Conformalized Strategy-Proof Auctions

• URL: http://arxiv.org/abs/2405.12016v4
• Date: Mon, 10 Feb 2025 17:06:04 GMT
• Title: Conformalized Strategy-Proof Auctions
• Authors: Roy Maor Lotan, Inbal Talgam-Cohen, Yaniv Romano,
• Abstract summary: Strategy-proofness is crucial as it ensures that buyers are incentivized to bid their true valuations. We propose a formulation of statistical strategy-proofness auction mechanism. We develop an auction acceptance rule that leverages regret predictions to guarantee that the data-driven auction mechanism meets the statistical strategy-proofness requirement with high probability.
• Score: 19.750369749595734
• License:
• Abstract: Auctions are key for maximizing sellers' revenue and ensuring truthful bidding among buyers. Recently, an approach known as differentiable economics based on machine learning (ML) has shown promise in learning powerful auction mechanisms for multiple items and participants. However, this approach has no guarantee of strategy-proofness at test time. Strategy-proofness is crucial as it ensures that buyers are incentivized to bid their true valuations, leading to optimal and fair auction outcomes without the risk of manipulation. In this work, we propose a formulation of statistical strategy-proofness auction mechanism, ensuring that the probability of regret exceeding a predefined threshold is strictly controlled. Building upon conformal prediction techniques, we develop an auction acceptance rule that leverages regret predictions to guarantee that the data-driven auction mechanism meets the statistical strategy-proofness requirement with high probability. Our approach represents a practical middle-ground between two extremes: forcing zero-regret at the cost of significant revenue loss, and naively using ML to construct auctions with the hope of attaining low regret at test time. Numerical experiments demonstrate the necessity of the proposed method, the validity of our theoretical result, and its applicability.

Related papers

• Learning to Bid in Non-Stationary Repeated First-Price Auctions [20.933903777434086]
  First-price auctions have gained significant traction in digital advertising markets. determining an optimal bidding strategy in first-price auctions is more complex. We introduce two metrics to quantify the regularity of the bidding sequence.
  arXiv  Detail & Related papers  (2025-01-23T03:53:27Z)
• The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks [90.52808174102157]
  In safety-critical applications such as medical imaging and autonomous driving, it is imperative to maintain both high adversarial robustness to protect against potential adversarial attacks. A notable knowledge gap remains concerning the uncertainty inherent in adversarially trained models. This study investigates the uncertainty of deep learning models by examining the performance of conformal prediction (CP) in the context of standard adversarial attacks.
  arXiv  Detail & Related papers  (2024-05-14T18:05:19Z)
• Mimicking Better by Matching the Approximate Action Distribution [48.95048003354255]
  We introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations. We show that it requires considerable fewer interactions to achieve expert performance, outperforming current state-of-the-art on-policy methods.
  arXiv  Detail & Related papers  (2023-06-16T12:43:47Z)
• Robust multi-item auction design using statistical learning: Overcoming uncertainty in bidders' types distributions [6.5920927560926295]
  Our proposed approach utilizes nonparametric density estimation to accurately estimate bidders' types from historical bids. To further enhance efficiency of our mechanism, we introduce two novel strategies for query reduction. Simulation experiments conducted on both small-scale and large-scale data demonstrate that our mechanism consistently outperforms existing methods in terms of revenue design and query reduction.
  arXiv  Detail & Related papers  (2023-02-02T08:32:55Z)
• Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising [47.14651340748015]
  We propose a novel adaptive risk-aware bidding algorithm with budget constraint via reinforcement learning. We theoretically unveil the intrinsic relation between the uncertainty and the risk tendency based on value at risk (VaR)
  arXiv  Detail & Related papers  (2022-12-06T18:50:09Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Multi-Stage Decentralized Matching Markets: Uncertain Preferences and Strategic Behaviors [91.3755431537592]
  This article develops a framework for learning optimal strategies in real-world matching markets. We show that there exists a welfare-versus-fairness trade-off that is characterized by the uncertainty level of acceptance. We prove that participants can be better off with multi-stage matching compared to single-stage matching.
  arXiv  Detail & Related papers  (2021-02-13T19:25:52Z)
• A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components [2.28438857884398]
  The proposed methodology is showcased on two automated trading platform components. Namely, price levels, a well-known trading pattern, and a novel 2-step feature extraction method. The main hypothesis was formulated to evaluate whether the selected trading pattern is suitable for use in the machine learning setting.
  arXiv  Detail & Related papers  (2020-09-21T16:17:42Z)
• 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 Bidding Strategy without Exploration in Real-time Bidding [14.035270361462576]
  maximizing utility with a budget constraint is the primary goal for advertisers in real-time bidding (RTB) systems. Previous works ignore the losing auctions to alleviate the difficulty with censored states. We propose a novel practical framework using the maximum entropy principle to imitate the behavior of the true distribution observed in real-time traffic.
  arXiv  Detail & Related papers  (2020-03-31T20:43:28Z)

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.