arXiv:2606.29252v1 Announce Type: new Abstract: We study repeated bidding in multi-unit discriminatory (pay-as-bid) auctions for a single bidder with per-round utility equal to value minus $\alpha$ times payment, where $\alpha\in[0,1]$ is a cost-of-capital parameter. The bidder aims to maximize cumulative utility over $T$ rounds subject to a total budget $B$. The problem is challenging even without budgets: the action space is exponential in $M$, the maximum demand of the bidder and the valuation vector (context) varies over time. Exploiting a decomposition of utility across units, we develop
The increasing complexity and scale of online auctions, particularly in programmatic advertising and cloud resource allocation, necessitate more sophisticated automated bidding strategies.
Optimized bidding strategies with budget constraints are crucial for businesses to maximize returns on their investments in competitive digital markets and for efficient resource allocation in AI-driven economies.
This research provides a theoretical and algorithmic framework for AI agents to participate more effectively in multi-unit discriminatory auctions, particularly in scenarios with financial constraints.
- · Companies with significant digital advertising spend
- · Cloud service providers (optimizing resource allocation)
- · AI/ML researchers in game theory
- · Ad-tech platforms
- · Inefficient bidders without advanced AI strategies
- · Auction platforms with easily exploitable mechanisms
Improved bidding efficiency for automated agents in complex auctions.
Increased competition and potentially higher prices in programmatic ad markets as agents become more sophisticated.
The development of more complex, dynamic auction mechanisms designed to counter advanced AI bidding strategies.
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