Learning Advertiser Values from Strategic Bids in Platform Position Auctions

Digital platforms allocate scarce user attention through position auctions, yet the advertiser values needed for platform design are not directly observed. In generalized second-price (GSP) auctions, bids are strategic and therefore cannot be interpreted as values. This poses a structural estimation problem: how can a platform recover latent advertiser value distributions from observed bids when analytical bid-to-value inversion is mechanism-specific or altogether unavailable? We propose a simulation-based estimator for non-truthful platform auctions. The method represents advertiser value distributions with flexible Bernstein-polynomial CDFs, computes approximate equilibrium bidding strategies for candidate primitives, and selects the primitives whose simulated bids match the observed bids in Wasserstein distance. A Bayesian-optimization-assisted SMC-ABC procedure guides the search while limiting the number of costly equilibrium computations. We validate the approach on benchmark auction simulations and on GSP laboratory data, and use the recovered value distributions to evaluate counterfactual platform-design choices such as optimal reserve prices. The framework offers a practical route to data-driven market design in digital platform auctions where standard inversion-based estimators are difficult to apply.