Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/235530
Authors: 
Buchali, Katrin
Year of Publication: 
2021
Series/Report no.: 
Hohenheim Discussion Papers in Business, Economics and Social Sciences No. 02-2021
Abstract: 
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 sensitivities 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.
Subjects: 
pricing algorithm
reinforcement learning
Q-learning
price discrimi-nation
fairness
inequity
JEL: 
D63
D91
L12
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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