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93
Distributed Algorithmic Mechanism Design: Recent Results and Future Directions
, 2002
"... Distributed Algorithmic Mechanism Design (DAMD) combines theoretical computer science’s traditional focus on computational tractability with its more recent interest in incentive compatibility and distributed computing. The Internet’s decentralized nature, in which distributed computation and autono ..."
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Cited by 283 (24 self)
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Distributed Algorithmic Mechanism Design (DAMD) combines theoretical computer science’s traditional focus on computational tractability with its more recent interest in incentive compatibility and distributed computing. The Internet’s decentralized nature, in which distributed computation and autonomous agents prevail, makes DAMD a very natural approach for many Internet problems. This paper first outlines the basics of DAMD and then reviews previous DAMD results on multicast cost sharing and interdomain routing. The remainder of the paper describes several promising research directions and poses some specific open problems.
A BGPbased Mechanism for LowestCost Routing
, 2002
"... The routing of traffic between... this paper, we address the problem of interdomain routing from a mechanismdesign point of view. The application of mechanismdesign principles to the study of routing is the subject of earlier work by Nisan and Ronen [15] and Hershberger and Suri [11]. In this pape ..."
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Cited by 268 (16 self)
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The routing of traffic between... this paper, we address the problem of interdomain routing from a mechanismdesign point of view. The application of mechanismdesign principles to the study of routing is the subject of earlier work by Nisan and Ronen [15] and Hershberger and Suri [11]. In this paper, we formulate and solve a version of the routingmechanism design problem that is different from the previously studied version in three ways that make it more accurately reflective of realworld interdomain routing: (1) we treat the nodes as strategic agents, rather than the links; (2) our mechanism computes lowestcost routes for all sourcedestination pairs and payments for transit nodes on all of the routes (rather than computing routes and payments for only one sourcedestination pair at a time, as is done in [15,11]); (3) we show how to compute our mechanism with a distributed algorithm that is a straightforward extension to BGP and causes only modest increases in routingtable size and convergence time (in contrast with the centralized algorithms used in [15,11]). This approach of using an existing protocol as a substrate for distributed computation may prove useful in future development of Internet algorithms generally, not only for routing or pricing problems. Our design and analysis of a strategyproof, BGPbased routing mechanism provides a new, promising direction in distributed algorithmic mechanism design, which has heretofore been focused mainly on multicast cost sharing.
On ProfitMaximizing Envyfree Pricing
"... We study the problem of pricing items for sale to consumers so as to maximize the seller’s revenue. We assume that for each consumer, we know the maximum amount he would be willing to pay for each bundle of items, and want to find pricings of the items with corresponding allocations that maximize se ..."
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Cited by 122 (12 self)
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We study the problem of pricing items for sale to consumers so as to maximize the seller’s revenue. We assume that for each consumer, we know the maximum amount he would be willing to pay for each bundle of items, and want to find pricings of the items with corresponding allocations that maximize seller profit and at the same time are envyfree, which is a natural fairness criterion requiring that consumers are maximally happy with the outcome they receive given the pricing. We study this problem for two important classes of inputs: unit demand consumers, who want to buy at most one item from among a selection they are interested in, and singleminded consumers, who want to buy one particular subset, but only if they can afford it. We show that computing envyfree prices to maximize the seller’s revenue is APXhard in both of these cases, and give a logarithmic approximation algorithm for them. For several interesting special cases, we derive polynomialtime algorithms. Furthermore, we investigate some connections with the corresponding mechanism design problem, in which the consumer’s preferences are private values: for this case, we give a logcompetitive truthful mechanism.
Competitive Auctions
"... We study a class of singleround, sealedbid auctions for an item in unlimited supply, such as adigital good. We introduce the notion of competitive auctions. A competitive auction is truthful (i.e., encourages bidders to bid their true valuations) and on all inputs yields profit that is withina co ..."
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Cited by 113 (11 self)
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We study a class of singleround, sealedbid auctions for an item in unlimited supply, such as adigital good. We introduce the notion of competitive auctions. A competitive auction is truthful (i.e., encourages bidders to bid their true valuations) and on all inputs yields profit that is withina constant factor of the profit of the optimal single sale price. We justify the use of optimal single price profit as a benchmark for evaluating a competitive auctions profit. We exhibitseveral randomized competitive auctions and show that there is no symmetric deterministic competitive auction. Our results extend to bounded supply markets, for which we also givecompetitive auctions.
An Approximate Truthful Mechanism for Combinatorial Auctions with Single Parameter Agents
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Truthful randomized mechanisms for combinatorial auctions
 IN STOC
, 2006
"... We design two computationallyefficient incentivecompatible mechanisms for combinatorial auctions with general bidder preferences. Both mechanisms are randomized, and are incentivecompatible in the universal sense. This is in contrast to recent previous work that only addresses the weaker notion o ..."
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Cited by 105 (17 self)
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We design two computationallyefficient incentivecompatible mechanisms for combinatorial auctions with general bidder preferences. Both mechanisms are randomized, and are incentivecompatible in the universal sense. This is in contrast to recent previous work that only addresses the weaker notion of incentive compatibility in expectation. The first mechanism obtains an O(pm)approximation of the optimal social welfare for arbitrary bidder valuations  this is the best approximation possible in polynomial time. The second one obtains an O(log2 m) approximation for a subclass of bidder valuations that includes all submodular bidders. This improves over the best previously obtained incentivecompatible mechanism for this class which only provides an O(pm)approximation.
Knapsack auctions
"... We consider a game theoretic knapsack problem that has application to auctions for selling advertisements on Internet search engines. Consider n agents each wishing to place an object in the knapsack. Each agent has a private valuation for having their object in the knapsack and each object has a pu ..."
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Cited by 76 (13 self)
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We consider a game theoretic knapsack problem that has application to auctions for selling advertisements on Internet search engines. Consider n agents each wishing to place an object in the knapsack. Each agent has a private valuation for having their object in the knapsack and each object has a publicly known size. For this setting, we consider the design of auctions in which agents have an incentive to truthfully reveal their private valuations. Following the framework of Goldberg et al. [10], we look to design an auction that obtains a constant fraction of the profit obtainable by a natural optimal pricing algorithm that knows the agents ’ valuations and object sizes. We give an auction that obtains a constant factor approximation in the nontrivial special case where the knapsack has unlimited capacity. We then reduce the limited capacity version of the problem to the unlimited capacity version via an approximately efficient auction (i.e., one that maximizes the social welfare). This reduction follows from generalizable principles.
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 70 (6 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)
The Value of Knowing a Demand Curve: Bounds on Regret for Online PostedPrice Auctions
 In Proc. of the 44nd IEEE Symp. on Foundations of Computer Science
, 2003
"... We consider the revenuemaximization problem for a seller with an unlimited supply of identical goods, interacting sequentially with a population of n buyers through an online postedprice auction mechanism, a paradigm which is frequently available to vendors selling goods over the Internet. For e ..."
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Cited by 67 (7 self)
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We consider the revenuemaximization problem for a seller with an unlimited supply of identical goods, interacting sequentially with a population of n buyers through an online postedprice auction mechanism, a paradigm which is frequently available to vendors selling goods over the Internet. For each buyer, the seller names a price between 0 and 1; the buyer decides whether or not to buy the item at the specified price, based on her privatelyheld valuation. The price offered is allowed to vary as the auction proceeds, as the seller gains information from interactions with the earlier buyers. The additive regret of a pricing strategy is defined to be the difference between the strategy’s expected revenue and the revenue derived from the optimal fixedprice strategy. In the case where buyers ’ valuations are independent samples from a fixed probability distribution (usually specified by a demand curve), one can interpret the regret as specifying how much the seller should be willing to pay for knowledge of the demand curve from which buyers ’ valuations are sampled. The answer to the problem depends on what assumptions one makes about the buyers ’ valuations. We consider three such assumptions: that the valuations are all equal to some unknown number p, that they are independent samples from an unknown probabilility distribution, or that they are chosen by an oblivious adversary. In each case, we derive upper and lower bounds on regret which match within a factor of logn; the bounds match up to a constant factor in the case of identical valuations.
Frugality in Path Auctions
 In Proceedings of the 15th Annual ACMSIAM Symposium on Discrete Algorithms
, 2003
"... We consider the problem of picking (buying) an inexpensive s t path in a graph where edges are owned by independent (selfish) agents, and the cost of an edge is known to its owner only. We study the problem of finding frugal mechanisms for this task, i.e. we investigate the payments the buyer m ..."
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Cited by 63 (2 self)
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We consider the problem of picking (buying) an inexpensive s t path in a graph where edges are owned by independent (selfish) agents, and the cost of an edge is known to its owner only. We study the problem of finding frugal mechanisms for this task, i.e. we investigate the payments the buyer must make in order to buy a path.