Your Browsing History May Cost You: A Framework for Discovering Differential Pricing in Non-Transparent Markets

Aditya Karan, Naina Balepur, Hari Sundaram

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In many online markets we "shop alone"- there is no way for us to know the prices other consumers paid for the same goods. Could this lack of price transparency lead to differential pricing? To answer this question, we present a generalized framework to audit online markets for differential pricing using automated agents. Consensus is a key idea in our work: for a successful black-box audit, both the experimenter and seller must agree on the agents' attributes. We audit two competitive online travel markets on kayak.com (flight and hotel markets) and construct queries representative of the demand for goods. Crucially, we assume ignorance of the sellers' pricing mechanisms while conducting these audits. We conservatively implement consensus with nine distinct profiles based on behavior, not demographics. We use a structural causal model for price differences and estimate model parameters using Bayesian inference. We can unambiguously show that many sellers (but not all) demonstrate behavior-driven differential pricing. In the flight market, some profiles are nearly more likely to see a worse price than the best performing profile, and nearly more likely in the hotel market. While the control profile (with no browsing history) was on average offered the best prices in the flight market, surprisingly, other profiles outperformed the control in the hotel market. The price difference between any pair of profiles occurring by chance is $ 0.44 in the flight market and $ 0.09 for hotels. However, the expected loss of welfare for any profile when compared to the best profile can be as much as $ 6.00 for flights and $ 3.00 for hotels (i.e., 15 × and 33 × the price difference by chance respectively). This illustrates the need for new market designs or policies that encourage more transparent market design to overcome differential pricing practices.

Original languageEnglish (US)
Title of host publicationProceedings of the 6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023
PublisherAssociation for Computing Machinery
Pages717-735
Number of pages19
ISBN (Electronic)9781450372527
DOIs
StatePublished - Jun 12 2023
Externally publishedYes
Event6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023 - Chicago, United States
Duration: Jun 12 2023Jun 15 2023

Publication series

NameACM International Conference Proceeding Series

Conference

Conference6th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023
Country/TerritoryUnited States
CityChicago
Period6/12/236/15/23

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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