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Optimal Multi-Attribute Auctions Based on Multi-Scale Loss Network

Author

Listed:
  • Zefeng Zhao

    (Faculty of Data Science, City University of Macau, Macau 999078, China)

  • Haohao Cai

    (Faculty of Data Science, City University of Macau, Macau 999078, China)

  • Huawei Ma

    (Institute of AI and Blockchain, Guangzhou University, Guangzhou 510006, China)

  • Shujie Zou

    (Faculty of Data Science, City University of Macau, Macau 999078, China)

  • Chiawei Chu

    (Faculty of Data Science, City University of Macau, Macau 999078, China)

Abstract

There is a strong demand for multi-attribute auctions in real-world scenarios for non-price attributes that allow participants to express their preferences and the item’s value. However, this also makes it difficult to perform calculations with incomplete information, as a single attribute—price—no longer determines the revenue. At the same time, the mechanism must satisfy individual rationality (IR) and incentive compatibility (IC). This paper proposes an innovative dual network to solve these problems. A shared MLP module is constructed to extract bidder features, and multiple-scale loss is used to determine network status and update. The method was tested on real and extended cases, showing that the approach effectively improves the auctioneer’s revenue without compromising the bidder.

Suggested Citation

  • Zefeng Zhao & Haohao Cai & Huawei Ma & Shujie Zou & Chiawei Chu, 2023. "Optimal Multi-Attribute Auctions Based on Multi-Scale Loss Network," Mathematics, MDPI, vol. 11(14), pages 1-11, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3240-:d:1200822
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    References listed on IDEAS

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