Artificial Intelligence and Auction Design

TL;DR

This paper investigates how simple AI algorithms affect auction outcomes, revealing that first-price auctions tend to tacit collusion without feedback, while second-price auctions remain competitive, highlighting the importance of information provision.

Contribution

The study demonstrates the impact of auction format and feedback on AI-driven bidding behavior, showing how information sharing can enhance competition.

Findings

First-price auctions with no feedback lead to collusion.

Second-price auctions maintain competitiveness.

Providing bid information increases auction competitiveness.

Abstract

Motivated by online advertising auctions, we study auction design in repeated auctions played by simple Artificial Intelligence algorithms (Q-learning). We find that first-price auctions with no additional feedback lead to tacit-collusive outcomes (bids lower than values), while second-price auctions do not. We show that the difference is driven by the incentive in first-price auctions to outbid opponents by just one bid increment. This facilitates re-coordination on low bids after a phase of experimentation. We also show that providing information about lowest bid to win, as introduced by Google at the time of switch to first-price auctions, increases competitiveness of auctions.