This repository contains the code for the paper Privacy Can Arise Endogenously in an Economic System with Learning Agents by Nivasini Ananthakrishnan, Tiffany Ding, Mariel Werner, Sai Praneeth Karimireddy, and Michael I. Jordan
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.
Simply run the following command to reproduce plots in the paper.
python3 run_dynamics.py