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434
TRUST: A general framework for truthful double spectrum auctions
 in IEEE INFOCOM
, 2009
"... Abstract — We design truthful double spectrum auctions where multiple parties can trade spectrum based on their individual needs. Open, marketbased spectrum trading motivates existing spectrum owners (as sellers) to lease their selected idle spectrum to new spectrum users, and provides new users (a ..."
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Cited by 43 (3 self)
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Abstract — We design truthful double spectrum auctions where multiple parties can trade spectrum based on their individual needs. Open, marketbased spectrum trading motivates existing spectrum owners (as sellers) to lease their selected idle spectrum to new spectrum users, and provides new users (as buyers) the spectrum they desperately need. The most significant challenge is how to make the auction economicrobust (truthful in particular) while enabling spectrum reuse to improve spectrum utilization. Unfortunately, existing designs either do not consider spectrum reuse or become untruthful when applied to double spectrum auctions. We address this challenge by proposing TRUST, a general framework for truthful double spectrum auctions. TRUST takes as input any reusabilitydriven spectrum allocation algorithm, and applies a novel winner determination and pricing mechanism to achieve truthfulness and other economic properties while significantly improving spectrum utilization. To our best knowledge, TRUST is the first solution for truthful double spectrum auctions that enable spectrum reuse. Our results show that economic factors introduce a tradeoff between spectrum efficiency and economic robustness. TRUST makes an important contribution on enabling spectrum reuse to minimize such tradeoff. I.
Mdpop: Faithful distributed implementation of efficient social choice problems
 In AAMAS’06  Autonomous Agents and Multiagent Systems
, 2006
"... In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In this paper we model the social choice problem ..."
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Cited by 41 (15 self)
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In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In this paper we model the social choice problem as a distributed constraint optimization problem (DCOP), in which each agent can communicate with other agents that share an interest in one or more variables. Whereas existing DCOP algorithms can be easily manipulated by an agent, either by misreporting private information or deviating from the algorithm, we introduce MDPOP, the first DCOP algorithm that provides a faithful distributed implementation for efficient social choice. This provides a concrete example of how the methods of mechanism design can be unified with those of distributed optimization. Faithfulness ensures that no agent can benefit by unilaterally deviating from any aspect of the protocol, neither informationrevelation, computation, nor communication, and whatever the private information of other agents. We allow for payments by agents to a central bank, which is the only central authority that we require. To achieve faithfulness, we carefully integrate the VickreyClarkeGroves (VCG) mechanism with the DPOP algorithm, such that each agent is only asked to perform computation, report
Algorithmic pricing via virtual valuations
 In Proc. of 8th EC
, 2007
"... Algorithmic pricing is the computational problem that sellers (e.g., in supermarkets) face when trying to set prices for their items to maximize their profit in the presence of a known demand. Guruswami et al. [9] propose this problem and give logarithmic approximations (in the number of consumers) ..."
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Cited by 31 (5 self)
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Algorithmic pricing is the computational problem that sellers (e.g., in supermarkets) face when trying to set prices for their items to maximize their profit in the presence of a known demand. Guruswami et al. [9] propose this problem and give logarithmic approximations (in the number of consumers) for the unitdemand and singleparameter cases where there is a specific set of consumers and their valuations for bundles are known precisely. Subsequently several versions of the problem have been shown to have polylogarithmic inapproximability. This problem has direct ties to the important open question of better understanding the Bayesian optimal mechanism in multiparameter agent settings; however, for this purpose approximation factors logarithmic in the number of agents are inadequate. It is therefore of vital interest to consider special cases where constant approximations are possible. We consider the unitdemand variant of this pricing problem. Here a consumer has a valuation for each different item and their value for a set of items is simply the maximum value they have for any item in the set. Instead of considering a set of consumers with precisely known preferences, like the prior algorithmic pricing literature, we assume that the preferences of the consumers are drawn from a distribution. This is the standard assumption in economics; furthermore, the setting of a specific set of customers with specific preferences, which is employed in all of the prior work in algorithmic pricing, is a special case of this general Bayesian pricing problem, where there is a discrete Bayesian distribution for preferences specified by picking one consumer uniformly from the given set of consumers. Notice that the distribution over the valuations for the individual items that this generates is obviously correlated. Our work complements these existing works by considering the case where the consumer’s valuations for the different items are independent random variables. Our main
Approximate Strategic Reasoning through Hierarchical Reduction of Large Symmetric Games
"... To deal with exponential growth in the size of a game with the number of agents, we propose an approximation based on a hierarchy of reduced games. The reduced game achieves savings by restricting the number of agents playing any strategy to fixed multiples. We validate the idea through experiments ..."
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Cited by 30 (15 self)
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To deal with exponential growth in the size of a game with the number of agents, we propose an approximation based on a hierarchy of reduced games. The reduced game achieves savings by restricting the number of agents playing any strategy to fixed multiples. We validate the idea through experiments on randomly generated localeffect games. An extended application to strategic reasoning about a complex trading scenario motivates the approach, and demonstrates methods for gametheoretic reasoning over incompletelyspecified games at multiple levels of granularity.
ICE: An iterative combinatorial exchange
 In Proceedings of the ACM Conference on Electronic Commerce
, 2005
"... We present the first design for a fully expressive iterative combinatorial exchange (ICE). The exchange incorporates a treebased bidding language that is concise and expressive for CEs. Bidders specify lower and upper bounds on their value for different trades. These bounds allow price discovery an ..."
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Cited by 26 (7 self)
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We present the first design for a fully expressive iterative combinatorial exchange (ICE). The exchange incorporates a treebased bidding language that is concise and expressive for CEs. Bidders specify lower and upper bounds on their value for different trades. These bounds allow price discovery and useful preference elicitation in early rounds, and allow termination with an efficient trade despite partial information on bidder valuations. All computation in the exchange is carefully optimized to exploit the structure of the bidtrees and to avoid enumerating trades. A proxied interpretation of a revealedpreference activity rule ensures progress across rounds. A VCGbased payment scheme that has been shown to mitigate opportunities for bargaining and strategic behavior is used to determine final payments. The exchange is fully implemented and in a validation phase.
Nonparametric estimation of an eBay auction model with an unknown number of bidders
, 2004
"... In this paper, I present new identification results and proposes an estimation method for an eBay auction model with an application. A key difficulty with data from eBay auctions is the fact that the number of potential bidders willing to pay the reserve price is not observable and the number of pot ..."
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Cited by 26 (1 self)
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In this paper, I present new identification results and proposes an estimation method for an eBay auction model with an application. A key difficulty with data from eBay auctions is the fact that the number of potential bidders willing to pay the reserve price is not observable and the number of potential bidders varies auction by auction. While this precludes application of existing estimation methods, I show that this need not preclude structural analysis of the available bid data. In particular, I show that within the symmetric independent private values (IPV) model, observation of any two valuations of which rankings from the top is known (for example, the second and thirdhighest valuations) nonparametrically identifies the bidders ' underlying value distribution. In contrast to the results of previous studies, the researcher does not need to know the number of potential bidders willing to pay the reserve price nor assume that the number of potential bidders is fixed across auctions. I then propose a consistent estimator using the seminonparametric maximum likelihood estimation method developed by Gallant and his coauthors. Several Monte Carlo experiments are conducted to illustrate its performance. The simulation results show that the proposed estimator performs well. I apply the proposed method to university yearbook sales on eBay. Using my estimate of bidders ' value distribution, I explore the effects of sellers ' ratings on bidders ' value distribution; compute consumers' surplus; and examine a regularity assumption that is often made in the mechanism design literature.
Methods for Boosting Revenue in Combinatorial Auctions
, 2004
"... We study the recognized open problem of designing revenuemaximizing combinatorial auctions. It is unsolved even for two bidders and two items for sale. Rather than pursuing the pure economic approach of attempting to characterize the optimal auction, we explore techniques for automatically modif ..."
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Cited by 26 (4 self)
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We study the recognized open problem of designing revenuemaximizing combinatorial auctions. It is unsolved even for two bidders and two items for sale. Rather than pursuing the pure economic approach of attempting to characterize the optimal auction, we explore techniques for automatically modifying existing mechanisms in a way that increase expected revenue. We introduce a general family of auctions, based on bidder weighting and allocation boosting, which we call virtual valuations combinatorial auctions (VVCA). All auctions in the family are based on the VickreyClarkeGroves (VCG) mechanism, executed on virtual valuations that are linear transformations of the bidders' real valuations. The restriction to linear transformations is motivated by incentive compatibility. The auction family is parameterized by the coefficients in the linear transformations. The problem
On the computational complexity of coalitional resource games
 Artificial Intelligence
"... www.elsevier.com/locate/artint We study Coalitional Resource Games (CRGs), a variation of Qualitative Coalitional Games (QCGs) in which each agent is endowed with a set of resources, and the ability of a coalition to bring about a set of goals depends on whether they are collectively endowed with th ..."
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Cited by 26 (6 self)
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www.elsevier.com/locate/artint We study Coalitional Resource Games (CRGs), a variation of Qualitative Coalitional Games (QCGs) in which each agent is endowed with a set of resources, and the ability of a coalition to bring about a set of goals depends on whether they are collectively endowed with the necessary resources. We investigate and classify the computational complexity of a number of natural decision problems for CRGs, over and above those previously investigated for QCGs in general. For example, we show that the complexity of determining whether conflict is inevitable between two coalitions with respect to some stated resource bound (i.e., a limit value for every resource) is coNPcomplete. We then investigate the relationship between CRGs and QCGs, and in particular the extent to which it is possible to translate between the two models. We first characterise the complexity of determining equivalence between CRGs and QCGs. We then show that it is always possible to translate any given CRG into a succinct equivalent QCG, and that it is not always possible to translate a QCG into an equivalent CRG; we establish some necessary and some sufficient conditions for a translation from QCGs to CRGs to be possible, and show that even where an equivalent CRG exists, it may have size exponential in the number of goals and agents of its source QCG.
Sequences of takeitorleaveit offers: Nearoptimal auctions without full valuation revelation
 In AMECV
, 2003
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A Gametheoretic Look at the Gaussian Multiaccess Channel
 DIMACS Workshop on Network Information Theory, Rutgers University, Piscataway
, 2002
"... We study the issue of how to fairly allocate communication rate among the users of a Gaussian multiaccess channel. All users are assumed to value rate equally and each is assumed to have no limit on its desired rate. We adopt a cooperative gametheoretic viewpoint, i.e. it is assumed that the use ..."
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Cited by 23 (0 self)
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We study the issue of how to fairly allocate communication rate among the users of a Gaussian multiaccess channel. All users are assumed to value rate equally and each is assumed to have no limit on its desired rate. We adopt a cooperative gametheoretic viewpoint, i.e. it is assumed that the users can potentially form coalitions off line to threaten other users with jamming the channel, using this as an argument for deserving a larger share of the rate. To determine the characteristic function of the game, we first determine the capacity region of the Gaussian multiaccess arbitrarily varying channel, with an operational meaning of capacity somewhat modified from the usual one, which is more appropriate to our context and permits time sharing. We then propose a solution concept for the game through a set of natural fairness axioms and prove that there exists a unique fair allocation that satisfies the axioms. Moreover, we demonstrate that the unique allocation is always feasible and lies in the core of the game.