RT Generic
T1 Price discrimination with inequity-averse consumers : a reinforcement learning approach
A1 Buchali,Katrin
WP 2021/06/23
AB 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.
K1 Preisdiskriminierung
K1 Algorithmus
PP Hohenheim
PB Kommunikations-, Informations- und Medienzentrum der Universität Hohenheim
UL http://opus.uni-hohenheim.de/volltexte/2021/1905