A Context-Integrated Transformer-Based Neural Network for Auction Design

• URL: http://arxiv.org/abs/2201.12489v1
• Date: Sat, 29 Jan 2022 03:47:00 GMT
• Title: A Context-Integrated Transformer-Based Neural Network for Auction Design
• Authors: Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng, Xiang Yan, Manzil Zaheer, Xiaotie Deng
• Abstract summary: One of the central problems in auction design is developing an incentive-compatible mechanism that maximizes the auctioneer's expected revenue. We propose $mathttCITransNet$, a context-integrated transformer-based neural network for optimal auction design. We show by extensive experiments that $mathttCITransNet$ can recover the known optimal solutions in single-item settings, outperform strong baselines in multi-item auctions, and generalize well to cases other than those in training.
• Score: 25.763612577196124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the central problems in auction design is developing an incentive-compatible mechanism that maximizes the auctioneer's expected revenue. While theoretical approaches have encountered bottlenecks in multi-item auctions, recently, there has been much progress on finding the optimal mechanism through deep learning. However, these works either focus on a fixed set of bidders and items, or restrict the auction to be symmetric. In this work, we overcome such limitations by factoring \emph{public} contextual information of bidders and items into the auction learning framework. We propose $\mathtt{CITransNet}$, a context-integrated transformer-based neural network for optimal auction design, which maintains permutation-equivariance over bids and contexts while being able to find asymmetric solutions. We show by extensive experiments that $\mathtt{CITransNet}$ can recover the known optimal solutions in single-item settings, outperform strong baselines in multi-item auctions, and generalize well to cases other than those in training.

Related papers

• Selling Joint Ads: A Regret Minimization Perspective [7.288063443108292]
  Motivated by online retail, we consider the problem of selling one item (e.g., an ad slot) to two non-excludable buyers (say, a merchant and a brand) This problem captures, for example, situations where a merchant and a brand bid cooperatively in an auction to advertise a product, and both benefit from the ad being shown. A mechanism collects bids from the two and decides whether to allocate and which payments the two parties should make.
  arXiv  Detail & Related papers  (2024-09-12T07:59:10Z)
• Understanding Iterative Combinatorial Auction Designs via Multi-Agent Reinforcement Learning [10.41350502488723]
  We investigate whether multi-agent reinforcement learning algorithms can be used to understand iterative auctions. We find that MARL can indeed benefit auction analysis, but that deploying it effectively is nontrivial. We illustrate the promise of our resulting approach by using it to evaluate a specific rule change to a clock auction.
  arXiv  Detail & Related papers  (2024-02-29T18:16:13Z)
• Refined Mechanism Design for Approximately Structured Priors via Active Regression [50.71772232237571]
  We consider the problem of a revenue-maximizing seller with a large number of items for sale to $n$ strategic bidders. It is well-known that optimal and even approximately-optimal mechanisms for this setting are notoriously difficult to characterize or compute.
  arXiv  Detail & Related papers  (2023-10-11T20:34:17Z)
• Benefits of Permutation-Equivariance in Auction Mechanisms [90.42990121652956]
  An auction mechanism that maximizes the auctioneer's revenue while minimizes bidders' ex-post regret is an important yet intricate problem in economics. Remarkable progress has been achieved through learning the optimal auction mechanism by neural networks.
  arXiv  Detail & Related papers  (2022-10-11T16:13:25Z)
• Learning Revenue-Maximizing Auctions With Differentiable Matching [50.62088223117716]
  We propose a new architecture to approximately learn incentive compatible, revenue-maximizing auctions from sampled valuations. Our architecture uses the Sinkhorn algorithm to perform a differentiable bipartite matching which allows the network to learn strategyproof revenue-maximizing mechanisms.
  arXiv  Detail & Related papers  (2021-06-15T04:37:57Z)
• PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning [31.509832387330928]
  We propose PreferenceNet, an extension of existing neural-network-based auction mechanisms to encode constraints. We show that our proposed method is competitive with current state-of-the-art neural-network based auction designs.
  arXiv  Detail & Related papers  (2021-06-06T19:29:40Z)
• ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning [55.76903822619047]
  We study the design of revenue-maximizing auctions with strong incentive guarantees. We extend techniques for approximating auctions using deep learning to address concerns of fairness while maintaining high revenue and strong incentive guarantees.
  arXiv  Detail & Related papers  (2020-10-13T13:54:21Z)
• Certifying Strategyproof Auction Networks [53.37051312298459]
  We focus on the RegretNet architecture, which can represent auctions with arbitrary numbers of items and participants. We propose ways to explicitly verify strategyproofness under a particular valuation profile using techniques from the neural network verification literature.
  arXiv  Detail & Related papers  (2020-06-15T20:22:48Z)
• Optimal No-regret Learning in Repeated First-price Auctions [38.908235632001116]
  We study online learning in repeated first-price auctions. We develop the first learning algorithm that achieves a near-optimal $widetildeO(sqrtT)$ regret bound.
  arXiv  Detail & Related papers  (2020-03-22T03:32:09Z)
• A Permutation-Equivariant Neural Network Architecture For Auction Design [49.41561446069114]
  Design of an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design. In this work, we consider auction design problems that have permutationequivariant symmetry and construct a neural architecture that is capable of perfectly recovering the permutationequi optimal mechanism.
  arXiv  Detail & Related papers  (2020-03-02T00:37:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.