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This paper presents the first polynomial-time algorithm for position and matroid auction environments that learns, from samples from an unknown bounded valuation distribution, an auction with expected revenue arbitrarily close to the maximum possible. In contrast to most previous work, our results apply to arbitrary (not necessarily regular) distributions and the strongest possible benchmark, the Myerson-optimal auction. Learning a near-optimal auction for an irregular distribution is technically challenging because it requires learning the appropriate "ironed intervals," a delicate global property of the distribution.

In the isolated auction of a single item, second price often dominates first price in properties of theoretical interest. But, single items are rarely sold in true isolation, so considering the broader context is critical when adopting a pricing strategy. In this paper, we study a model centrally relevant to Internet advertising and show that when items (ad impressions) are individually auctioned within the context of a larger system that is managing budgets, theory offers surprising endorsement for using a first price auction to sell each individual item. In particular, first price auctions offer theoretical guarantees of equilibrium uniqueness, monotonicity, and other desirable properties, as well as efficient computability as the solution to the well-studied Eisenberg-Gale convex program. We also use simulations to demonstrate that a bidder's incentive to deviate vanishes in thick markets. *

Mature internet advertising platforms offer high-level campaign management tools to help advertisers run their campaigns, often abstracting away the intricacies of how each ad is placed and focusing on aggregate metrics of interest to advertisers. On such platforms, advertisers often participate in auctions through a proxy bidder, so the standard incentive analyses that are common in the literature do not apply directly. In this paper, we take the perspective of a budget management system that surfaces aggregated incentives—instead of individual auctions—and compare first and second price auctions. We show that theory offers surprising endorsement for using a first price auction to sell individual impressions. In particular, first price auctions guarantee uniqueness of the steady-state equilibrium of the budget management system, monotonicity, and other desirable properties, as well as efficient computation through the solution to the well-studied Eisenberg–Gale convex program. Contrary to what one can expect from first price auctions, we show that incentives issues are not a barrier that undermines the system. Using realistic instances generated from data collected at real-world auction platforms, we show that bidders have small regret with respect to their optimal ex post strategy, and they do not have a big incentive to misreport when they can influence equilibria directly by giving inputs strategically. Finally, budget-constrained bidders, who have significant prevalence in real-world platforms, tend to have smaller regrets. Our computations indicate that bidder budgets, pacing multipliers, and regrets all have a positive association in statistical terms. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

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