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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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Notes
Publication license
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
Supervisor
Edition / version
Citation
Identification
DOI
ISSN
ISBN
Language
English
Publisher
Publisher place
Classification (DDC)
330 Economics
Original object
Standardized keywords (GND)
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},
}