# Advancing Ad Auction Realism: Practical Insights & Modeling Implications

arXiv: https://arxiv.org/abs/2307.11732 · HTML: https://mlsanity.com/paper/2307.11732/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 21 Jul 2023.

## Builds on (most cited first, 16 of 16)

- [Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.08365/) (2018)
- [Online learning in repeated auctions](https://mlsanity.com/paper/1511.05720/) (2015)
- [Learning to Bid Without Knowing your Value](https://mlsanity.com/paper/1711.01333/) (2017)
- [Optimal No-regret Learning in Repeated First-price Auctions](https://mlsanity.com/paper/2003.09795/) (2020)
- [Contextual Bandits with Cross-learning](https://mlsanity.com/paper/1809.09582/) (2018)
- [Auction learning as a two-player game](https://mlsanity.com/paper/2006.05684/) (2020)
- [Econometrics for Learning Agents](https://mlsanity.com/paper/1505.00720/) (2015)
- [Artificial Intelligence and Auction Design](https://mlsanity.com/paper/2202.05947/) (2022)
- [Bayesian Meta-Prior Learning Using Empirical Bayes](https://mlsanity.com/paper/2002.01129/) (2020)
- [Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions](https://mlsanity.com/paper/2009.06136/) (2020)
- [Multi-channel Autobidding with Budget and ROI Constraints](https://mlsanity.com/paper/2302.01523/) (2023)
- [Robust Multi-agent Counterfactual Prediction](https://mlsanity.com/paper/1904.02235/) (2019)
- [Robust Learning of Optimal Auctions](https://mlsanity.com/paper/2107.06259/) (2021)
- [Using Multi-Agent Reinforcement Learning in Auction Simulations](https://mlsanity.com/paper/2004.02764/) (2020)
- [Equilibria in Auctions With Ad Types](https://mlsanity.com/paper/2103.06177/) (2021)
- [Optimal Bidding Strategy without Exploration in Real-time Bidding](https://mlsanity.com/paper/2004.00100/) (2020)

## Abstract (arXiv)

Contemporary real-world online ad auctions differ from canonical models [Edelman et al., 2007; Varian, 2009] in at least four ways: (1) values and click-through rates can depend upon users' search queries, but advertisers can only partially "tune" their bids to specific queries; (2) advertisers do not know the number, identity, and precise value distribution of competing bidders; (3) advertisers only receive partial, aggregated feedback, and (4) payment rules are only partially known to bidders. These features make it virtually impossible to fully characterize equilibrium bidding behavior. This paper shows that, nevertheless, one can still gain useful insight into modern ad auctions by modeling advertisers as agents governed by an adversarial bandit algorithm, independent of auction mechanism intricacies. To demonstrate our approach, we first simulate "soft-floor" auctions [Zeithammer, 2019], a complex, real-world pricing rule for which no complete equilibrium characterization is known. We find that (i) when values and click-through rates are query-dependent, soft floors can improve revenues relative to standard auction formats even if bidder types are drawn from the same distribution; and (ii) with distributional asymmetries that reflect relevant real-world scenario, we find that soft floors yield lower revenues than suitably chosen reserve prices, even restricting attention to a single query. We then demonstrate how to infer advertiser value distributions from observed bids for a variety of pricing rules, and illustrate our approach with aggregate data from an e-commerce website.