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67
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.
Adaptive LimitedSupply Online Auctions
 In Proceedings of the 5th ACM Conference on Electronic Commerce
, 2004
"... We study a limitedsupply online auction problem, in which an auctioneer has k goods to sell and bidders arrive and depart dynamically. We suppose that agent valuations are drawn independently from some unknown distribution and construct an adaptive auction that is nevertheless value and timestrat ..."
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Cited by 72 (16 self)
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We study a limitedsupply online auction problem, in which an auctioneer has k goods to sell and bidders arrive and depart dynamically. We suppose that agent valuations are drawn independently from some unknown distribution and construct an adaptive auction that is nevertheless value and timestrategyproof. For the k = 1 problem we have a strategyproof variant on the classic secretary problem. We present a 4competitive (ecompetitive) strategyproof online algorithm with respect to offline Vickrey for revenue (efficiency) . We also show (in a model that slightly generalizes the assumption of independent valuations) that no mechanism can be better than 3/2competitive (2competitive) for revenue (efficiency). Our general approach considers a learning phase followed by an accepting phase, and is careful to handle incentive issues for agents that span the two phases. We extend to the k > 1 case, by deriving strategyproof mechanisms which are constantcompetitive for revenue and efficiency. Finally, we present some strategyproof competitive algorithms for the case in which adversary uses a distribution known to the mechanism.
An MDPBased Approach to Online Mechanism Design
 In Proc. 17th Annual Conf. on Neural Information Processing Systems (NIPS’03
, 2003
"... Online mechanism design (MD) considers the problem of providing incentives to implement desired systemwide outcomes in systems with selfinterested agents that arrive and depart dynamically. Agents can choose to... ..."
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Cited by 60 (19 self)
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Online mechanism design (MD) considers the problem of providing incentives to implement desired systemwide outcomes in systems with selfinterested agents that arrive and depart dynamically. Agents can choose to...
Online Learning in Online Auctions
, 2003
"... ding truthfully and setting b i = v i . As shown in that paper, this condition # Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, Email: avrim@cs.cmu.edu + Strategic Planning and Optimization Team, Amazon.com, Seattle, WA, Email: vijayk@amazon.com # Department of Compute ..."
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Cited by 58 (5 self)
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ding truthfully and setting b i = v i . As shown in that paper, this condition # Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, Email: avrim@cs.cmu.edu + Strategic Planning and Optimization Team, Amazon.com, Seattle, WA, Email: vijayk@amazon.com # Department of Computer Science, University of Texas at Austin, Austin, TX. This work was done while the author was at IBM India Research Lab, New Delhi, India. Email: atri@cs.utexas.edu Computer Science Division, University of California at Berkeley, Berkeley, CA, Email: felix@cs.berkeley.edu is equivalent to the condition that each s i depends only on the first i 1 bids, and not on the ith bid. Hence, the auction mechanism is essentially trying to guess the ith valuation, based on the first i 1 valuations. As in previous papers [3, 5, 6], we will use competitive analysis to analyze the performance of any given auction. Hence, we are interested in the worstcase ratio (over all sequences of valuations)
IncentiveCompatible Online Auctions for Digital Goods
 In Proc. 13th Symp. on Discrete Alg. ACM/SIAM
, 2002
"... Goldberg et al. [6] recently began the study of incentivecompatible auctions for digital goods, that is, goods which are available in unlimited supply. Many digital goods, however, such as books, music, and software, are sold continuously, rather than in a single round, as is the case for traditiona ..."
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Cited by 45 (3 self)
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Goldberg et al. [6] recently began the study of incentivecompatible auctions for digital goods, that is, goods which are available in unlimited supply. Many digital goods, however, such as books, music, and software, are sold continuously, rather than in a single round, as is the case for traditional auctions. Hence, it is important to consider what happens in the online version of such auctions. We de ne a model for online auctions for digital goods, and within this model, we examine auctions in which bidders have an incentive to bid their true valuations, that is, incentivecompatible auctions. Since the best oine auctions achieve revenue comparable to the revenue of the optimal xed pricing scheme, we use the latter as our benchmark. We show that deterministic auctions perform poorly relative to this benchmark, but we give a randomized auction which is within a factor O(exp( p log log h)) of the benchmark, where h is the ratio between the highest and lowest bids. As part of this result, we also give a new oine auction, which improves upon the previously best auction in a certain class of auctions for digital goods. We also give lower bounds for both randomized and deterministic online auctions for digital goods. 1
Pricing WiFi at Starbucks  Issues in Online Mechanism Design
 In Fourth ACM Conf. on Electronic Commerce (EC’03
, 2003
"... We consider the problem of designing mechanisms for online problems in which agents arrive over time and the mechanism is unaware of the agent until the agent announces her arrival. Problems of this sort are becoming extremely common particularly in a wide variety of problems involving wireless n ..."
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Cited by 45 (11 self)
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We consider the problem of designing mechanisms for online problems in which agents arrive over time and the mechanism is unaware of the agent until the agent announces her arrival. Problems of this sort are becoming extremely common particularly in a wide variety of problems involving wireless networking.
Approximation and Collusion in Multicast Cost Sharing
, 2004
"... in Proceedings of the 3rd ACM Conference on Electronic Commerce, Tampa FL, October 2001. This work was supported by the DoD University Research Initiative (URI) program administered by the Oce of Naval Research under Grant N000140110795. ..."
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Cited by 43 (4 self)
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in Proceedings of the 3rd ACM Conference on Electronic Commerce, Tampa FL, October 2001. This work was supported by the DoD University Research Initiative (URI) program administered by the Oce of Naval Research under Grant N000140110795.
Online Algorithms for Market Clearing
, 2002
"... In this paper we study the problem of online market clearing where there is one commodity in the market being bought and sold by multiple buyers and sellers whose bids arrive and expire at different times. The auctioneer is faced with an online clearing problem of deciding which buy and sell bids to ..."
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Cited by 38 (4 self)
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In this paper we study the problem of online market clearing where there is one commodity in the market being bought and sold by multiple buyers and sellers whose bids arrive and expire at different times. The auctioneer is faced with an online clearing problem of deciding which buy and sell bids to match without knowing what bids will arrive in the future. For maximizing profit, we present a (randomized) online algorithm with a competitive ratio of ln(p max min )+1, when bids are in a range [p min ,p max ], which we show is the best possible. A simpler algorithm has a ratio twice this, and can be used even if expiration times are not known. For maximizing the number of trades, we present a simple greedy algorithm that achieves a factor of 2 competitive ratio if no moneylosing trades are allowed. Interestingly, we show that if the online algorithm is allowed to subsidize matches  match moneylosing pairs if it has already collected enough money from previous pairs to pay for them  then it can be 1competitive with respect to the optimal offline algorithm that is not allowed subsidy. That is, the ability to subsidize is at least as valuable as knowing the future. We also consider the objectives of maximizing buy or sell volume, and present algorithms that achieve a competitive ratio of 2(ln(p max /p min ) + 1), or ln(p max /p min ) + 1 if the online algorithm is allowed subsidization. We show the latter is the best possible competitive ratio for this setting. For social welfare maximization we also obtain an optimal competitive ratio, which is below ln(p max /p min ). We present all of these results as corollaries of theorems on online matching in an incomplete interval graph.
Reducing truthtelling online mechanisms to online optimization
 In STOC
, 2003
"... We describe a general technique for converting an online algorithm�to a truthtelling mechanism. We require that the original online competitive algorithm has certain “niceness ” properties in that actions on future requests are independent of the actual value of requests which were accepted (though ..."
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Cited by 32 (1 self)
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We describe a general technique for converting an online algorithm�to a truthtelling mechanism. We require that the original online competitive algorithm has certain “niceness ” properties in that actions on future requests are independent of the actual value of requests which were accepted (though these actions will of course depend upon the set of accepted requests). Under these conditions, we are able to give an online truth telling mechanism (where the values of requests are given by bids which may not accurately represent the valuation of the requesters) such that our total profit is within ��of the optimum offline profit obtained by an omniscient algorithm (one which knows the true valuations of the users). Here�is the competitive ratio of�for the optimization version of the problem, and�is the ratio of the maximum to minimum valuation for a request. In general there is anÅ��lower bound on the ratio of worstcase profit for a truth telling mechanism when compared to the profit obtained by an omniscient algorithm, so this result is in some sense best possible. In addition, we prove that our construction is resilient against many forms of “cheating ” attempts, such as forming coalitions. We demonstrate applications of this result to several problems. We develop online truthtelling mechanisms for online routing and admission control of path or multicast requests, assuming large network capacities. Assuming the existance of an algorithm�for the optimization version of the problem, our techniques provide truthtelling mechanisms for general