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| SWD-Schlagwörter: |
| Preisdiskriminierung , Algorithmus |
| Freie Schlagwörter (Englisch): |
| pricing algorithm , reinforcement learning , Q-learning , price discrimination , fairness , inequity |
| Institut: |
| Institut für Volkswirtschaftslehre |
| DDC-Sachgruppe: |
| Wirtschaft |
| Dokumentart: |
| ResearchPaper |
| Schriftenreihe: |
| Hohenheim discussion papers in business, economics and social sciences |
| Bandnummer: |
| 2021,02 |
| Sprache: |
| Englisch |
| Erstellungsjahr: |
| 2021 |
| Publikationsdatum: |
| 23.06.2021 |
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Veröffentlichungsvertrag mit der Universitätsbibliothek Hohenheim
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| Kurzfassung auf Englisch: |
| 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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