5期

最新文章

• Does the Prohibition of Trade-Through Hurt Liquidity Demanders? AI翻译
  作者：Chen, Ningyuan; Gao, Pin; Kou, Steven
  作者单位：University of Toronto; The Chinese University of Hong Kong, Shenzhen; Shenzhen Institute of Artificial Intelligence & Robotics for Society; Boston University
  摘要：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 trade...
• Dynamic Mechanism Design with Capacity Constraint AI翻译
  作者：He, Wei
  作者单位：Chinese University of Hong Kong
  摘要：We study a project assignment problem, where a principal needs to assign multiple projects to an agent. The agent is privately informed about the cost, which could be high or low. The agent's type evolves stochastically over time. We fully characterize the optimal mechanism via a sequence of deadlines and show that the presence of the capacity constraint reduces the principal's payoff and delays the assignment of projects. In particular, as the number of projects increases, the limit optimal c...
• Pricing in On-Demand and One-Way Vehicle-Sharing Networks AI翻译
  作者：Benjaafar, Saif; Shen, Xiaobing
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
  摘要：We consider the dynamic pricing problem that arises in the context of an on demand vehicle sharing system with one-way trips. Existing results show that a static pricing policy that arises from solving a maximum flow relaxation of the problem guarantees a performance ratio that is bounded by K/(N+ K-1) when travel times are negligible and by root ffiffififfi 1 O(1/ K ) otherwise, where K is the number of vehicles and N is the number of locations. In this paper, we build on these results by pro...
• Tight Guarantees for Static Threshold Policies in the Prophet Secretary Problem AI翻译
  作者：Arnosti, Nick; Ma, Will
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Columbia University
  摘要：In the prophet secretary problem, n values are drawn independently from known distributions and presented in a uniformly random order. A decision maker must accept or reject each value when it is presented and may accept at most k values in total. The objective is to maximize the expected sum of accepted values. We analyze the performance of static threshold policies, which accept the first k values exceeding a fixed threshold (or all such values, if fewer than k exist). We show that an approp...
• Inventory Sharing for Perishable Products: Application to Platelet Inventory Management in Hospital Blood Banks AI翻译
  作者：Zhang, Can; Ayer, Turgay; White, Chelsea C.; Bodeker, Joy N.; Roback, John D.
  作者单位：Duke University; University System of Georgia; Georgia Institute of Technology; Emory University; Emory University
  摘要：Platelets are critical blood products. The management of platelet inventory is particularly challenging because of its perishable nature with a short shelf life. Motivated by a platelet inventory management problem at a two-location hospital system, we study how the wastage of platelets and, more broadly, perishable products can be reduced through inventory sharing. In particular, we consider a system with two locations and a single product (e.g., a two-hospital system sharing blood products, ...
• Using Hospital Admission Predictions at Triage for Improving Patient Length of Stay in Emergency Departments AI翻译
  作者：Chen, Wanyi; Argon, Nilay Tanik; Bohrmann, Tommy; Linthicum, Benjamin; Lopiano, Kenneth; Mehrotra, Abhishek; Travers, Debbie; Ziya, Serhan
  作者单位：Harvard University; Harvard Medical School; Harvard University Medical Affiliates; Massachusetts General Hospital; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Duke University
  摘要：Long boarding times have long been recognized as one of the main reasons behind emergency department (ED) crowding. One of the suggestions made in the literature to reduce boarding times was to predict, at the time of triage, whether a patient will eventually be admitted to the hospital and if the prediction turns out to be admit, start preparations for the patient's transfer to the main hospital early in the ED visit. However, there has been no systematic effort in developing a method to help...
• The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity AI翻译
  作者：Sellke, Mark; Slikvins, Aleksandrs
  作者单位：Institute for Advanced Study - USA; Microsoft
  摘要：We consider incentivized exploration: a version of multiarmed bandits where the choice of arms is controlled by self-interested agents and the algorithm can only issue recommendations. The algorithm controls the flow of information, and the information asymmetry can incentivize the agents to explore. Prior work achieves optimal regret rates up to multiplicative factors that become arbitrarily large depending on the Bayesian priors and scale exponentially in the number of arms. A more basic pro...
• Robustness of Proactive Intensive Care Unit Transfer Policies AI翻译
  作者：Grand-Clement, Julien; Chan, Carri W.; Goyal, Vineet; Escobar, Gabriel
  作者单位：Columbia University; Columbia University; Kaiser Permanente
  摘要：Patients whose transfer to the intensive care unit (ICU) is unplanned are prone to higher mortality rates and longer length of stay. Recent advances in machine learning to predict patient deterioration have introduced the possibility of proactive transfer from the ward to the ICU. In this work, we study the problem of finding robust patient transfer policies that account for the important problem of uncertainty in statistical estimates because of data limitations when optimizing to improve ove...
• Adaptive Discretization in Online Reinforcement Learning AI翻译
  作者：Sinclair, Sean R.; Banerjee, Siddhartha; Yu, Christina Lee
  作者单位：Cornell University
  摘要：Discretization-based approaches to solving online reinforcement learning problems are studied extensively on applications such as resource allocation and cache management. The two major questions in designing discretization-based algorithms are how to create the discretization and when to refine it. There are several experimental results investigating heuristic approaches to these questions but little theoretical treatment. In this paper, we provide a unified theoretical analysis of model-free...
• Strategic Release of Information in Platforms: Entry, Competition, and Welfare AI翻译
  作者：Bimpikis, Kostas; Mantegazza, Giacomo
  作者单位：Stanford University
  摘要：Two-sided platforms play an important role in reducing frictions and facilitating trade, and in doing so they increasingly engage in collecting and processing data about supply and demand. This paper establishes that platforms have an incentive to strategically disclose (coarse) information about demand to the supply side, as this can considerably boost their profits. However, this practice may also adversely affect the welfare of consumers. By optimally designing its information disclosure po...