6期

最新文章

• Pricing and Positioning of Horizontally Differentiated Products with Incomplete Demand Information AI翻译
  作者：den Boer, Arnoud V.; Chen, Boxiao; Wang, Yining
  作者单位：University of Amsterdam; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of Texas System; University of Texas Dallas
  摘要：We consider the problem of determining the optimal prices and product configurations of horizontally differentiated products when customers purchase according to a locational (Hotelling) choice model and where the problem parameters are initially unknown to the decision maker. Both for the single-product and multiple-product setting, we propose a data-driven algorithm that learns the optimal prices and product configurations from accumulating sales data, and we show that their regret-the expec...
• Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games AI翻译
  作者：Yan, Yuling; Li, Gen; Chen, Yuxin; Fan, Jianqing
  作者单位：Massachusetts Institute of Technology (MIT); Chinese University of Hong Kong; University of Pennsylvania; Princeton University
  摘要：This paper makes progress toward learning Nash equilibria in two-player, zero-sum Markov games from offline data. Specifically, consider a gamma-discounted, infinite-horizon Markov game with S states, in which the max-player has A actions and the min-player has B actions. We propose a pessimistic model-based algorithm with Bernstein-style lower confidence bounds-called the value iteration with lower confidence bounds for zero-sum Markov games-that provably finds an epsilon-approximate Nash equ...
• Optimal Auction Design with Deferred Inspection and Reward AI翻译
  作者：Alaei, Saeed; Belloni, Alexandre; Makhdoumi, Ali; Malekian, Azarakhsh
  作者单位：Alphabet Inc.; Google Incorporated; Duke University; Amazon.com; University of Toronto
  摘要：Consider a mechanism run by an auctioneer who can use both payment and inspection instruments to incentivize agents. The timeline of the events is as follows. Based on a prespecified allocation rule and the reported values of agents, the auctioneer allocates the item and secures the reported values as deposits. The auctioneer then inspects the values of agents and, using a prespecified reward rule, rewards the ones who have reported truthfully. Using techniques from convex analysis and calculu...
• To Interfere or Not To Interfere: Information Revelation and Price-Setting Incentives in a Multiagent Learning Environment AI翻译
  作者：Birge, John R.; Chen, Hongfan (Kevin); Keskin, N. Bora; Ward, Amy
  作者单位：University of Chicago; Chinese University of Hong Kong; Duke University
  摘要：We consider a platform in which multiple sellers offer their products for sale over a time horizon of T periods. Each seller sets its own price. The platform collects a fraction of the sales revenue and provides price-setting incentives to the sellers to maximize its own revenue. The demand for each seller's product is a function of all sellers' prices and some customer features. Initially, neither the platform nor the sellers know the demand function, but they can learn about it through sales...
• Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing AI翻译
  作者：Bansak, Kirk; Paulson, Elisabeth
  作者单位：University of California System; University of California Berkeley; Harvard University
  摘要：This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multiyear randomized control trial in Switzerland, seeks to maximize the average predicted employment level (or any measured outcome of interest) of refugees through a minimum-discord online assignment algorithm. The performance of this algorithm is tested on real refugee resettlement data from both the United State...
• 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...
• Conditional Distributionally Robust Functionals AI翻译
  作者：Shapiro, Alexander; Pichler, Alois
  作者单位：University System of Georgia; Georgia Institute of Technology
  摘要：Many decisions, in particular decisions in a managerial context, are subject to uncertainty. Risk measures cope with uncertainty by involving more than one candidate probability. The corresponding risk averse decision takes all potential candidate probabilities into account and is robust with respect to all potential probabilities. This paper considers conditional robust decision making, where decisions are subject to additional prior knowledge or information. The literature discusses various ...
• Stochastic Approximation of Symmetric Nash Equilibria in Queueing Games AI翻译
  作者：Ravner, Liron; Snitkovsky, Ran I.
  作者单位：University of Haifa; Tel Aviv University
  摘要：We suggest a novel stochastic-approximation algorithm to compute a symmetric Nash-equilibrium strategy in a general queueing game with a finite action space. The algorithm involves a single simulation of the queueing process with dynamic updating of the strategy at regeneration times. Under mild assumptions on the utility function and on the regenerative structure of the queueing process, the algorithm converges to a symmetric equilibrium strategy almost surely. This yields a powerful tool tha...
• Dynamic Pricing with Unknown Nonparametric Demand and Limited Price Changes AI翻译
  作者：Perakis, Georgia; Singhvi, Divya
  作者单位：Massachusetts Institute of Technology (MIT); New York University
  摘要：We consider the dynamic pricing problem of a retailer who does not have any information on the underlying demand for a product. The retailer aims to maximize cumulative revenue collected over a finite time horizon by balancing two objectives: learning demand and maximizing revenue. The retailer also seeks to reduce the amount of price experimentation because of the potential costs associated with price changes. Existing literature solves this problem in the case where the unknown demand is par...
• Assortment Optimization Under the Multi-Purchase Multinomial Logit Choice Model AI翻译
  作者：Bai, Yicheng; Feldman, Jacob; Segev, Danny; Topaloglu, Huseyin; Wagner, Laura
  作者单位：Washington University (WUSTL); Tel Aviv University; Universidade Catolica Portuguesa
  摘要：In this paper, we introduce the Multi-Purchase Multinomial Logit choice model, which extends the random utility maximization framework of the classical Multinomial Logit model to a multiple-purchase setting. In this model, customers sample random utilities for each offered product as in the Multinomial Logit model. However, rather than focusing on a single product, they concurrently sample a budget parameter M , which indicates the maximum number of products that the customer is willing to pur...