Model Monotonicity in Autobidding Auctions: When Do Better Predictions Lead to Better Outcomes?
This work addresses a critical problem for online advertising platforms: ensuring that better predictive models translate into better business outcomes, which is not always guaranteed.
This paper investigates when improvements in machine learning models for predicting click-through and conversion rates in online advertising auctions lead to better platform-level outcomes like revenue or welfare. It finds that first-price auctions with uniform bidding guarantee revenue monotonicity for tCPA bidders without budgets, but second-price auctions and budget constraints can break this property.
Online advertising platforms rely on machine learning models to predict click-through rates (pCTR) and conversion rates (pCVR) for auction mechanisms. We introduce a novel framework to study the interaction between recommender system model quality, auction format, and autobidder behavior. We formalize when model improvements -- defined via a refinement relation inspired by filtrations in probability theory -- lead to improvements in platform-level Evaluation Criteria Metrics (ECM) such as revenue, welfare, or liquid welfare. Our main contributions are: (1) a formal definition of model improvement based on cluster refinement, and (2) a systematic characterization of ECM monotonicity across different combinations of bidder types (tCPA, max-CPA), auction formats (first-price, second-price, VCG), and budget constraints. We show that first-price auctions with uniform bidding guarantee revenue monotonicity for tCPA bidders without budgets (via Jensen's inequality), while second-price auctions and budget constraints can break this property. We provide full numerical constructions for the non-monotonicity results. Our findings have practical implications for advertising platforms seeking to align model improvements with business outcomes.