Arbeitspapier
Price discrimination with inequity-averse consumers: A reinforcement learning approach
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Hohenheim Discussion Papers in Business, Economics and Social Sciences ; No. 02-2021
- Klassifikation
-
Wirtschaft
Equity, Justice, Inequality, and Other Normative Criteria and Measurement
Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making‡
Monopoly; Monopolization Strategies
- Thema
-
pricing algorithm
reinforcement learning
Q-learning
price discrimi-nation
fairness
inequity
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Buchali, Katrin
- Ereignis
-
Veröffentlichung
- (wer)
-
Universität Hohenheim, Fakultät Wirtschafts- und Sozialwissenschaften
- (wo)
-
Stuttgart
- (wann)
-
2021
- Handle
- URN
-
urn:nbn:de:bsz:100-opus-19059
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Buchali, Katrin
- Universität Hohenheim, Fakultät Wirtschafts- und Sozialwissenschaften
Entstanden
- 2021