Optimizing Bidding Strategies in First-Price Auctions in Binary Feedback Setting with Predictions (2506.15817v1)

Abstract: This paper studies Vickrey first-price auctions under binary feedback. Leveraging the enhanced performance of machine learning algorithms, the new algorithm uses past information to improve the regret bounds of the BROAD-OMD algorithm. Motivated by the growing relevance of first-price auctions and the predictive capabilities of machine learning models, this paper proposes a new algorithm within the BROAD-OMD framework (Hu et al., 2025) that leverages predictions of the highest competing bid. This paper's main contribution is an algorithm that achieves zero regret under accurate predictions. Additionally, a bounded regret bound of O(T^3/4 * Vt^1/4) is established under certain normality conditions.

Overview of Bidding Strategies in First-Price Auctions with Predictions

The discussed paper presents a novel approach for optimizing bidding strategies in first-price auctions employing binary feedback and machine learning predictions. It introduces an advancement in the BROAD-OMD framework by Hu et al. (2025), providing a model that leverages predictions of the highest competing bid for improved outcomes in auction scenarios. This research elucidates the dynamics of first-price auctions where the winner pays the highest bid they made, in contrast to second-price auctions where the winner pays the second-highest bid. The paper targets an important issue emerging from increased first-price auction adoption, addressing motivations rooted in potential transparency problems attributed to second-price auctions.

Key Contributions

The paper makes several notable contributions:

1. Algorithm Development: The primary contribution is the formulation of an algorithm capable of achieving zero regret with accurate predictions, alongside a bounded regret of $\tilde{O}(T^{3/4}V_t^{1/4})$ under stipulated normality conditions when predictions are less accurate. This is noteworthy as it extends the work on first-price auction strategies and focuses on the potential for predictive algorithms to reduce regret compared to extant methods.
2. Window Algorithm: The creation of the "Window Algorithm" is particularly noteworthy. This algorithm dynamically adapts based on the accuracy of predictions and optimizes bidding strategies within a finite time window. It maintains zero regret when predictions are accurate and promises bounded regret even when the predictions deviate from actual outcomes. The algorithm's robustness hinges on assumptions regarding the temporal variation $V_T$, specified to ensure constraints on bid fluctuation over time.
3. Numerical Results: The paper provides theoretical bounds on regret using these algorithms. The Window Algorithm improves upon previous methods, offering a refined approach to incorporating predictive models in auction strategies without negatively impacting robustness.

Implications for Theory and Practice

The implications of this research are significant both theoretically and practically. Theoretically, it extends the scope of auction theory by incorporating machine learning predictions to enhance bidder strategies within the constraints of minimal feedback. Practically, it addresses real-world challenges faced by bidders in first-price auctions, offering strategies that could be applicable in online advertisement markets and beyond. The emphasis on achieving low consistency and bounded robustness in regret bounds indicates a viable pathway for engaging predictive models in competitive auction settings in a scalable manner.

Speculation on Future Developments

Potential future developments in this area could focus on further refining the prediction models to enhance their accuracy and robustness, ensuring that these strategies are more widely applicable across varying market conditions. Additionally, exploring continuous bid-value landscapes, as opposed to discretized avenues, could provide an important direction for making the model more aligned with real-world practices. Personalized models incorporating bidder-specific predictors might also lead to enhanced strategies.

In summary, the paper presents an analytical advancement for predicting optimal bidding strategies in first-price auctions, enriching both theoretical constructs and practical applications. The proposed methodologies demonstrate the use of predictive analytics in dynamic and uncertain environments, paving the way for future research that could extend and refine these approaches.