Testing Incentive Compatibility in Display Ad Auctions

Lahaie, Sébastien; Medina, Andrés Muñoz; Sivan, Balasubramanian; Vassilvitskii, Sergei

doi:10.1145/3178876.3186047

Cited by 14 publications

(21 citation statements)

References 16 publications

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“…Incentive compatibility from a buyer's perspective. Lahaie et al [2018] also provide tools for estimating approximate incentive compatibility, but from the buyer's perspective rather than the mechanism designer's perspective. As such, the type of information available to their estimation tools versus ours is different.…”

Section: Additional Related Researchmentioning

confidence: 99%

Estimating Approximate Incentive Compatibility

Balcan

Sandholm

Vitercik

2019

Proceedings of the 2019 ACM Conference on Economics and Computation

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In practice, most mechanisms for selling, buying, matching, voting, and so on are not incentive compatible. We present techniques for estimating how far a mechanism is from incentive compatible. Given samples from the agents' type distribution, we show how to estimate the extent to which an agent can improve his utility by misreporting his type. We do so by first measuring the maximum utility an agent can gain by misreporting his type on average over the samples, assuming his true and reported types are from a finite subset-which our technique constructs-of the type space. The challenge is that by measuring utility gains over a finite subset of the type space, we might miss type pairs θ andθ where an agent with type θ can greatly improve his utility by reporting typeθ. Indeed, our primary technical contribution is proving that the maximum utility gain over this finite subset nearly matches the maximum utility gain overall, despite the volatility of the utility functions we study. We apply our tools to the single-item and combinatorial first-price auctions, generalized second-price auction, discriminatory auction, uniform-price auction, and second-price auction with spiteful bidders.

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Section: Additional Related Researchmentioning

confidence: 99%

Estimating Approximate Incentive Compatibility

Balcan

Sandholm

Vitercik

2019

Proceedings of the 2019 ACM Conference on Economics and Computation

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“…Lahaie et al [18] are the first to study the problem of testing incentive compatibility assuming only black box access. They design an A/B experiment to determine whether an auction is incentive compatible in both single-shot and dynamic settings (the latter concerns cases where an advertiser's bid is used to set their reserve price in later auctions).…”

Section: Related Workmentioning

confidence: 99%

“…They design an A/B experiment to determine whether an auction is incentive compatible in both single-shot and dynamic settings (the latter concerns cases where an advertiser's bid is used to set their reserve price in later auctions). The work of Lahaie et al [18] provides a valuable tool for validation of incentive compatibility, but leaves open the question of how to design an experiment that minimizes the time required to get results with high confidence. The present paper complements [18] by giving an algorithm to select alternative bids that minimize the error in the estimate for IC regret.…”

Section: Related Workmentioning

confidence: 99%

“…The work of Lahaie et al [18] provides a valuable tool for validation of incentive compatibility, but leaves open the question of how to design an experiment that minimizes the time required to get results with high confidence. The present paper complements [18] by giving an algorithm to select alternative bids that minimize the error in the estimate for IC regret.…”

Section: Related Workmentioning

confidence: 99%

“…a uniform interval [0, 1]). Utilizing the discretization result in [16,15,12], let B be the discretization of continuous bid space B and DE(B, B) to represent the discretization error of bid space B and B such that In practice, the expected allocation function g * and payment function p * are both relatively smooth (see e.g.. the plots in [18]). Assume the Lipschitzness of expected allocation function and payment function, the discretization error DE(B, B) can be easily bounded, then our Pseudo-Regret analysis can be directly applied in this continuous bid space.…”

Section: Continuous Bids Spacementioning

confidence: 99%

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Online Learning for Measuring Incentive Compatibility in Ad Auctions?

Feng

Schrijvers

Sodomka

2019

The World Wide Web Conference

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In this paper we investigate the problem of measuring end-to-end Incentive Compatibility (IC) regret given black-box access to an auction mechanism. Our goal is to 1) compute an estimate for IC regret in an auction, 2) provide a measure of certainty around the estimate of IC regret, and 3) minimize the time it takes to arrive at an accurate estimate. We consider two main problems, with different informational assumptions: In the advertiser problem the goal is to measure IC regret for some known valuation v, while in the more general demandside platform (DSP) problem we wish to determine the worst-case IC regret over all possible valuations. The problems are naturally phrased in an online learning model and we design Regret-UCB algorithms for both problems. We give an online learning algorithm where for the advertiser problem the error of determining IC shrinks as O |B| T · ln T n + ln T n (where B is the finite set of bids, T is the number of time steps, and n is number of auctions per time step), and for the DSP problem it shrinks as O |B| T · |B| ln T n + |B| ln T n . For the DSP problem, we also consider stronger IC regret estimation and extend our Regret-UCB algorithm to achieve better IC regret error. We validate the theoretical results using simulations with Generalized Second Price (GSP) auctions, which are known to not be incentive compatible and thus have strictly positive IC regret.

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