Reproduce “Auctions Between Regret-Minimizing Agents” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Yoav Kolumbus, Noam Nisan
Researchers at The Hebrew University of Jerusalem investigated how regret-minimizing software agents influence repeated first-price, second-price, and generalized first-price auctions. They found that second-price auctions lose their incentive compatibility, prompting users to misreport valuations, while generalized first-price auctions exhibited severe implicit collusion among agents, allowing users to capture approximately 89% of the total welfare.
Key insights
- Auction mechanisms designed for human bidders may lose their incentive-compatible properties when regret-minimizing agents are introduced, fundamentally altering strategic user incentives.
- The concept of 'co-undominated CCE' offers a more predictive analytical tool for the long-term behavior of mean-based regret-minimizing algorithms than general CCE analysis.
- In second-price auctions, users have an incentive to misreport (exaggerate) their valuations to their agents, leading to lower average prices paid and significantly reduced auctioneer revenue.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.