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ResearchPaper
2021
Price discrimination with inequity-averse consumers : a reinforcement learning approach
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
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Faculty
Faculty of Business, Economics and Social Sciences
Institute
Institute of Economics
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Language
English
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Classification (DDC)
330 Economics