Price Discrimination with Inequity-Averse Consumers: A Reinforcement Learning Approach

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. 

Zitieren

@article{buchali2021, title={Price Discrimination with Inequity-Averse Consumers: A Reinforcement Learning Approach}, author={Buchali, Katrin}, year={2021}, }

Beteiligte Personen

• M. Sc. Katrin Buchali