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15
Competitive Auctions
, 2002
"... We study a class of singleround, sealedbid auctions for items in unlimited supply, such as digital goods. We introduce the notion of competitive auctions. A competitive auction is truthful (i.e., encourages buyers to bid their utility) and yields profit that is roughly within a constant factor of ..."
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Cited by 79 (11 self)
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We study a class of singleround, sealedbid auctions for items in unlimited supply, such as digital goods. We introduce the notion of competitive auctions. A competitive auction is truthful (i.e., encourages buyers to bid their utility) and yields profit that is roughly within a constant factor of the profit of optimal fixed pricing for all inputs. We justify the use of optimal fixed pricing as a benchmark for evaluating competitive auction profit. We show that several randomized auctions are truthful and competitive and that no truthful deterministic auction is competitive. Our results extend to bounded supply markets, for which we also get truthful and competitive auctions.
Efficient, strategyproof and almost budgetbalanced assignment
, 2007
"... Call a VickreyClarkeGroves (VCG) mechanism to assign p identical objects among n agents, feasible if cash transfers yield no deficit. The efficiency loss of such a mechanism is the worst (largest) ratio of the budget surplus to the efficient surplus, over all profiles of non negative valuations. T ..."
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Cited by 24 (3 self)
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Call a VickreyClarkeGroves (VCG) mechanism to assign p identical objects among n agents, feasible if cash transfers yield no deficit. The efficiency loss of such a mechanism is the worst (largest) ratio of the budget surplus to the efficient surplus, over all profiles of non negative valuations. The optimal (smallest) efficiency loss � L(n, p) satisfies is strictly smaller or strictly �L(n, p) ≤ �L(n, { n 4
Truthful and Competitive Double Auctions
, 2002
"... In this paper we consider the problem of designing a mechanism for double auctions where bidders each bid to buy or sell one unit of a single commodity. We assume that each bidder's utility value for the item is private to them and we focus on truthful mechanisms, ones were the bidders' optimal stra ..."
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Cited by 23 (8 self)
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In this paper we consider the problem of designing a mechanism for double auctions where bidders each bid to buy or sell one unit of a single commodity. We assume that each bidder's utility value for the item is private to them and we focus on truthful mechanisms, ones were the bidders' optimal strategy is to bid their true utility. The profit of the auctioneer is the difference between the total payments from buyers and total to the sellers. We aim to maximize this profit. We extend the competitive analysis framework of basic auctions [9] and give an upper bound on the profit of any truthful double auction. We then reduce the competitive double auction problem to basic auctions by showing that any competitive basic auction can be converted into a competitive double auction with a competitive ratio of twice that of the basic auction.
On the Competitive Ratio of the Random Sampling Auction
 In Proc. 1st Workshop on Internet and Network Economics
, 2005
"... Abstract. We give a simple analysis of the competitive ratio of the random sampling auction from [10]. The random sampling auction was first shown to be worstcase competitive in [9] (with a bound of 7600 on its competitive ratio); our analysis improves the bound to 15. In support of the conjecture ..."
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Cited by 20 (6 self)
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Abstract. We give a simple analysis of the competitive ratio of the random sampling auction from [10]. The random sampling auction was first shown to be worstcase competitive in [9] (with a bound of 7600 on its competitive ratio); our analysis improves the bound to 15. In support of the conjecture that random sampling auction is in fact 4competitive, we show that on the equal revenue input, where any sale price gives the same revenue, random sampling is exactly a factor of four from optimal. 1 Introduction. Random sampling is the most prevalent technique for designing auctions to maximize the auctioneer’s profit when the bidders ’ valuations are a priori unknown [2–4, 7, 8, 10, 11]. The first and simplest application of random sampling to auctions is in the context of auctioning a digital good. 5 For this problem, the random
Limited and online supply and the Bayesian foundations of priorfree mechanism design
 In EC ’09
"... We study auctions for selling a limited supply of a single commodity in the case where the supply is known in advance and the case it is unknown and must be instead allocated in an online fashion. The latter variant was proposed by Mahdian and Saberi [12] as a model of an important phenomena in auct ..."
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Cited by 12 (4 self)
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We study auctions for selling a limited supply of a single commodity in the case where the supply is known in advance and the case it is unknown and must be instead allocated in an online fashion. The latter variant was proposed by Mahdian and Saberi [12] as a model of an important phenomena in auctions for selling Internet advertising: advertising impressions must be allocated as they arrive and the total quantity available is unknown in advance. We describe the Bayesian optimal mechanism for these variants and extend the random sampling auction of Goldberg et al. [8] to address the priorfree case.
On random sampling auctions for digital goods
, 2008
"... In the context of auctions for digital goods, an interesting Random Sampling Optimal Price auction (RSOP) has been proposed by Goldberg, Hartline and Wright; this leads to a truthful mechanism. Since random sampling is a popular approach for auctions that aims to maximize the seller’s revenue, this ..."
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Cited by 8 (0 self)
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In the context of auctions for digital goods, an interesting Random Sampling Optimal Price auction (RSOP) has been proposed by Goldberg, Hartline and Wright; this leads to a truthful mechanism. Since random sampling is a popular approach for auctions that aims to maximize the seller’s revenue, this method has been analyzed further by Feige, Flaxman, Hartline and Kleinberg, who have shown that it is 15competitive in the worst case – which is substantially better than the previously proved bounds but still far from the conjectured competitive ratio of 4. In this paper, we prove that RSOP is indeed 4competitive for a large class of instances in which the number λ of bidders receiving the item at the optimal uniform price, is at least 6. We also show that it is 4.68 competitive for the small class of remaining instances thus leaving a negligible gap between the lower and upper bound. Furthermore, we develop a robust version of RSOP – one in which the seller’s revenue is, with high probability, not much below its mean – when the above parameter λ grows large. We employ a mix of probabilistic techniques and dynamic programming to compute these bounds.
Information in Mechanism Design
 IN ADVANCES IN ECONOMICS AND ECONOMETRICS
, 2006
"... We survey the recent literature on the role of information in mechanism design. First, we discuss an emerging literature on the role of endogenous payo and strategic information for the design and the efficiency of the mechanism. We speci cally consider information management in the form of acquisit ..."
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Cited by 8 (2 self)
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We survey the recent literature on the role of information in mechanism design. First, we discuss an emerging literature on the role of endogenous payo and strategic information for the design and the efficiency of the mechanism. We speci cally consider information management in the form of acquisition of new information or disclosure of existing information. Second, we argue that in the presence of endogenous information, the robustness of the mechanism to the type space and higher order beliefs becomes a natural desideratum. We discuss recent approaches to robust mechanism design and robust implementation.
Mechanism Design via Consensus Estimates, Cross Checking, and Profit Extraction
, 2012
"... There is only one technique for priorfree optimal mechanism design that generalizes beyond the structurally benevolent setting of digital goods. This technique uses random sampling to estimate the distribution of agent values and then employs the Bayesian optimal mechanism for this estimated distri ..."
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Cited by 3 (2 self)
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There is only one technique for priorfree optimal mechanism design that generalizes beyond the structurally benevolent setting of digital goods. This technique uses random sampling to estimate the distribution of agent values and then employs the Bayesian optimal mechanism for this estimated distribution on the remaining players. Though quite general, even for digital goods, this random sampling auction has a complicated analysis and is known to be suboptimal. To overcome these issues we generalize the profit extraction and consensus techniques from [5] to structurally rich environments that include, e.g., singleminded combinatorial auctions.
Approximation in mechanism design
 American Economic Review
"... A mechanism gives a mapping between the actions of strategic agents and outcomes of the system. Equilibrium theory describes what outcomes will arise in the equilibrium of selfish agent play. Mechanism design then considers the optimization question of what mechanisms have good outcomes in equilibri ..."
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Cited by 2 (0 self)
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A mechanism gives a mapping between the actions of strategic agents and outcomes of the system. Equilibrium theory describes what outcomes will arise in the equilibrium of selfish agent play. Mechanism design then considers the optimization question of what mechanisms have good outcomes in equilibrium. Optimal mechanism design searches for the best of these mechanisms. The space of all mechanisms is rich and positive results for optimal mechanism design (a) identify a subclass of mechanisms from which an optimal mechanism can be drawn, (b) interpret the salient characteristics of this subclass, and (c) predict the mechanisms that arise in practice. This agenda has a rich and elegant history in the economic literature with many success stories. But what can a theory of mechanism design say (a) when the only subclass of mechanisms that contains all optimal mechanisms is the full class, (b) when analytical approaches fail to identify salient characteristics of optimal mechanisms, or (c) when the mechanisms in practice are not the ones predicted by optimal mechanism design? To address these and other issues I survey several results from the theory of approximation in mechanism design. A mechanism is a βapproximation in some setting if its objective performance is within a multiplicative factor of β of that of the optimal mechanism for the same setting. For example, a 2approximation obtains 50 % of the optimal performance. A subclass of mechanisms is a βapproximation if for every setting there is a mechanism in the subclass that is a βapproximation. Below I will motivate the perspective that a, for example, 2approximation can have important theoretical and practical consequences. As discussed, the class of all mechanisms is incredibly rich and there are environments, see,
Lectures on Optimal Mechanism Design
, 2005
"... These lecture notes cover the second third of the class CS364B, Topics in Algorithmic Game Theory, offered at Stanford University in the Fall 2005 term. They cover the topic of optimal mechanism design. As this is a traditional economic objective, we will review the Economics treatment of optimal me ..."
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Cited by 1 (0 self)
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These lecture notes cover the second third of the class CS364B, Topics in Algorithmic Game Theory, offered at Stanford University in the Fall 2005 term. They cover the topic of optimal mechanism design. As this is a traditional economic objective, we will review the Economics treatment of optimal mechanism design first before moving on to cover recent work on the problem from the theoretical computer science community. Prerequisites for reading these lecture notes are basic understanding of algorithms and complexity as well as elementary calculus and probability theory. I will also assume that the reader has access to the notes on the first third of this course which covers combinatorial auctions. Thanks to the students of CS364B, Ning Chen, and coinstructor Tim Roughgarden. Comments are welcome. 1