2026-08-16T05:16:07Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:20092 2024-06-10T05:20:53Z ddc:004 open_access

Privacy Can Arise Endogenously in an Economic System with Learning Agents Ananthakrishnan, Nivasini Ding, Tiffany Werner, Mariel Karimireddy, Sai Praneeth Jordan, Michael I. Privacy Game Theory Online Learning Price Discrimination We study price-discrimination games between buyers and a seller where privacy arises endogenously - that is, utility maximization yields equilibrium strategies where privacy occurs naturally. In this game, buyers with a high valuation for a good have an incentive to keep their valuation private, lest the seller charge them a higher price. This yields an equilibrium where some buyers will send a signal that misrepresents their type with some probability; we refer to this as buyer-induced privacy. When the seller is able to publicly commit to providing a certain privacy level, we find that their equilibrium response is to commit to ignore buyers' signals with some positive probability; we refer to this as seller-induced privacy. We then turn our attention to a repeated interaction setting where the game parameters are unknown and the seller cannot credibly commit to a level of seller-induced privacy. In this setting, players must learn strategies based on information revealed in past rounds. We find that, even without commitment ability, seller-induced privacy arises as a result of reputation building. We characterize the resulting seller-induced privacy and seller’s utility under no-regret and no-policy-regret learning algorithms and verify these results through simulations. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Nivasini Ananthakrishnan and Tiffany Ding and Mariel Werner and Sai Praneeth Karimireddy and Michael I. Jordan 2024 Is Part Of LIPIcs, Volume 295, 5th Symposium on Foundations of Responsible Computing (FORC 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.FORC.2024.9 urn:nbn:de:0030-drops-200921 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2024.9 eng https://creativecommons.org/licenses/by/4.0/legalcode