Individual Fairness in Feature-Based Pricing for Monopoly Markets
Paper i proceeding, 2022

We study fairness in the context of feature-based price discrimination in monopoly markets. We propose a new notion of individual fairness, namely, \alpha-fairness, which guarantees that individuals with similar features face similar prices. First, we study discrete valuation space and give an analytical solution for optimal fair feature-based pricing. We show that the cost of fair pricing is defined as the ratio of expected revenue in an optimal feature-based pricing to the expected revenue in an optimal fair feature-based pricing (CoF) can be arbitrarily large in general. When the revenue function is continuous and concave with respect to the prices, we show that one can achieve CoF strictly less than 2, irrespective of the model parameters. Finally, we provide an algorithm to compute fair feature-based pricing strategy that achieves this CoF.

Författare

Shantanu Das

International Institute of Information Technology

Swapnil Vilas Dhamal

Institut Polytechnique de Paris

Ganesh Ghalme

Indian Institute of Technology

Shweta Jain

Indian Institute of Technology

Sujit Gujar

International Institute of Information Technology

UAI 2022 - Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence

Vol. PMLR 180 486-795

38th Conference on Uncertainty in Artificial Intelligence, UAI 2022
Eindhoven, Netherlands,

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Nationalekonomi

Datavetenskap (datalogi)

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Senast uppdaterat

2024-05-29