Do Not Trust The Auctioneer: Learning to Bid in Feedback-Manipulated Auctions
Shilling is the use of artificial bids to make competition appear stronger and push prices upward. We study repeated first-price auctions in which shilling affects feedback but not allocation: the learner wins or loses against the real competing bid, but after a loss observes the maximum of the real bid and an independent shill bid. Thus the manipulation changes what the learner observes and hence how it learns to bid, without changing the outcome of the current auction. We analyze regret with respect to the best bid benchmark, assuming that the shill-bid distribution is known. Even then, shilling can mask the real bid, while useful side information appears only through intermittent low-s...
| Authors | Luigi Foscari, Matilde Tullii, Vianney Perchet |
|---|---|
| Primary category | stat.ML |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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