Statistically Truthful Auctions via Acceptance Rule
Roy Maor Lotan, Inbal Talgam-Cohen, Yaniv Romano

TL;DR
This paper introduces STAR, a method that uses conformal prediction to ensure auction mechanisms are statistically strategy-proof, balancing truthful bidding guarantees with revenue considerations.
Contribution
It proposes a novel statistical formulation of strategy-proofness and an acceptance rule that guarantees high-probability truthful bidding in data-driven auctions.
Findings
Guarantees high-probability strategy-proofness in auctions
Balances revenue and truthfulness effectively
Provides a practical approach for data-driven auction design
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 for auction mechanisms. Specifically, we offer a method that bounds the regret -- quantifying deviation from truthful bidding -- below a pre-specified level with high probability. Building upon conformal prediction techniques, we develop an auction acceptance rule that leverages regret…
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Taxonomy
TopicsAuction Theory and Applications · Consumer Market Behavior and Pricing
