Competitive auctions

Goldberg, Andrew V.; Hartline, Jason D.; Karlin, Anna R.; Saks, Michael; Wright, Andrew

doi:10.1016/j.geb.2006.02.003

Cited by 239 publications

(286 citation statements)

References 28 publications

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“…Specifically, we use the random-sampling method introduced in [6] in order to be able to "guess" the departure time of each bidder. This mechanism is also a prompt mechanism.…”

Section: Theoremmentioning

confidence: 99%

“…We first claim that since no bidder dominates the optimal welfare, with probability 1 − o(1) the bidders in C hold a fraction of at least 1 8 of the optimal welfare. The claim follows from the fact that each bidder has a probability of 1 4 to be selected to C (proof of claim omitted -see, e.g., [6]). We will now see that we can recover at least a logarithmic fraction of the welfare of bidders in C.…”

Section: Proofmentioning

confidence: 99%

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Prompt Mechanisms for Online Auctions

Cole

Dobzinski

Fleischer

2008

Algorithmic Game Theory

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We study the following online problem: at each time unit, one of m identical items is offered for sale. Bidders arrive and depart dynamically, and each bidder is interested in winning one item between his arrival and departure. Our goal is to design truthful mechanisms that maximize the welfare, the sum of the utilities of winning bidders.We first consider this problem under the assumption that the private information for each bidder is his value for getting an item. In this model constant-competitive mechanisms are known, but we observe that these mechanisms suffer from the following disadvantage: a bidder might learn his payment only when he departs. We argue that these mechanism are essentially unusable, because they impose several seemingly undesirable requirements on any implementation of the mechanisms.To crystalize these issues, we define the notions of prompt and tardy mechanisms. We present two prompt truthful mechanisms -one deterministic and the other randomized, that guarantee a constant competitive ratio. We also prove that our deterministic mechanism is optimal for this setting.We then study a model in which both the value and the departure time are private information. While in the deterministic setting only a trivial competitive ratio can be guaranteed, we use randomization to obtain a prompt truthful Θ( 1 log m )-competitive mechanism. Finally, we show that no truthful randomized mechanism can achieve a ratio better than 1 2 in this model.

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“…Specifically, we use the random-sampling method introduced in [6] in order to be able to "guess" the departure time of each bidder. This mechanism is also a prompt mechanism.…”

Section: Theoremmentioning

confidence: 99%

Section: Proofmentioning

confidence: 99%

Prompt Mechanisms for Online Auctions

Cole

Dobzinski

Fleischer

2008

Algorithmic Game Theory

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“…Indeed if the goal is revenue maximization, the auctioneer must resort to other means. In particular, under the (realistic) assumption that the auctioneer has no prior information about the distribution of agent valuations, maximizing revenue while simultaneously ensuring strategyproofness requires the use of randomized auctions [19]. Such auctions advocate randomly sampling agents to determine a good revenue maximizing price.…”

Section: Strategyproof Approximately Optimal Channel Allocationmentioning

confidence: 99%

Strategyproof Wireless Spectrum Auctions with Interference

Gopinathan

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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Abstract-Wireless spectrum is a regulated resource, whose control and usage is regulated by government agencies. The allocation of spectrum to interested parties is usually conducted through auctions, and are an important source of income for these regulatory agencies. However, previous spectrum auction design fail to take into consideration the effect of interference, which can adversely affect the truthfulness of an auction. In this paper, we explicitly consider interference effects, and design truthful auctions for maximizing social welfare. Since the spectrum allocation problem is NP-Hard, we first show how to compute an approximate spectrum allocation scheme that is within a constant factor of the optimal solution, under certain simplifying assumptions on the interference graph. We then proceed to make this scheme strategyproof by tailoring a payment scheme based on the idea of minimum bids. A naive method to compute such a payment scheme requires O(n) iterations of the spectrum allocation algorithm, where n is the number of bidders. We show how to reduce the complexity to O(1) iterations instead. We conclude by discussing possible directions for future research in this area.

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“…The next challenge is to design a mechanism to elicit user valuations truthfully. We focus on the more realistic prior-free setting [13], when no information on the distribution of user valuations is known a priori. We first prepare a new revenue extraction procedure tailored to the layered multicast setting.…”

Section: Introductionmentioning

confidence: 99%

“…We first prepare a new revenue extraction procedure tailored to the layered multicast setting. This procedure, coupled with the revenue-maximization pricing algorithm, are used as ingredients in a strategyproof, randomized auction first proposed by Goldberg et al [13]. We model the auction using discrete martingales [14], and show that one can achieve a δ-fraction of the optimal revenue with probability at least 1 − 2 exp(−…”

Section: Introductionmentioning

confidence: 99%

Strategyproof Mechanisms for Content Delivery via Layered Multicast

Gopinathan¹,

Li²

2011

Networking 2011

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Abstract. Layered multicast exploits the heterogeneity of user capacities, making it ideal for delivering content such as media streams over the Internet. In order to maximize either its revenue or the total utility of users, content providers employing layered multicast need to carefully choose a routing, layer allocation and pricing scheme. We study algorithms and mechanisms for achieving either goal from a theoretical perspective. When the goal is maximizing social welfare, we prove that the problem is NP-hard, and provide a simple 3-approximation algorithm. We next tailor a payment scheme based on the idea of critical bids to derive a truthful mechanism that achieves a constant fraction of the optimal social welfare. When the goal is revenue maximization, we first design an algorithm that computes the revenue-maximizing layer pricing scheme, assuming truthful valuation reports. This algorithm, coupled with a new revenue extraction procedure for layered multicast, is used to design a randomized, strategyproof auction that elicits truthful reports. Employing discrete martingales to model the auction, we show that a constant fraction of the optimal revenue can be guaranteed with high probability. Finally, we study the efficacy of our algorithms via simulations.

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Competitive auctions

Cited by 239 publications

References 28 publications

Prompt Mechanisms for Online Auctions

Prompt Mechanisms for Online Auctions

Strategyproof Wireless Spectrum Auctions with Interference

Strategyproof Mechanisms for Content Delivery via Layered Multicast

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