Algorithmic Collusion in Auctions: Evidence from Controlled Laboratory Experiments

Abstract

Algorithms are increasingly being used to automate participation in online markets. Banchio and Skrzypacz (2022) demonstrate how exploration under identical valuation in first-price auctions may lead to spontaneous coupling into sub-competitive bidding. However, it is an open question if these findings extend to affiliated values, optimal exploration, and specifically which algorithmic details play a role in facilitating algorithmic collusion. This paper contributes to the literature by generating robust stylized facts to cover these gaps. I conduct a set of fully randomized experiments in a controlled laboratory setup and apply double machine learning to estimate granular conditional treatment effects of auction design on seller revenues. I find that first-price auctions lead to lower seller revenues and higher seller regret under identical values, affiliated values, and under both Q-learning and Bandits. There is more possibility of such tacit collusion under fewer bidders, Boltzmann exploration, asynchronous updating, and longer episodes; while high reserve prices can offset this. This evidence suggests that programmatic auctions, e.g. the Google Ad Exchange, which depend on first-price auctions, might be susceptible to coordinated bid suppression and significant revenue losses.

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