Multi-Platform Budget Management in Ad Markets with Non-IC Auctions
This work addresses budget management for advertisers in complex, non-incentive-compatible auction environments, representing an incremental improvement over prior adaptive pacing methods.
The paper tackles the problem of optimal bidding for budget-constrained advertisers in multi-platform ad auctions that may not be incentive-compatible, proposing a strategy that maximizes expected utility while meeting budget constraints and achieving O(T^{3/4}) regret in an online learning setting. It demonstrates superior cumulative regret compared to existing adaptive pacing algorithms on synthetic and real-world datasets.
In online advertising markets, budget-constrained advertisers acquire ad placements through repeated bidding in auctions on various platforms. We present a strategy for bidding optimally in a set of auctions that may or may not be incentive-compatible under the presence of budget constraints. Our strategy maximizes the expected total utility across auctions while satisfying the advertiser's budget constraints in expectation. Additionally, we investigate the online setting where the advertiser must submit bids across platforms while learning about other bidders' bids over time. Our algorithm has $O(T^{3/4})$ regret under the full-information setting. Finally, we demonstrate that our algorithms have superior cumulative regret on both synthetic and real-world datasets of ad placement auctions, compared to existing adaptive pacing algorithms.