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57
Distributed Algorithmic Mechanism Design: Recent Results and Future Directions
 In Proceedings of the 6th International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications
, 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 239 (17 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 230 (17 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.
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 82 (15 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.
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 64 (3 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.
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)
Knapsack Auctions
 Proceedings of the Seventeenth Annual ACMSIAM Symposium on Discrete Algorithms (SODA
, 2006
"... 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 56 (9 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. 1
Mechanism Design via Machine Learning
 IN PROC. OF THE 46TH IEEE SYMP. ON FOUNDATIONS OF COMPUTER SCIENCE
, 2005
"... We use techniques from samplecomplexity in machine learning to reduce problems of incentivecompatible mechanism design to standard algorithmic questions, for a broad class of revenuemaximizing pricing problems. Our reductions imply that for these problems, given an optimal (or #approximation) al ..."
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Cited by 47 (11 self)
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We use techniques from samplecomplexity in machine learning to reduce problems of incentivecompatible mechanism design to standard algorithmic questions, for a broad class of revenuemaximizing pricing problems. Our reductions imply that for these problems, given an optimal (or #approximation) algorithm for the standard algorithmic problem, we can convert it into a (1 + #)approximation (or #(1 + #)approximation) for the incentivecompatible mechanism design problem, so long as the number of bidders is sufficiently large as a function of an appropriate measure of complexity of the comparison class of solutions. We apply these results to the problem of auctioning a digital good, to the attribute auction problem which includes a wide variety of discriminatory pricing problems, and to the problem of itempricing in unlimitedsupply combinatorial auctions. From a machine learning perspective, these settings present several challenges: in particular, the loss function is discontinuous and asymmetric, and the range of bidders' valuations may be large.
NearOptimal Online Auctions
 In Proceedings of the 16th Annual ACMSIAM Symposium on Discrete Algorithms
, 2005
"... Abstract We consider the online auction problem proposed byBarYossef, Hildrum, and Wu [4] in which an auctioneer is selling identical items to bidders arriving one at atime. We give an auction that achieves a constant factor of the optimal profit less an O(h) additive loss term,where h is the value ..."
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Cited by 45 (11 self)
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Abstract We consider the online auction problem proposed byBarYossef, Hildrum, and Wu [4] in which an auctioneer is selling identical items to bidders arriving one at atime. We give an auction that achieves a constant factor of the optimal profit less an O(h) additive loss term,where h is the value of the highest bid. Furthermore,this auction does not require foreknowledge of the range of bidders ' valuations. On both counts, this answersopen questions from [4, 5]. We further improve on the results from [5] for the online postedprice problem by reducing their additive loss term from O(h log h log log h)to O(h log log h). Finally, we define the notion of an(offline) attribute auction for modeling the problem of auctioning items to consumers who are not apriori indistinguishable. We apply our online auction solution to achieve good bounds for the attribute auction problemwith 1dimensional attributes.
Strategyproof Costsharing Mechanisms for Set Cover and Facility Location Games
, 2003
"... this paper, we obtain strategyproof cost allocations for two fundamental games whose underlying optimization problems are NPhard, the set cover game and the facility location game. For the latter game, this is made possible by new approximation algorithms for the underlying optimization problem usi ..."
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Cited by 44 (0 self)
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this paper, we obtain strategyproof cost allocations for two fundamental games whose underlying optimization problems are NPhard, the set cover game and the facility location game. For the latter game, this is made possible by new approximation algorithms for the underlying optimization problem using the technique of dual fitting [7]. In retrospect, the natural greedy algorithm for the set cover problem (see [17]) can also analyzed using this technique  we utilize this viewpoint for handling the set cover game. The facility location game was studied in [9, 4], who left the open problem of obtaining a group strategyproof mechanism based on a constant factor approximation algorithm. Our paper partially answers this question. We give a strategyproof mechanism, but cannot achieve group strategyproofness. More recently, Pal and Tardos [15] have announced a 3approximately budget balanced crossmonotonic costsharing method for the facility location problem. This gives a group strategyproof mechanism for the facility location game that recovers 3 rd of the cost