Collaborative Research: CIF: Medium: An Information-Theoretic Foundation for Adaptive Bidding in First-Price Auctions

With the advent and increasing consolidation of e-commerce, digital advertising has very recently replaced traditional advertising as the main marketing force in the economy. In the past two years, a particularly important development in the digital advertising industry is the shift from second-price auctions to first-price auctions for online display ads.

This shift immediately motivated the intellectually challenging question of how to bid in first-price auctions, because unlike in second-price auctions, bidding one's private value truthfully is no longer optimal. Furthermore, this shift has two unique modern characteristics: 1) the auctions are occurring repeatedly at a very high frequency and the bidding decisions must be made on that (milliseconds) timescale; second, there is exchange-dependent feedback information that one can and should leverage to inform one's sequential bidding decisions.

These two characteristics expose drawbacks in the existing game-theoretical approaches and call for novel and principled developments in sequential bidding. The methodological and algorithmic innovation established in this project could also potentially help various organizations with advertising needs to navigate in the new and challenging landscape of display ads bidding.

The broad goal of this project is to develop a methodological framework that intelligently and adaptively leverages past information to construct bidding strategies that are both computationally and statistically efficient. This requires developing information-theoretic tools to understand the fundamental learning limits for bidding in first-price auctions, where the reward function is neither convex nor continuous but has a special structure of its own that needs to be exploited.

Further, it requires developing computationally efficient bidding and private value estimation algorithms for repeated first-price auctions that could meet the demanding nature of real-time bidding and large-scale historical bidding dataset, as well as learning-theoretical tools that enable the analysis and rigorous characterization of the algorithms' performance.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.