Adaptive Bidding Strategies for Hybrid Auction Mechanisms in Programmatic Advertising

Abstract

Programmatic advertising ecosystems increasingly employ hybrid auction mechanisms combining first-price and second-price dynamics, creating unprecedented challenges for optimal bidding strategy formulation. This research develops an adaptive bidding framework integrating statistical detection methodologies with differentiated bidding functions tailored to distinct auction mechanisms. Through implementation of multi-armed bandit algorithms for continuous strategy optimization, our approach addresses the complexity of cross-mechanism environments where auction types remain opaque to bidders. Experimental evaluation across diverse market conditions demonstrates performance improvements of 27.3% in cost efficiency and 18.5% in win rate compared to static baseline strategies. The proposed framework incorporates real-time mechanism identification achieving 94.2% classification accuracy within seven bid iterations. Computational experiments validate the methodology's robustness across varying market volatilities and competition intensities. Our contributions extend beyond theoretical advancement, offering practical implementation pathways for digital advertising platforms navigating heterogeneous auction environments.