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Author: Maxime C. Cohen
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Maxime C. Cohen
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Matthew Selove
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Although some firms use dynamic pricing to respond to demand fluctuations, other firms claim that fairness concerns prevent them from raising prices during periods when demand exceeds capacity. This paper explores conditions in which fairness concerns can or cannot cause shortages. In our model, a firm announces a price policy that states its prices during high and low demand, and customers must travel to a venue to learn the current price. We show that the interaction of fairness concerns with travel costs can cause the firm to set stable prices, which leads to shortages during high demand. However, if the firm is able to inform customers about the current price before they incur any travel costs, then dynamic pricing with no shortages is optimal even with strong fairness concerns.
Author: Luisa Domanska
Publisher: GRIN Verlag
ISBN: 3668863830
Category : Business & Economics
Languages : en
Pages : 105
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Book Description
Master's Thesis from the year 2018 in the subject Business economics - Business Management, Corporate Governance, grade: 1,7, Cologne Business School Köln, language: English, abstract: The thesis describes the conflict of price differentiation strategies and the consumers' perception of price fairness in different manners. By that, the role of a price is explained in detail in the role of marketing, as well as it's role in behavioral economics regarding price fairness. Different examples, being a highly discussed topic in the current state of the art, are brought together to demonstrate the variety of this price policy. Uber, the haircutting industry as well as typical gender-based pricing or emergency situations represent crucial practices that are discussed in regard of moral and ethical limitations in terms of price fairness. Finally, the work aims to understand the limitations on price differentiation in the context of price fairness and tries to answer questions as: When is price differentiation acceptable for consumers and when should firms reconsider differential price strategies due to a perceived abuse of trust, anger or similar emotions of targeted customers? For that, an empirical social research is considered to bring first and secondary data together, and to enable the defintion of those limitations to become a little clearer.
Author: Robert Phillips
Publisher: Stanford University Press
ISBN: 0804781648
Category : Business & Economics
Languages : en
Pages : 470
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Book Description
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].
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 76
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Book Description
Author: Animesh Mukherjee
Publisher: Springer Nature
ISBN: 9819971845
Category : Technology & Engineering
Languages : en
Pages : 150
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Book Description
This book is a collection of chapters in the newly developing area of ethics in artificial intelligence. The book comprises chapters written by leading experts in this area which makes it a one of its kind collections. Some key features of the book are its unique combination of chapters on both theoretical and practical aspects of integrating ethics into artificial intelligence. The book touches upon all the important concepts in this area including bias, discrimination, fairness, and interpretability. Integral components can be broadly divided into two segments – the first segment includes empirical identification of biases, discrimination, and the ethical concerns thereof in impact assessment, advertising and personalization, computational social science, and information retrieval. The second segment includes operationalizing the notions of fairness, identifying the importance of fairness in allocation, clustering and time series problems, and applications of fairness in software testing/debugging and in multi stakeholder platforms. This segment ends with a chapter on interpretability of machine learning models which is another very important and emerging topic in this area.
Author: Guillermo Gallego
Publisher: Springer
ISBN: 1493996061
Category : Business & Economics
Languages : en
Pages : 336
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Book Description
“There is no strategic investment that has a higher return than investing in good pricing, and the text by Gallego and Topaloghu provides the best technical treatment of pricing strategy and tactics available.” Preston McAfee, the J. Stanley Johnson Professor, California Institute of Technology and Chief Economist and Corp VP, Microsoft. “The book by Gallego and Topaloglu provides a fresh, up-to-date and in depth treatment of revenue management and pricing. It fills an important gap as it covers not only traditional revenue management topics also new and important topics such as revenue management under customer choice as well as pricing under competition and online learning. The book can be used for different audiences that range from advanced undergraduate students to masters and PhD students. It provides an in-depth treatment covering recent state of the art topics in an interesting and innovative way. I highly recommend it." Professor Georgia Perakis, the William F. Pounds Professor of Operations Research and Operations Management at the Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts. “This book is an important and timely addition to the pricing analytics literature by two authors who have made major contributions to the field. It covers traditional revenue management as well as assortment optimization and dynamic pricing. The comprehensive treatment of choice models in each application is particularly welcome. It is mathematically rigorous but accessible to students at the advanced undergraduate or graduate levels with a rich set of exercises at the end of each chapter. This book is highly recommended for Masters or PhD level courses on the topic and is a necessity for researchers with an interest in the field.” Robert L. Phillips, Director of Pricing Research at Amazon “At last, a serious and comprehensive treatment of modern revenue management and assortment optimization integrated with choice modeling. In this book, Gallego and Topaloglu provide the underlying model derivations together with a wide range of applications and examples; all of these facets will better equip students for handling real-world problems. For mathematically inclined researchers and practitioners, it will doubtless prove to be thought-provoking and an invaluable reference.” Richard Ratliff, Research Scientist at Sabre “This book, written by two of the leading researchers in the area, brings together in one place most of the recent research on revenue management and pricing analytics. New industries (ride sharing, cloud computing, restaurants) and new developments in the airline and hotel industries make this book very timely and relevant, and will serve as a critical reference for researchers.” Professor Kalyan Talluri, the Munjal Chair in Global Business and Operations, Imperial College, London, UK.
Author: Takanori Adachi
Publisher: Springer Nature
ISBN: 981993205X
Category : Business & Economics
Languages : en
Pages : 85
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Book Description
This book provides an updated overview of the recent progress in the theoretical study of third-degree price discrimination. It is a marketing tactic and is said to be present if the unit price is different across different groups of buyers. Its welfare evaluation is often difficult because it entails two countervailing effects: on one hand, it exploits surplus from consumers who have high willingness-to-pay, but on the other hand, it generates gains from trade from consumers who otherwise would not purchase the good. Recognizing this difficulty, we provide new insights on evaluation of third-degree price discrimination in consideration of network effects and vertical product differentiation. Our analysis is particularly useful for the industries related to information and communication technologies (ICT) because these two elements characterize them. Furthermore, we also study the welfare effects of third-degree price discrimination under imperfect competition other than monopoly. At first, it seems that it may complicate the analysis under monopoly. However, we argue that the main thrusts of analysis under monopoly carry over to the case of oligopoly. We also take into account behavioral aspects and their implications for studying third-degree price discrimination. Overall, this book is designed to provide implications for contemporary management and policy issues by advancing theoretical issues in industrial organization.
Author: Norman J. Finkel
Publisher: Amer Psychological Assn
ISBN: 9781557987525
Category : Philosophy
Languages : en
Pages : 335
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Book Description
Annotation In his systematic analysis of "fairness" and "unfairness," Finkel (psychology, Georgetown U.) discusses how claims of unfair treatment not only inform our judicial system but are implicit in news reports and everyday conversation. As familiar as the concepts are, however, many people confuse "fairness" with "justice" and are clearer about what's "unfair" than what's "fair." By looking at the deeper meanings underlying "unfairness narratives" volunteered by American and international study participants, Finkel creates a typology of basic unfairness categories. He explores unfairness in broad historic, religious, legal, and psychological contexts and shows how age, sex, and culture are likely to play a part in how people perceive unfairness. Annotation c. Book News, Inc., Portland, OR (booknews.com).
Author: Wang Chi Cheung
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
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Book Description
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.
