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作者：Correa, Jose; Cristi, Andres; Norouzi-Fard, Ashkan; Norouzi-Fard, Ashkan
作者单位：Universidad de Chile; Alphabet Inc.; Google Incorporated
摘要：There is growing awareness and concern about fairness in machine learning and algorithm design. This is particularly true in online selection problems, where decisions are often biased: for example, when assessing credit risks or hiring staff. We address the issues of fairness and bias in online selection by studying multicolor versions of the classic secretary and prophet problems. In the multicolor secretary problem, we consider that each candidate has a color, and we can only compare candid...
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作者：Chen, Xi; Lyu, Jiameng; Zhang, Xuan; Zhou, Yuan
作者单位：New York University; Fudan University; University of Illinois System; University of Illinois Urbana-Champaign; Tsinghua University
摘要：Price discrimination, which refers to the strategy of setting different prices for different customer groups, has been widely used in online retailing. Although it helps boost the collected revenue for online retailers, it might create serious concerns about fairness, which even violates regulations and laws. This paper studies the problem of dynamic discriminatory pricing under a relative price fairness constraint in the pricing literature. We first establish a regret lower bound of ohm(T4=5)...
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作者：Simchi-Levi, David; Xu, Yunzong; Zhao, Jinglong
作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of Illinois System; University of Illinois Urbana-Champaign; Boston University
摘要：This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributionally unknown demand, and must allocate finite initial inventory across multiple resources over time. In addition to standard resource constraints, we impose a switching constrai...
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作者：Ba, Wenjia; Lin, Tianyi; Zhang, Jiawei; Zhou, Zhengyuan
作者单位：University of British Columbia; Columbia University; New York University
摘要：We consider online no-regret learning in unknown games with bandit feedback, where each player can only observe its reward at each time-determined by all players' current joint action-rather than its gradient. We focus on the class of smooth and strongly monotone games and study optimal no-regret learning therein. Leveraging self-concordant barrier functions, we first construct a new bandit learning algorithm and show that it root ffiffiffi achieves the single-agent optimal regret of Theta ( n...
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作者：Deng, Tianhu; Shao, Feiyu; Song, Jing-Sheng Jeannette; Yu, Yi
作者单位：Soochow University - China; Tsinghua University; Duke University; Shanghai University of Finance & Economics
摘要：To enhance supply chain resilience, assembly manufacturers increasingly adopt dual-sourcing strategies, utilizing both regular and faster but costlier express sources for each key component. Although research has focused on single-item systems, coordinating dual-sourced orders across multiple components in an assembly system remains underexplored. To address this gap, we introduce a novel Critical-Set Base-Surge (CSBS) policy, which combines a constant order policy for regular sources to meet ...
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作者：Elmachtoub, Adam N.; Kim, Hyemi
作者单位：Columbia University; Columbia University
摘要：Vehicle sharing systems, such as those for bicycles, scooters, and cars, are fundamental to serve transportation needs. Companies that operate these systems set prices (or fares) using algorithms to determine how much a user must pay and display the fares through mobile applications. This may result in users from different locations paying different prices for a vehicle. Moreover, the overall accessibility of these systems may be very different depending on the user's location. Platforms and r...
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作者：Yang, Jingyuan; Xin, Linwei
作者单位：University of Chicago
摘要：One of the main results of Yu and Kong [Yu and Kong (2020) Robust contract designs: Linear contracts and moral hazard. Oper. Res. 68(5):1457-1473] is proposition 4, which states that the optimal robust contract with a piecewise linear concave agent utility only consists of progressive fixed payments and linear rewards with progressive commission rates. In this note, we present a proof-based counterexample to theoretically demonstrate the incorrectness of this result. We then perform a numerica...
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作者：El Housni, Omar; Goyal, Vineet; Hanguir, Oussama; Stein, Clifford
作者单位：Cornell University; Columbia University
摘要：Matching demand (riders) to supply (drivers) efficiently is a fundamental problem for ride-sharing platforms that need to match the riders (almost) as soon as the request arrives with only partial knowledge about future ride requests. A myopic approach that computes an optimal matching for current requests ignoring future uncertainty can be highly suboptimal. In this paper, we consider a two-stage robust optimization framework for this matching problem in which future demand uncertainty is mod...
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作者：Kannan, Rohit; Bayraksan, Guezin; Luedtke, James R.
作者单位：Virginia Polytechnic Institute & State University; University System of Ohio; Ohio State University; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
摘要：We study optimization for data-driven decision making when we have observations of the uncertain parameters within an optimization model together with concurrent observations of covariates. The goal is to choose a decision that minimizes the expected cost conditioned on a new covariate observation. We investigate two data-driven frameworks that integrate a machine learning prediction model within a stochastic programming sample average approximation (SAA) for approximating the solution to this...
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作者：Yang, Jincheng; Zhang, Luhao; Chen, Ningyuan; Gao, Rui; Hu, Ming
作者单位：Johns Hopkins University; University of Toronto; University Toronto Mississauga; University of Toronto; University of Texas System; University of Texas Austin
摘要：We consider stochastic optimization with side information where, prior to decision making, covariate data are available to inform better decisions. To hedge against data uncertainty while capturing the information structure revealed from the conditional distribution of random problem parameters given the covariate values, we propose a distributionally robust formulation based on causal transport distance. We derive a dual reformulation for evaluating the worst-case expected cost and show that ...
