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Differentiable Economics for Randomized Affine Maximizer Auctions

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  • Michael Curry
  • Tuomas Sandholm
  • John Dickerson

Abstract

A recent approach to automated mechanism design, differentiable economics, represents auctions by rich function approximators and optimizes their performance by gradient descent. The ideal auction architecture for differentiable economics would be perfectly strategyproof, support multiple bidders and items, and be rich enough to represent the optimal (i.e. revenue-maximizing) mechanism. So far, such an architecture does not exist. There are single-bidder approaches (MenuNet, RochetNet) which are always strategyproof and can represent optimal mechanisms. RegretNet is multi-bidder and can approximate any mechanism, but is only approximately strategyproof. We present an architecture that supports multiple bidders and is perfectly strategyproof, but cannot necessarily represent the optimal mechanism. This architecture is the classic affine maximizer auction (AMA), modified to offer lotteries. By using the gradient-based optimization tools of differentiable economics, we can now train lottery AMAs, competing with or outperforming prior approaches in revenue.

Suggested Citation

  • Michael Curry & Tuomas Sandholm & John Dickerson, 2022. "Differentiable Economics for Randomized Affine Maximizer Auctions," Papers 2202.02872, arXiv.org.
    • Handle: RePEc:arx:papers:2202.02872
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    References listed on IDEAS

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    Cited by:

    1. Andreas A. Haupt & Phillip J. K. Christoffersen & Mehul Damani & Dylan Hadfield-Menell, 2022. "Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL," Papers 2208.10469, arXiv.org, revised Jan 2024.

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