Modularity and greed in double auctions

Dütting, Paul; Roughgarden, Tim; Talgam-Cohen, Inbal

doi:10.1145/2600057.2602854

Cited by 39 publications

(47 citation statements)

References 27 publications

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“…Their revenue-maximization result is similar to Myerson's seminal single auction result [10] in that it, too, uses virtual valuation functions, for both buyer and seller. Welfare maximization for multiple buyers and sellers has been further studied for instance by McAfee [9] or more recently by [4,5,1].…”

Section: Related Workmentioning

confidence: 99%

Revenue Maximization for Market Intermediation with Correlated Priors

Gerstgrasser

Goldberg

Koutsoupias

2016

Algorithmic Game Theory

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We study the computational challenge faced by a intermediary who attempts to profit from trade with a small number of buyers and sellers of some item. In the version of the problem that we study, the number of buyers and sellers is constant, but their joint distribution of the item's value may be complicated. We consider discretized distributions, where the complexity parameter is the support size, or number of different prices that may occur. We show that maximizing the expected revenue is computationally tractable (via an LP) if we are allowed to use randomized mechanisms. For the deterministic case, we show how an optimal mechanism can be efficiently computed for the oneseller/one-buyer case, but give a contrasting NP-completeness result for the one-seller/two-buyer case.

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Section: Related Workmentioning

confidence: 99%

Revenue Maximization for Market Intermediation with Correlated Priors

Gerstgrasser

Goldberg

Koutsoupias

2016

Algorithmic Game Theory

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“…Several results show that generalizations of posted price mechanisms can achieve a constant fraction of the first-best welfare in fairly general multi-dimensional two-sided markets [5,14,16,19]. However, GFT maxmization in multidimensional settings has remained elusive.…”

Section: Introductionmentioning

confidence: 99%

On Multi-Dimensional Gains from Trade Maximization

Cai¹,

Goldner²,

Ma³

et al. 2021

Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA)

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We study gains from trade in multi-dimensional two-sided markets. Specifically, we focus on a setting with n heterogeneous items, where each item is owned by a different seller i, and there is a constrained-additive buyer with feasibility constraint F. Multi-dimensional settings in one-sided markets, e.g. where a seller owns multiple heterogeneous items but also is the mechanism designer, are well-understood. In addition, single-dimensional settings in two-sided markets, e.g. where a buyer and seller each seek or own a single item, are also well-understood. Multi-dimensional two-sided markets, however, encapsulate the major challenges of both lines of work: optimizing the sale of heterogeneous items, ensuring incentive-compatibility among both sides of the market, and enforcing budget balance. We present, to the best of our knowledge, the first worst-case approximation guarantee for gains from trade in a multi-dimensional two-sided market.Our first result provides an O(log(1/r))-approximation to the first-best gains from trade for a broad class of downward-closed feasibility constraints (such as matroid, matching, knapsack, or the intersection of these). Here r is the minimum probability over all items that a buyer's value for the item exceeds the seller's cost. Our second result removes the dependence on r and provides an unconditional O(log n)-approximation to the second-best gains from trade. We extend both results for a general constrained-additive buyer, losing another O(log n)-factor en-route. The first result is achieved using a fixed posted price mechanism, and the analysis involves a novel application of the prophet inequality or a new concentration inequality. Our second result follows from a stitching lemma that allows us to upper bound the second-best gains from trade by the first-best gains * The arxiv version of this paper can be found at https://arxiv. org/abs/2007.13934. † Supported by a Sloan Foundation Research Fellowship and the NSF Award CCF-1942583 (CAREER) . ‡ Supported by NSF Award DMS-1903037 and a Columbia Data Science Institute postdoctoral fellowship.from trade from the "likely to trade" items (items with trade probability at least 1/n) and the optimal profit from selling the "unlikely to trade" items. We can obtain an O(log n)approximation to the first term by invoking our O(log(1/r))approximation on the "likely to trade" items. We introduce a generalization of the fixed posted price mechanism-seller adjusted posted price-to obtain an O(log n)-approximation to the optimal profit for the "unlikely to trade" items. Unlike fixed posted price mechanisms, not all seller adjusted posted price mechanisms are incentive compatible and budget balanced. We develop a new argument based on "allocation coupling" to show the seller adjusted posted price mechanism used in our approximation is indeed budget balanced and incentive-compatible.

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“…Nguyen and Sandholm [37] provide an experimental evaluation of the performance of various DA heuristics for the actual interference constraints of the upcoming FCC auction. The DA framework has also been extended to double auctions by Dütting et al [12] and to multilateral markets by Blumrosen and Zohar [7]. The application to double auctions is explored further by Marx and Loertscher [29].…”

Section: Introductionmentioning

confidence: 99%

Deferred-Acceptance Auctions for Multiple Levels of Service

Gkatzelis

Markakis

Roughgarden

2017

Proceedings of the 2017 ACM Conference on Economics and Computation

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Deferred-acceptance (DA) auctions are mechanisms that are based on backward-greedy algorithms and possess a number of remarkable incentive properties, including implementation as an obviously-strategyproof ascending auction. All existing work on DA auctions considers only binary single-parameter problems, where each bidder either "wins" or "loses. " This paper generalizes the DA auction framework to non-binary settings, and applies this generalized framework to obtain approximately welfare-maximizing DA auctions for a number of basic mechanism design problems: multiunit auctions, problems with polymatroid constraints or multiple knapsack constraints, and the problem of scheduling jobs to minimize their total weighted completion time. Our results require the design of novel backward-greedy algorithms with good approximation guarantees. CCS Concepts: • Theory of computation → Approximation algorithms analysis; Algorithmic game theory and mechanism design;

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Modularity and greed in double auctions

Cited by 39 publications

References 27 publications

Revenue Maximization for Market Intermediation with Correlated Priors

Revenue Maximization for Market Intermediation with Correlated Priors

On Multi-Dimensional Gains from Trade Maximization

Deferred-Acceptance Auctions for Multiple Levels of Service

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