Model Monotonicity in Autobidding Auctions: When Do Better Predictions Lead to Better Outcomes?
arXiv:2605.31036v1 Announce Type: cross 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 contributi
The increasing sophistication and widespread adoption of machine learning in advertising platforms necessitate deeper understanding of their economic implications and how model improvements translate to real-world outcomes.
This research provides a foundational framework for understanding how AI model quality directly impacts the economic efficiency and fairness of large-scale automated markets like online advertising auctions, affecting platform revenue and user welfare.
We gain a formal understanding of the conditions under which better predictive models in autobidding actually lead to improved financial metrics for platforms and provide guidance for optimizing auction mechanisms.
- · Online advertising platforms
- · Advertisers leveraging AI
- · AI/ML model developers
- · Auction mechanism designers
- · Platforms with suboptimal auction designs
- · Advertisers without advanced bidding models
Improved model quality directly correlates with enhanced platform performance metrics in autobidding auctions.
This understanding will drive further investment in AI research focused on economic implications and predictive accuracy within market mechanisms.
These principles could extend to other automated resource allocation systems, leading to more efficient and equitable digital economies.
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