Results 1  10
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104
Truthful approximation schemes for singleparameter agents
 In FOCS ’08
"... We present the first monotone randomized polynomialtime approximation scheme (PTAS) for minimizing the makespan of parallel related machines (QCmax), the paradigmatic problem in singleparameter algorithmic mechanism design. This result immediately gives a polynomialtime, truthful (in expectation ..."
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Cited by 42 (9 self)
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We present the first monotone randomized polynomialtime approximation scheme (PTAS) for minimizing the makespan of parallel related machines (QCmax), the paradigmatic problem in singleparameter algorithmic mechanism design. This result immediately gives a polynomialtime, truthful (in expectation) mechanism whose approximation guarantee attains the bestpossible one for all polynomialtime algorithms (assuming P ̸ = NP). Our algorithmic techniques are flexible and also yield a monotone deterministic quasiPTAS for QCmax and a monotone randomized PTAS for maxmin scheduling on related machines. 1
Item Pricing for Revenue Maximization
"... We consider the problem of pricing n items to maximize revenue when faced with a series of unknown buyers with complex preferences, and show that a simple pricing scheme achieves surprisingly strong guarantees. We show that in the unlimited supply setting, a random single price achieves expected rev ..."
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Cited by 41 (4 self)
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We consider the problem of pricing n items to maximize revenue when faced with a series of unknown buyers with complex preferences, and show that a simple pricing scheme achieves surprisingly strong guarantees. We show that in the unlimited supply setting, a random single price achieves expected revenue within a logarithmic factor of the total social welfare for customers with general valuation functions, which may not even necessarily be monotone. This generalizes work of Guruswami et. al [18], who show a logarithmic factor for only the special cases of singleminded and unitdemand customers. In the limited supply setting, we show that for subadditive valuations, a random single price achieves revenue within a factor of 2 O( √ log n log log n) of the total social welfare, i.e., the optimal revenue the seller could hope to extract even if the seller could price each bundle differently for every buyer. This is the best approximation known for any item pricing scheme for subadditive (or even submodular) valuations, even using multiple prices. We complement this result with a lower bound showing a sequence of subadditive (in fact, XOS) buyers for which any single price has approximation ratio 2 Ω(log1/4 n), thus showing that single price schemes cannot achieve a polylogarithmic ratio. This lower bound demonstrates a clear distinction between revenue maximization and social welfare maximization in this setting, for which [12, 10] show that a fixed price achieves a logarithmic approximation in the case of XOS [12], and more generally subadditive [10], customers.
Incentive compatible regression learning.
 In Proceedings of the 19th Annual ACMSIAM Symposium on Discrete Algorithms (SODA),
, 2008
"... Abstract We initiate the study of incentives in a general machine learning framework. We focus on a gametheoretic regression learning setting where private information is elicited from multiple agents with different, possibly conflicting, views on how to label the points of an input space. This co ..."
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Cited by 37 (13 self)
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Abstract We initiate the study of incentives in a general machine learning framework. We focus on a gametheoretic regression learning setting where private information is elicited from multiple agents with different, possibly conflicting, views on how to label the points of an input space. This conflict potentially gives rise to untruthfulness on the part of the agents. In the restricted but important case when every agent cares about a single point, and under mild assumptions, we show that agents are motivated to tell the truth. In a more general setting, we study the power and limitations of mechanisms without payments. We finally establish that, in the general setting, the VCG mechanism goes a long way in guaranteeing truthfulness and economic efficiency.
Game Theory Meets Network Security and Privacy
"... This survey provides a structured and comprehensive overview of the research contributions that analyze and solve security and privacy problems in computer networks by gametheoretic approaches. A selected set of works are presented to highlight the application of game theory in order to address dif ..."
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Cited by 35 (5 self)
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This survey provides a structured and comprehensive overview of the research contributions that analyze and solve security and privacy problems in computer networks by gametheoretic approaches. A selected set of works are presented to highlight the application of game theory in order to address different forms of security and privacy problems in computer networks and mobile applications. The presented works are classified into six main categories based on their topics: security of the physical and MAC layers, application layer security in mobile networks, intrusion detection systems, anonymity and privacy, economics of network security, and cryptography. In each category, security problems, players, and game models are identified and the main results of selected works, such as equilibrium analysis and security mechanism designs are summarized. In addition, a discussion on advantages, drawbacks, and the future direction of using game theory in this field is provided. In this survey, we aim to provide a better understanding of the different research approaches for applying game theory to network security. This survey can also help researchers from various fields develop gametheoretic solutions to current and emerging security problems in computer networking. Categories and Subject Descriptors: C.2.0 [ComputerCommunication Networks]: General—
From convex optimization to randomized mechanisms: Toward optimal combinatorial auctions
 In Proceedings of the 43rd annual ACM Symposium on Theory of Computing (STOC
, 2011
"... We design an expected polynomialtime, truthfulinexpectation, (1 − 1/e)approximation mechanism for welfare maximization in a fundamental class of combinatorial auctions. Our results apply to bidders with valuations that are matroid rank sums (MRS), which encompass mostconcreteexamplesofsubmodular ..."
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Cited by 34 (11 self)
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We design an expected polynomialtime, truthfulinexpectation, (1 − 1/e)approximation mechanism for welfare maximization in a fundamental class of combinatorial auctions. Our results apply to bidders with valuations that are matroid rank sums (MRS), which encompass mostconcreteexamplesofsubmodularfunctionsstudiedinthiscontext,includingcoveragefunctions, matroid weightedrank functions, and convex combinations thereof. Our approximation factor is the best possible, even for known and explicitly given coverage valuations, assuming P ̸ = NP. Ours is the first truthfulinexpectation and polynomialtime mechanism to achieve a constantfactor approximation for an NPhard welfare maximization problem in combinatorial auctions with heterogeneous goods and restricted valuations. Our mechanism is an instantiation of a new framework for designing approximation mechanisms based on randomized rounding algorithms. A typical such algorithm first optimizes over a fractional relaxation of the original problem, and then randomly rounds the fractional solution to an integral one. With rare exceptions, such algorithms cannot be converted into truthful mechanisms. The highlevel idea of our mechanism design framework is to optimize directly
An impossibility result for truthful combinatorial auctions with submodular valuations
 In ACM STOC
, 2011
"... ar ..."
On the power of randomization in algorithmic mechanism design
"... In many settings the power of truthful mechanisms is severely bounded. In this paper we use randomization to overcome this problem. In particular, we construct an FPTAS for multiunit auctions that is truthful in expectation, whereas there is evidence that no polynomialtime truthful deterministic m ..."
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Cited by 32 (8 self)
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In many settings the power of truthful mechanisms is severely bounded. In this paper we use randomization to overcome this problem. In particular, we construct an FPTAS for multiunit auctions that is truthful in expectation, whereas there is evidence that no polynomialtime truthful deterministic mechanism provides an approximation ratio better than 2. We also show for the first time that truthful in expectation polynomialtime mechanisms are provably stronger than polynomialtime universally truthful mechanisms. Specifically, we show that there is a setting in which: (1) there is a nonpolynomial time truthful mechanism that always outputs the optimal solution, and that (2) no universally truthful randomized mechanism can provide an approximation ratio better than 2 in polynomial time, but (3) an FPTAS that is truthful in expectation exists.
A Scheduling Approach to Coalitional Manipulation
"... The coalitional manipulation problem is one of the central problems in computational social choice. In this paper we focus on solving the problem under the important family of positional scoring rules, in an approximate sense that was advocated by Zuckerman et al. [SODA 2008]. Our main result is a p ..."
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Cited by 31 (12 self)
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The coalitional manipulation problem is one of the central problems in computational social choice. In this paper we focus on solving the problem under the important family of positional scoring rules, in an approximate sense that was advocated by Zuckerman et al. [SODA 2008]. Our main result is a polynomialtime algorithm with (roughly speaking) the following theoretical guarantee: given a manipulable instance with m alternatives the algorithm finds a successful manipulation with at most m − 2 additional manipulators. Our technique is based on a reduction to the scheduling problem known as QpmtnCmax, along with a novel rounding procedure. We demonstrate that our analysis is tight by establishing a new type of integrality gap. We also resolve a known open question in computational social choice by showing that the coalitional manipulation problem remains (strongly) NPcomplete for positional scoring rules even when votes are unweighted. Finally, we discuss the implications of our results with respect to the question: “Is there a prominent voting rule that is usually hard to manipulate?”
On the approximability of Dodgson and Young elections
, 2008
"... The voting rules proposed by Dodgson and Young are both designed to find the alternative closest to being a Condorcet winner, according to two different notions of proximity; the score of a given alternative is known to be hard to compute under either rule. In this paper, we put forward two algorith ..."
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Cited by 25 (8 self)
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The voting rules proposed by Dodgson and Young are both designed to find the alternative closest to being a Condorcet winner, according to two different notions of proximity; the score of a given alternative is known to be hard to compute under either rule. In this paper, we put forward two algorithms for approximating the Dodgson score: an LPbased randomized rounding algorithm and a deterministic greedy algorithm, both of which yield an O(log m) approximation ratio, where m is the number of alternatives; we observe that this result is asymptotically optimal, and further prove that our greedy algorithm is optimal up to a factor of 2, unless problems in N P have quasipolynomial time algorithms. Although the greedy algorithm is computationally superior, we argue that
BlackBox Randomized Reductions in Algorithmic Mechanism Design
"... We give the first blackbox reduction from arbitrary approximation algorithms to truthful approximation mechanisms for a nontrivial class of multiparameter problems. Specifically, we prove that every packing problem that admits an FPTAS also admits a truthfulinexpectation randomized mechanism th ..."
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Cited by 25 (5 self)
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We give the first blackbox reduction from arbitrary approximation algorithms to truthful approximation mechanisms for a nontrivial class of multiparameter problems. Specifically, we prove that every packing problem that admits an FPTAS also admits a truthfulinexpectation randomized mechanism that is an FPTAS. Our reduction makes novel use of smoothed analysis, by employing small perturbations as a tool in algorithmic mechanism design. We develop a “duality” between linear perturbations of the objective function of an optimization problem and of its feasible set, and use the “primal” and “dual” viewpoints to prove the running time bound and the truthfulness guarantee, respectively, for our mechanism.