Learning to Bid in Repeated Second-Price Auctions with Dynamic Values and Aggregated Feedback
arXiv:2605.28133v1 Announce Type: new Abstract: We study the problem of learning to bid when the bidder's value is dynamic, i.e., when the current value depends on past outcomes. Specifically, we consider a bidder participating in repeated second-price auctions whose value depends on the time elapsed since their last successful bid, with auctions arriving in continuous time and only aggregated feedback revealed at the end of the horizon. Such a bidder must (1) balance the immediate benefit of winning the current auction against its impact on future values and (2) learn unknown environmental pa
The proliferation of complex online marketplaces and the increasing sophistication of AI models make learning optimal bidding strategies a critical area of research.
This research provides a foundational understanding for how AI agents can operate effectively in dynamic, competitive environments with limited feedback, impacting various automated decision-making systems.
The ability of automated systems to engage in more sophisticated and adaptive economic interactions, moving beyond static bidding strategies.
- · Ad platforms
- · Automated bidding software providers
- · E-commerce marketplaces
- · Computational economists
- · Bidders using static strategies
- · Inefficient auction mechanisms
- · Platforms with easily exploitable bidding protocols
Improved efficiency and competitiveness in online auctions and marketplaces due to more intelligent bidding agents.
Development of more complex and robust auction mechanisms to account for sophisticated AI bidders with dynamic value functions.
The integration of such learning-to-bid algorithms into broader AI agent frameworks, enabling autonomous agents to participate and optimize across various economic activity streams.
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