Price discrimination with inequity-averse consumers : a reinforcement learning approach

publ-mit-podpubl-mit-podBuchali, Katrin2024-04-082024-04-082021-06-232021https://hohpublica.uni-hohenheim.de/handle/123456789/6621With 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.engPricing algorithmReinforcement learningQ-learningPrice discriminationFairnessInequity330PreisdiskriminierungAlgorithmusPrice discrimination with inequity-averse consumers : a reinforcement learning approachWorkingPaper1761104497urn:nbn:de:bsz:100-opus-19059