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ResearchPaper
2021

Price discrimination with inequity-averse consumers : a reinforcement learning approach

Abstract (English)

With the advent of big data, unique opportunities arise for data collection and analysis and thus for personalized pricing. We simulate a self-learning algorithm setting personalized prices based on additional information about consumer sensi- tivities in order to analyze market outcomes for consumers who have a preference for fair, equitable outcomes. For this purpose, we compare a situation that does not consider fairness to a situation in which we allow for inequity-averse consumers. We show that the algorithm learns to charge different, revenue-maximizing prices and simultaneously increase fairness in terms of a more homogeneous distribution of prices.

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Publication series

Hohenheim discussion papers in business, economics and social sciences; 2021,02

Published in

Faculty
Faculty of Business, Economics and Social Sciences
Institute
Institute of Economics

Examination date

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ISBN

Language
English

Publisher

Publisher place

Classification (DDC)
330 Economics

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BibTeX

@techreport{Buchali2021, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/6621}, author = {Buchali, Katrin}, title = {Price discrimination with inequity-averse consumers : a reinforcement learning approach}, year = {2021}, school = {Universität Hohenheim}, series = {Hohenheim discussion papers in business, economics and social sciences}, }