The Role of Prescreening in Auctions with Predictions
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914000219930624 |
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| author | Sun, Yanwei Sun, Fupeng Yan, Chiwei Wu, Jiahua |
| author_facet | Sun, Yanwei Sun, Fupeng Yan, Chiwei Wu, Jiahua |
| contents | Sellers often prescreen potential bidders, restricting participation to a select group of capable participants. Recent advances in machine learning and generative AI make this strategy increasingly viable by enabling the cost-effective identification of high-quality bidders. However, the practice departs from classic auction theory, which usually favors broad competition over selective exclusion. In this paper, we examine whether and under what conditions bidder prescreening can be justified. We analyze a setting in which bidders have independent and identically distributed private valuations, and the seller observes noisy signals generated by a valuation predictor. The seller determines how many top bidders to admit and, after receiving signals, selects exactly that many with the highest signal-based rankings. We demonstrate that an auction with prescreening is equivalent to a standard auction (i.e., without prescreening) but with correlated valuations. Our analysis shows that, although admitting fewer bidders leads to revenue losses in both second-price and first-price auctions, a more accurate predictor can mitigate or even fully offset these losses. In contrast, prescreening can significantly boost revenue in all-pay auctions; notably, when the predictor is perfect, admitting only two bidders is optimal. All results remain valid in the presence of reserve prices. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_12117 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Role of Prescreening in Auctions with Predictions Sun, Yanwei Sun, Fupeng Yan, Chiwei Wu, Jiahua Computer Science and Game Theory Sellers often prescreen potential bidders, restricting participation to a select group of capable participants. Recent advances in machine learning and generative AI make this strategy increasingly viable by enabling the cost-effective identification of high-quality bidders. However, the practice departs from classic auction theory, which usually favors broad competition over selective exclusion. In this paper, we examine whether and under what conditions bidder prescreening can be justified. We analyze a setting in which bidders have independent and identically distributed private valuations, and the seller observes noisy signals generated by a valuation predictor. The seller determines how many top bidders to admit and, after receiving signals, selects exactly that many with the highest signal-based rankings. We demonstrate that an auction with prescreening is equivalent to a standard auction (i.e., without prescreening) but with correlated valuations. Our analysis shows that, although admitting fewer bidders leads to revenue losses in both second-price and first-price auctions, a more accurate predictor can mitigate or even fully offset these losses. In contrast, prescreening can significantly boost revenue in all-pay auctions; notably, when the predictor is perfect, admitting only two bidders is optimal. All results remain valid in the presence of reserve prices. |
| title | The Role of Prescreening in Auctions with Predictions |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2502.12117 |