Results 1  10
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137
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 108 (19 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.
Approximate Mechanism Design Without Money
, 2009
"... The literature on algorithmic mechanism design is mostly concerned with gametheoretic versions of optimization problems to which standard economic moneybased mechanisms cannot be applied efficiently. Recent years have seen the design of various truthful approximation mechanisms that rely on enforc ..."
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Cited by 68 (19 self)
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The literature on algorithmic mechanism design is mostly concerned with gametheoretic versions of optimization problems to which standard economic moneybased mechanisms cannot be applied efficiently. Recent years have seen the design of various truthful approximation mechanisms that rely on enforcing payments. In this paper, we advocate the reconsideration of highly structured optimization problems in the context of mechanism design. We explicitly argue for the first time that, in such domains, approximation can be leveraged to obtain truthfulness without resorting to payments. This stands in contrast to previous work where payments are ubiquitous, and (more often than not) approximation is a necessary evil that is required to circumvent computational complexity. We present a case study in approximate mechanism design without money. In our basic setting agents are located on the real line and the mechanism must select the location of a public facility; the cost of an agent is its distance to the facility. We establish tight upper and lower bounds for the approximation ratio given by strategyproof mechanisms without payments, with respect to both deterministic and randomized mechanisms, under two objective functions: the social cost, and the maximum cost. We then extend our results in two natural directions: a domain where two facilities must be located, and a domain where each agent controls multiple locations.
Truthful mechanism design for multidimensional scheduling via cycle monotonicity
 In Proceedings 8th ACM Conference on Electronic Commerce (EC
, 2007
"... We consider the problem of makespan minimization on m unrelated machines in the context of algorithmic mechanism design, where the machines are the strategic players. This is a multidimensional scheduling domain, and the only known positive results for makespan minimization in such a domain are O(m) ..."
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Cited by 50 (12 self)
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We consider the problem of makespan minimization on m unrelated machines in the context of algorithmic mechanism design, where the machines are the strategic players. This is a multidimensional scheduling domain, and the only known positive results for makespan minimization in such a domain are O(m)approximation truthful mechanisms [22, 20]. We study a wellmotivated special case of this problem, where the processing time of a job on each machine may either be “low ” or “high”, and the low and high values are public and jobdependent. This preserves the multidimensionality of the domain, and generalizes the restrictedmachines (i.e., {pj, ∞}) setting in scheduling. We give a general technique to convert any capproximation algorithm to a 3capproximation truthfulinexpectation mechanism. This is one of the few known results that shows how to export approximation
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 (6 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.
Mechanisms for MultiUnit Auctions
 IN PROCEEDINGS OF THE ACM CONFERENCE ON ELECTRONIC COMMERCE (EC
, 2007
"... We present an incentivecompatible polynomialtime approximation scheme for multiunit auctions with general kminded player valuations. The mechanism fully optimizes over an appropriately chosen subrange of possible allocations and then uses VCG payments over this subrange. We show that obtaining ..."
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Cited by 37 (3 self)
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We present an incentivecompatible polynomialtime approximation scheme for multiunit auctions with general kminded player valuations. The mechanism fully optimizes over an appropriately chosen subrange of possible allocations and then uses VCG payments over this subrange. We show that obtaining a fully polynomialtime incentivecompatible approximation scheme, at least using VCG payments, is NPhard. For the case of valuations given by black boxes, we give a polynomialtime incentivecompatible 2approximation mechanism and show that no better is possible, at least using VCG payments.
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 35 (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
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 33 (9 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.
An impossibility result for truthful combinatorial auctions with submodular valuations
 In ACM STOC
, 2011
"... ar ..."
Strategyproof Auctions for Balancing Social Welfare and Fairness
 in Secondary Spectrum Markets,” in Proc. IEEE INFOCOM 2011, April 2011. et al.: DESIGNING TWODIMENSIONAL SPECTRUM AUCTIONS FOR MOBILE SECONDARY USERS 613
"... Abstract—Secondary spectrum access is emerging as a promising approach for mitigating the spectrum scarcity in wireless networks. Coordinated spectrum access for secondary users can be achieved using periodic spectrum auctions. Recent studies on such auction design mostly neglect the repeating natur ..."
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Cited by 32 (10 self)
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Abstract—Secondary spectrum access is emerging as a promising approach for mitigating the spectrum scarcity in wireless networks. Coordinated spectrum access for secondary users can be achieved using periodic spectrum auctions. Recent studies on such auction design mostly neglect the repeating nature of such auctions, and focus on greedily maximizing social welfare. Such auctions can cause subsets of users to experience starvation in the long run, reducing their incentive to continue participating in the auction. It is desirable to increase the diversity of users allocated spectrum in each auction round, so that a tradeoff between social welfare and fairness is maintained. We study truthful mechanisms towards this objective, for both local and global fairness criteria. For local fairness, we introduce randomization into the auction design, such that each user is guaranteed a minimum probability of being assigned spectrum. Computing an optimal, interferencefree spectrum allocation is NPHard; we present an approximate solution, and tailor a payment scheme to guarantee truthful bidding is a dominant strategy for all secondary users. For global fairness, we adopt the classic maxmin fairness criterion. We tailor another auction by applying linear programming techniques for striking the balance between social welfare and maxmin fairness, and for finding feasible channel allocations. In particular, a pair of primal and dual linear programs are utilized to guide the probabilistic selection of feasible allocations towards a desired tradeoff in expectation. I.
Setting lower bounds on truthfulness
 In Proceedings of the Eighteenth Annual ACMSIAM Symposium on Discrete Algorithms (SODA
, 2007
"... We present and discuss general techniques for proving inapproximability results for truthful mechanisms. We make use of these techniques to prove lower bounds on the approximability of several nonutilitarian multiparameter problems. In particular, we demonstrate the strength of our techniques by e ..."
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Cited by 30 (4 self)
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We present and discuss general techniques for proving inapproximability results for truthful mechanisms. We make use of these techniques to prove lower bounds on the approximability of several nonutilitarian multiparameter problems. In particular, we demonstrate the strength of our techniques by exhibiting a lower bound of 2 − 1 m for the scheduling problem with unrelated machines (formulated as a mechanism design problem in the seminal paper of Nisan and Ronen on Algorithmic Mechanism Design). Our lower bound applies to truthful randomized mechanisms (disregarding any computational assumptions on the running time of these mechanisms). Moreover, it holds even for the weaker notion of truthfulness for randomized mechanisms – i.e., truthfulness in expectation. This lower bound nearly matches the known 7 4 (randomized) truthful upper bound for the case of two machines (a nontruthful FPTAS exists). No lower bound for truthful randomized mechanisms in multiparameter settings was previously known. We show an application of our techniques to the workloadminimization problem in networks. We prove our lower bounds for this problem in the interdomain routing setting presented by Feigenbaum, Papadimitriou, Sami, and Shenker. Finally, we discuss several notions of nonutilitarian “fairness ” (MaxMin fairness, MinMax fairness, and envy minimization). We show how our techniques can be used to prove lower bounds for these notions.