, the endogenous dependence can be considered as a generalization of the exogenous dependence. As a result, if dependence is observed in data, one can always consider the system as having an endogenous dependence, regardless of the true underlying dependence mechanism. Because estimating the structure of an endogenous dependence is substantially easier than estimating a joint service-patience distribution from censored data, our approach facilitates statistical estimations considerably. Funding: C. A. Wu received financial support from the Hong Kong Research Grant Council [Early Career Scheme, Project 26206419]. A. Bassamboo and O. Perry received partial financial support from the National Science Foundation [Grant CMMI 2006350]. [ABSTRACT FROM AUTHOR]

Firms in many industries obtain superior knowledge of customer preferences through industry experience or data analytics, whereas customers often need costly efforts to learn their match values. In this paper, we examine the situations under which a customer chooses whether to inspect upon observing her personalized price from a firm with superior knowledge. On the surface, it seems that the firm can use personalized prices to directly communicate the customers' match value, and thus there is no need for customers to expend inspection efforts. However, we find that in equilibrium the firm may trick low-preference customers into overpaying more than their match value, even when the inspection cost is low. The opportunistic incentives induce customer suspicions, which may lead to excessive customer inspection that would be avoided if the firm were not capable of price discrimination. Therefore, personalized pricing cannot obviate customer inspection. Since inspection cost raises a deadweight loss in social welfare, public policies that prevent firms from price-discriminating against customers may benefit both firms and customers. History: Yuxin Chen served as the senior editor for this article and Zsolt Katona served as associate editor for this article. Funding: This work was supported by the Hong Kong Research Grants Council [Grant 21500920] and the National Natural Science Foundation of China [Grant 72172089]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/mksc.2022.1355. [ABSTRACT FROM AUTHOR]