Estimating Approximate Incentive Compatibility

Balcan, Maria-Florina; Sandholm, Tüomas; Vitercik, Ellen

doi:10.1145/3328526.3329628

Cited by 18 publications

(24 citation statements)

References 40 publications

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“…The general theorem of Balcan et al [2019a] (mentioned in Section 2.3) can be used to recover the bounds in these papers. Furthermore, the dispersion tools derived in Section 3 have been used for providing estimators for the degree of approximate incentive compatibility of an auction, another important problem in modern auction design [Balcan et al, 2019b].…”

Section: Summary and Discussionmentioning

confidence: 99%

Data-driven Algorithm Design

Balcan¹

2020

Preprint

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Data driven algorithm design is an important aspect of modern data science and algorithm design. Rather than using off the shelf algorithms that only have worst case performance guarantees, practitioners often optimize over large families of parametrized algorithms and tune the parameters of these algorithms using a training set of problem instances from their domain to determine a configuration with high expected performance over future instances. However, most of this work comes with no performance guarantees. The challenge is that for many combinatorial problems of significant importance including partitioning, subset selection, and alignment problems, a small tweak to the parameters can cause a cascade of changes in the algorithm's behavior, so the algorithm's performance is a discontinuous function of its parameters.In this chapter, we survey recent work that helps put data-driven combinatorial algorithm design on firm foundations. We provide strong computational and statistical performance guarantees, both for the batch and online scenarios where a collection of typical problem instances from the given application are presented either all at once or in an online fashion, respectively.

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Section: Summary and Discussionmentioning

confidence: 99%

Data-driven Algorithm Design

Balcan¹

2020

Preprint

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“…One thread of work has been learning-theoretic, determining the sample complexity for various known families of auctions to estimate properties like revenue [5,8,36] or incentive compatibility [4].…”

Section: Related Workmentioning

confidence: 99%

Learning Revenue-Maximizing Auctions With Differentiable Matching

Curry,

Lyi,

Goldstein

et al. 2021

Preprint

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We propose a new architecture to approximately learn incentive compatible, revenue-maximizing auctions from sampled valuations. Our architecture uses the Sinkhorn algorithm to perform a differentiable bipartite matching which allows the network to learn strategyproof revenue-maximizing mechanisms in settings not learnable by the previous RegretNet architecture. In particular, our architecture is able to learn mechanisms in settings without free disposal where each bidder must be allocated exactly some number of items. In experiments, we show our approach successfully recovers multiple known optimal mechanisms and highrevenue, low-regret mechanisms in larger settings where the optimal mechanism is unknown.Preprint. Under review.

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“…Note that Theorems 1 and 2 are significantly stronger versions of the theorem we presented in (Bosshard et al, 2017) producing much tighter bounds on ε. The general idea of using a finite subset of the value space to obtain a bound on the whole value space was also used more recently by Balcan et al (2019) who employ learning theory to estimate approximate incentive compatibility of non-truthful mechanisms.…”

Section: The Verification Phasementioning

confidence: 99%

Computing Bayes-Nash Equilibria in Combinatorial Auctions with Continuous Value and Action Spaces

Bosshard

Bünz

Lubin

et al. 2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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Combinatorial auctions (CAs) are widely used in practice, which is why understanding their incentive properties is an important problem. However, finding Bayes-Nash equilibria (BNEs) of CAs analytically is tedious, and prior algorithmic work has only considered limited solution concepts (e.g. restricted action spaces). In this paper, we present a fast, general algorithm for computing symmetric pure ε-BNEs in CAs with continuous values and actions. In contrast to prior work, we separate the search phase (for finding the BNE) from the verification step (for estimating the ε), and always consider the full (continuous) action space in the best response computation. We evaluate our method in the well-studied LLG domain, against a benchmark of 16 CAs for which analytical BNEs are known. In all cases, our algorithm converges quickly, matching the known results with high precision. Furthermore, for CAs with quasi-linear utility functions and independently distributed valuations, we derive a theoretical bound on ε. Finally, we introduce the new Multi-Minded LLLLGG domain with eight goods and six bidders, and apply our algorithm to finding an equilibrium in this domain. Our algorithm is the first to find an accurate BNE in a CA of this size.

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Estimating Approximate Incentive Compatibility

Cited by 18 publications

References 40 publications

Data-driven Algorithm Design

Data-driven Algorithm Design

Learning Revenue-Maximizing Auctions With Differentiable Matching

Computing Bayes-Nash Equilibria in Combinatorial Auctions with Continuous Value and Action Spaces

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