Bidding Behavior

Bidding behavior research investigates how agents strategically choose bids in various auction settings, aiming to optimize outcomes like revenue, efficiency, and individual profit. Current research focuses on developing sophisticated models, including reinforcement learning (particularly deep reinforcement learning) and adversarial bandit algorithms, to simulate and predict bidding strategies across diverse contexts such as advertising, energy markets, and data trading. These advancements are improving auction design, enabling more efficient resource allocation, and offering insights into strategic interactions in complex systems. The findings have significant implications for both theoretical understanding of game theory and practical applications in online advertising, energy markets, and data marketplaces.