Model Monotonicity in Autobidding Auctions: When Do Better Predictions Lead to Better Outcomes?

• URL: http://arxiv.org/abs/2605.31036v1
• Date: Fri, 29 May 2026 09:09:06 GMT
• Title: Model Monotonicity in Autobidding Auctions: When Do Better Predictions Lead to Better Outcomes?
• Abstract summary: We study the interaction between recommender system model quality, auction format, and autobidder behavior.<n>We show that first-price auctions with uniform bidding guarantee revenue monotonicity for tCPA bidders without budgets.<n>Our findings have practical implications for advertising platforms seeking to align model improvements with business outcomes.
• Score: 1.2691047660244335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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