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

Dieses Objekt wird bereitgestellt von:
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

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