Operations Research

最新文章

• Demand Estimation Under Uncertain Consideration Sets AI翻译
  作者：Jagabathula, Srikanth; Mitrofanov, Dmitry; Vulcano, Gustavo
  作者单位：New York University; Boston College; Universidad Torcuato Di Tella
  摘要：To estimate customer demand, choice models rely both on what the individuals do and do not purchase. A customer may not purchase a product because it was not offered but also because it was not considered. To account for this behavior, existing literature has proposed the so-called consider-then-choose (CTC) models, which posit that customers sample a consideration set and then choose the most preferred product from the intersection of the offer set and the consideration set. CTC models have b...
• Robust Risk Quantification via Shock Propagation in Financial Networks AI翻译
  作者：Ahn, Dohyun; Chen, Nan; Kim, Kyoung-Kuk
  作者单位：Chinese University of Hong Kong; Korea Advanced Institute of Science & Technology (KAIST)
  摘要：Given limited network information, we consider robust risk quantification under the Eisenberg-Noe model for financial networks. To be more specific, motivated by the fact that the structure of the interbank network is not completely known in practice, we propose a robust optimization approach to obtain worst-case default probabilities and associated capital requirements for a specific group of banks (e.g., systemically important financial institutions) under network information uncertainty. Us...
• Adversarial Robustness for Latent Models: Revisiting the Robust-Standard Accuracies Tradeoff AI翻译
  作者：Javanmard, Adel; Mehrabi, Mohammad
  作者单位：University of Southern California
  摘要：Over the past few years, several adversarial training methods have been proposed to improve the robustness of machine learning models against adversarial perturbations in the input. Despite remarkable progress in this regard, adversarial training is often observed to drop the standard test accuracy. This phenomenon has intrigued the research community to investigate the potential tradeoff between standard accuracy (a.k.a generalization) and robust accuracy (a.k.a robust generalization) as two ...
• Robust Dynamic Assortment Optimization in the Presence of Outlier Customers AI翻译
  作者：Chen, Xi; Krishnamurthy, Akshay; Wang, Yining
  作者单位：New York University; University of Texas System; University of Texas Dallas
  摘要：We consider the dynamic assortment optimization problem under the multinomial logit model with unknown utility parameters. The main question investigated in this paper is model mis-specification under the e-contamination model, which is a fundamental model in robust statistics and machine learning. In particular, throughout a selling horizon of length T, we assume that customers make purchases according to a well-specified underlying multinomial logit choice model in a (1 - e)-fraction of the ...
• Exploration and Incentives in Reinforcement Learning AI翻译
  作者：Simchowitz, Max; Slivkins, Aleksandrs
  作者单位：Massachusetts Institute of Technology (MIT)
  摘要：How do you incentivize self-interested agents to explore when they prefer to exploit? We consider complex exploration problems, where each agent faces the same (but unknown) Markov decision process (MDP). In contrast with traditional formulations of reinforcement learning, agents control the choice of policies, whereas an algorithm can only issue recommendations. However, the algorithm controls the flow of information, and can incentivize the agents to explore via information asymmetry. We des...
• In Congestion Games, Taxes Achieve Optimal Approximation AI翻译
  作者：Paccagnan, Dario; Gairing, Martin
  作者单位：Imperial College London; University of Liverpool
  摘要：In this work, we address the problem of minimizing social cost in atomic congestion games. For this problem, we present lower bounds on the approximation ratio achievable in polynomial time and demonstrate that efficiently computable taxes result in polynomial time algorithms matching such bounds. Perhaps surprisingly, these results show that indirect interventions, in the form of efficiently computed taxation mechanisms, yield the same performance achievable by the best polynomial time algori...
• Incomplete Information VCG Contracts for Common Agency AI翻译
  作者：Alon, Tal; Talgam-Cohen, Inbal; Lavi, Ron; Shamash, Elisheva
  作者单位：Technion Israel Institute of Technology; University of Bath; Keele University
  摘要：We study contract design for welfare maximization in the well-known common agency model introduced in 1986 by Bernheim and Whinston. This model combines the challenges of coordinating multiple principals with the fundamental challenge of contract design: that principals have incomplete information of the agent's choice of action. Our goal is to design contracts that satisfy truthfulness of the principals, welfare maximization by the agent, and two fundamental properties of individual rationali...
• Flattening Energy-Consumption Curves by Monthly Constrained Direct Load Control Contracts AI翻译
  作者：Fattahi, Ali; Ghodsi, Saeed; Dasu, Sriram; Ahmadi, Reza
  作者单位：Johns Hopkins University; University of California System; University of California Los Angeles; University of Southern California
  摘要：Balancing electricity demand and supply is one of the most critical tasks that utility firms perform to maintain grid stability and reduce system cost. Demand-response programs are among the strategies that utilities use to reduce electricity consumption during peak hours and flatten the energy-consumption curve. Direct load control contracts (DLCCs) are a class of incentive-based demand-response programs that allow utilities to assign calls to customer groups to reduce their energy usage by a...
• Dynamic Pricing and Learning with Discounting AI翻译
  作者：Feng, Zhichao; Dawande, Milind; Janakiraman, Ganesh; Qi, Anyan
  作者单位：Hong Kong Polytechnic University; University of Texas System; University of Texas Dallas
  摘要：In many practical settings, learning algorithms can take a substantial amount of time to converge, thereby raising the need to understand the role of discounting in learning. We illustrate the impact of discounting on the performance of learning algorithms by examining two classic and representative dynamic-pricing and learning problems studied in Broder and Rusmevichientong (BR) [Broder J, Rusmevichientong P (2012) Dynamic pricing under a general parametric choice model. Oper. Res. 60(4):965-...
• Least Squares Monte Carlo and Pathwise Optimization for Merchant Energy Production AI翻译
  作者：Yang, Bo; Nadarajah, Selvaprabu; Secomandi, Nicola
  作者单位：Carnegie Mellon University; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
  摘要：We study merchant energy production modeled as a compound switching and timing option. The resulting Markov decision process is intractable. Least squares Monte Carlo combined with information relaxation and duality is a state-of-the-art reinforcement learning methodology to obtain operating policies and optimality gaps for related models. Pathwise optimization is a competing technique developed for optimal stopping settings, in which it typically provides superior results compared with this a...