Download or read book Privacy Preserving Dynamic Personalized Pricing with Demand Learning written by Chen, Xi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Elements of Joint Learning and Optimization in Operations Management written by Xi Chen and published by Springer Nature. This book was released on 2022-09-20 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.

Download or read book Wisdom Well Being Win Win written by Isaac Sserwanga and published by Springer Nature. This book was released on 2024 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Three-volume set LNCS 14596, 14596 and 14598 constitutes the proceedings of the 19th International Conference on Wisdom, Well-Being, Win-Win, iConference 2024, which was hosted virtually by University of Tsukuba, Japan and in presence by Jilin University, Changchun, China, during April 15–26, 2024. The 36 full papers and 55 short papers are presented in these proceedings were carefully reviewed and selected from 218 submissions. The papers are organized in the following topical sections: Volume I: Archives and Information Sustainability; Behavioural Research; AI and Machine Learning; Information Science and Data Science; Information and Digital Literacy. Volume II: Digital Humanities; Intellectual Property Issues; Social Media and Digital Networks; Disinformation and Misinformation; Libraries, Bibliometrics and Metadata. Volume III: Knowledge Management; Information Science Education; Information Governance and Ethics; Health Informatics; Human-AI Collaboration; Information Retrieval; Community Informatics; Scholarly, Communication and Open Access. .

Download or read book A Primal dual Learning Algorithm for Personalized Dynamic Pricing with an Inventory Constraint written by Ningyuan Chen and published by . This book was released on 2020 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider the problem of a firm seeking to use personalized pricing to sell an exogenously given stock of a product over a finite selling horizon to different consumer types. We assume that the type of an arriving consumer can be observed but the demand function associated with each type is initially unknown. The firm sets personalized prices dynamically for each type and attempts to maximize the revenue over the season. We provide a learning algorithm that is near-optimal when the demand and capacity scale in proportion. The algorithm utilizes the primal-dual formulation of the problem and learns the dual optimal solution explicitly. It allows the algorithm to overcome the curse of dimensionality (the rate of regret is independent of the number of types) and sheds light on novel algorithmic designs for learning problems with resource constraints.

Download or read book Dynamic Pricing and Demand Learning with Limited Price Experimentation written by Wang Chi Cheung and published by . This book was released on 2017 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a dynamic pricing problem where the demand function is not known a priori, price experimentation can be used as a demand learning tool. Existing literature usually assumes no constraint on price changes, but in practice sellers often face business constraints that prevent them from conducting extensive experimentation. We consider a dynamic pricing model where the demand function is unknown but belongs to a known finite set. The seller is allowed to make at most m price changes during T periods. The objective is to minimize the worst case regret, i.e., the expected total revenue loss compared to a clairvoyant who knows the demand distribution in advance. We demonstrate a pricing policy that incurs a regret of O(log^(m) T), or m iterations of the logarithm. Furthermore, we describe an implementation at Groupon, a large e-commerce marketplace for daily deals. The field study shows significant impact on revenue and bookings.

Download or read book Dynamic Pricing with Demand Learning and Reference Effects written by Arnoud den Boer and published by . This book was released on 2020 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a seller's dynamic pricing problem with demand learning and reference effects. We first study the case where customers are loss-averse: they have a reference price that can vary over time, and the demand reduction when the selling price exceeds the reference price dominates the demand increase when the selling price falls behind the reference price by the same amount. Thus, the expected demand as a function of price has a time-varying "kink" and is not differentiable everywhere. The seller neither knows the underlying demand function nor observes the time-varying reference prices. In this setting, we design and analyze a policy that (i) changes the selling price very slowly to control the evolution of the reference price, and (ii) gradually accumulates sales data to balance the tradeoff between learning and earning. We prove that, under a variety of reference-price updating mechanisms, our policy is asymptotically optimal; i.e., its T-period revenue loss relative to a clairvoyant who knows the demand function and the reference-price updating mechanism grows at the smallest possible rate in T. We also extend our analysis to the case of a fixed reference price, and show how reference effects increase the complexity of dynamic pricing with demand learning in this case. Moreover, we study the case where customers are gain-seeking and design asymptotically optimal policies for this case. Finally, we design and analyze an asymptotically optimal statistical test for detecting whether customers are loss-averse or gain-seeking.

Download or read book Robust Dynamic Pricing with Demand Learning in the Presence of Outlier Customers written by Chen, Xi and published by . This book was released on 2020 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper studies the dynamic pricing problem under model mis-specifi cation settings. To characterize the model mis-specification, we extend the "eps-contamination model | the most fundamental model in robust statistics and machine learning, to the online setting. In particular, for a selling horizon of length T, the online "eps-contamination model assumes that the demands are realized according to a typical unknown demand function only for (1-eps)T periods. For the rest of eps T periods, an outlier purchase can happen with arbitrary demand functions. Under this model, we develop new robust dynamic pricing policies to hedge against outlier purchase behavior. For the dynamic pricing problem, there are two critical prices, the revenue-maximizing price and inventory clearance price, and the optimal price is the larger price. The challenge is that the seller has no information about which price is larger, and the revenues near these two prices behave entirely differently. To address this challenge, we propose robust online policies for both cases when the optimal price is the revenue-maximizing price and when the optimal price is the clearance price, and then develop a meta algorithm that combines these two cases. Our algorithm is a fully adaptive policy that does not require any prior knowledge of the outlier proportion parameter ". Our simulation study shows that our policy outperforms existing policies in the literature.

Download or read book Dynamic Pricing with Fairness Constraints written by Maxime C. Cohen and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Following the increasing popularity of personalized pricing, there is a growing concern from customers and policy makers regarding fairness considerations. This paper studies the problem of dynamic pricing with unknown demand under two types of fairness constraints: price fairness and demand fairness. For price fairness, the retailer is required to (i) set similar prices for different customer groups (called group fairness) and (ii) ensure that the prices over time for each customer group are relatively stable (called time fairness). We propose an algorithm based on an infrequently-changed upper-confidence-bound (UCB) method, which is proved to yield a near-optimal regret performance. In particular, we show that imposing group fairness does not affect the demand learning problem, in contrast to imposing time fairness. On the flip side, we show that imposing time fairness does not impact the clairvoyant optimal revenue, in contrast to imposing group fairness. For demand fairness, the retailer is required to satisfy that the resulting demand from different customer groups is relatively similar (e.g., the retailer offers a lower price to students to increase their demand to a similar level as non-students). In this case, we design an algorithm adapted from a primal-dual learning framework and prove that our algorithm also achieves a near-optimal performance.

Download or read book Personalized Dynamic Pricing with Machine Learning written by Gah-Yi Ban and published by . This book was released on 2020 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers' characteristics encoded as a d-dimensional feature vector. We assume a personalized demand model, parameters of which depend on s out of the d features. The seller initially does not know the relationship between the customer features and the product demand, but learns this through sales observations over a selling horizon of T periods. We prove that the seller's expected regret, i.e., the revenue loss against a clairvoyant who knows the underlying demand relationship, is at least of order s √T under any admissible policy. We then design a near-optimal pricing policy for a “semi-clairvoyant” seller (who knows which s of the d features are in the demand model) that achieves an expected regret of order s √Tlog T. We extend this policy to a more realistic setting where the seller does not know the true demand predictors, and show that this policy has an expected regret of order s √T(log d+logT), which is also near-optimal. Finally, we test our theory on simulated data and on a data set from an online auto loan company in the United States. On both data sets, our experimentation-based pricing policy is superior to intuitive and/or widely-practiced customized pricing methods such as myopic pricing and segment-then- optimize policies. Furthermore, our policy improves upon the loan company's historical pricing decisions by 47% in expected revenue over a six-month period.

Download or read book Dynamic Personalized Pricing with Active Consumers written by and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Offline Personalized Pricing with Censored Demand written by Zhengling Qi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study a personalized pricing problem with demand censoring in an offline data-driven setting. In this problem, a firm is endowed with a finite amount of inventory, and faces a random demand that is dependent on the offered price and the covariates (from products, customers, or both). Any unsatisfied demand that exceeds the inventory level is lost and unobservable. The firm does not know the demand function but has access to an offline dataset consisting of quadruplets of historical covariates, inventory, price, and potentially censored sales quantity. Our objective is to use the offline dataset to find the optimal personalized pricing rule so as to maximize the expected revenue. Through the lens of causal inference, we propose a novel data-driven algorithm that is motivated from survival analysis and doubly robust estimation. We derive a finite sample regret bound to justify the proposed offline learning algorithm and prove its robustness. Thorough numerical experiments demonstrate that our proposed algorithm performs robustly well in estimating the optimal prices on both training and testing datasets, demonstrating the value of factoring in demand censoring for personalized pricing.

Download or read book Local Electricity Markets written by Tiago Pinto and published by Academic Press. This book was released on 2021-07-03 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: Local Electricity Markets introduces the fundamental characteristics, needs, and constraints shaping the design and implementation of local electricity markets. It addresses current proposed local market models and lessons from their limited practical implementation. The work discusses relevant decision and informatics tools considered important in the implementation of local electricity markets. It also includes a review on management and trading platforms, including commercially available tools. Aspects of local electricity market infrastructure are identified and discussed, including physical and software infrastructure. It discusses the current regulatory frameworks available for local electricity market development internationally. The work concludes with a discussion of barriers and opportunities for local electricity markets in the future. - Delineates key components shaping the design and implementation of local electricity market structure - Provides a coherent view on the enabling infrastructures and technologies that underpin local market expansion - Explores the current regulatory environment for local electricity markets drawn from a global panel of contributors - Exposes future paths toward widespread implementation of local electricity markets using an empirical review of barriers and opportunities - Reviews relevant local electricity market case studies, pilots and demonstrators already deployed and under implementation

Download or read book Pricing and Revenue Optimization written by Robert Phillips and published by Stanford University Press. This book was released on 2005-08-05 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first comprehensive introduction to the concepts, theories, and applications of pricing and revenue optimization. From the initial success of "yield management" in the commercial airline industry down to more recent successes of markdown management and dynamic pricing, the application of mathematical analysis to optimize pricing has become increasingly important across many different industries. But, since pricing and revenue optimization has involved the use of sophisticated mathematical techniques, the topic has remained largely inaccessible to students and the typical manager. With methods proven in the MBA courses taught by the author at Columbia and Stanford Business Schools, this book presents the basic concepts of pricing and revenue optimization in a form accessible to MBA students, MS students, and advanced undergraduates. In addition, managers will find the practical approach to the issue of pricing and revenue optimization invaluable. Solutions to the end-of-chapter exercises are available to instructors who are using this book in their courses. For access to the solutions manual, please contact [email protected].

Download or read book Federated Learning written by Qiang Yang and published by Springer Nature. This book was released on 2020-11-25 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”

Download or read book Vehicular Social Networks written by Anna Maria Vegni and published by CRC Press. This book was released on 2017-03-31 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides a comprehensive guide to vehicular social networks. The book focuses on a new class of mobile ad hoc networks that exploits social aspects applied to vehicular environments. Selected topics are related to social networking techniques, social-based routing techniques applied to vehicular networks, data dissemination in VSNs, architectures for VSNs, and novel trends and challenges in VSNs. It provides significant technical and practical insights in different aspects from a basic background on social networking, the inter-related technologies and applications to vehicular ad-hoc networks, the technical challenges, implementation and future trends.

Download or read book The Antitrust Paradox written by Robert Bork and published by . This book was released on 2021-02-22 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most important book on antitrust ever written. It shows how antitrust suits adversely affect the consumer by encouraging a costly form of protection for inefficient and uncompetitive small businesses.

Download or read book Multi dimensional Urban Sensing Using Crowdsensing Data written by Chaocan Xiang and published by Springer Nature. This book was released on 2023-03-23 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chaocan Xiang is an Associate Professor at the College of Computer Science, Chongqing University, China. He received his bachelor’s degree and Ph.D. from Nanjing Institute of Communication Engineering, China, in 2009 and 2014, respectively. He subsequently studied at the University of Michigan-Ann Arbor in 2017 (supervised by Prof. Kang G. Shin, IEEE Life Fellow, ACM Fellow). His research interests mainly include UAVs/vehicle-based crowdsensing, urban computing, Internet of Things, Artificial Intelligence, and big data. He has published more than 50 papers, including over 20 in leading venues such as IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE INFOCOM, and ACM Ubicomp. He has received a best paper award and a best poster award at two international conferences. Panlong Yang is a full Professor at the University of Science and Technology of China. He has been supported by the NSF Jiangsu through a Distinguished Young Scholarship and was honored as a CCF Distinguished Lecturer in 2015. He has published over 150 papers, including 40 in CCF Class A. Since 2012, he has supervised 14 master’s and Ph.D. candidates, including two excellent dissertation winners in Jiangsu Province and the PLA education system. He has been supported by the National Key Development Project and NSFC projects. He has nominated by ACM MobiCom 2009 for the best demo honored mention awards, and won best paper awards at the IEEE MSN and MASS. He has served as general chair of BigCom and TPC chair of IEEE MSN. In addition, he has served as a TPC member of INFOCOM (CCF Class A) and an associate editor of the Journal of Communication of China. He is a Senior Member of the IEEE (2019). Fu Xiao received his Ph.D. in Computer Science and Technology from the Nanjing University of Science and Technology, Nanjing, China, in 2007. He is currently a Professor and Dean of the School of Computer, Nanjing University of Posts and Telecommunications. He has authored more than 60 papers in respected conference proceedings and journals, including IEEE INFOCOM, ACM Mobihoc, IEEE JASC, IEEE/ACM ToN, IEEE TPDS, IEEE TMC, etc. His main research interest is in the Internet of Things. He is a member of the IEEE Computer Society and the Association for Computing Machinery. Xiaochen Fan received his B.S. degree in Computer Science from Beijing Institute of Technology, Beijing, China, in 2013, and his Ph.D. from the University of Technology Sydney, NSW, Australia, in 2021. His research interests include mobile/pervasive computing, deep learning, and Internet of Things (IoT). He has published over 25 peer-reviewed papers in high-quality journals and IEEE/ACM international conference proceedings.