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56
The communication requirements of efficient allocations and supporting prices
 Journal of Economic Theory
, 2006
"... We show that any communication finding a Pareto efficient allocation in a privateinformation economy must also discover supporting Lindahl prices. In particular, efficient allocation of L indivisible objects requires naming a price for each of the 2 L ¡1 bundles. Furthermore, exponential communicat ..."
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Cited by 134 (18 self)
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We show that any communication finding a Pareto efficient allocation in a privateinformation economy must also discover supporting Lindahl prices. In particular, efficient allocation of L indivisible objects requires naming a price for each of the 2 L ¡1 bundles. Furthermore, exponential communication in L is needed just to ensure a higher share of surplus than that realized by auctioning all items as a bundle, or even a higher expected surplus (for some probability distribution over valuations). When the valuations are submodular, efficiency still requires exponential communication (and fully polynomial approximation is impossible). When the objects are homogeneous, arbitrarily good approximation is obtained using exponentially less communication than that needed for exact efficiency.
Preference Elicitation in Combinatorial Auctions (Extended Abstract)
 IN PROCEEDINGS OF THE ACM CONFERENCE ON ELECTRONIC COMMERCE (ACMEC
, 2001
"... Combinatorial auctions (CAs) where bidders can bid on bundles of items can be very desirable market mechanisms when the items sold exhibit complementarity and/or substitutability, so the bidder's valuations for bundles are not additive. However, in a basic CA, the bidders may need to bid on e ..."
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Cited by 108 (27 self)
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Combinatorial auctions (CAs) where bidders can bid on bundles of items can be very desirable market mechanisms when the items sold exhibit complementarity and/or substitutability, so the bidder's valuations for bundles are not additive. However, in a basic CA, the bidders may need to bid on exponentially many bundles, leading to di#culties in determining those valuations, undesirable information revelation, and unnecessary communication. In this paper we present a design of an auctioneer agent that uses topological structure inherent in the problem to reduce the amount of information that it needs from the bidders. An analysis tool is presented as well as data structures for storing and optimally assimilating the information received from the bidders. Using this information, the agent then narrows down the set of desirable (welfaremaximizing or Paretoe#cient) allocations, and decides which questions to ask next. Several algorithms are presented that ask the bidders for value, order, and rank information. A method is presented for making the elicitor incentive compatible.
Auction Design with Costly Preference Elicitation
 Annals of Mathematics and Artificial Intelligence
, 2003
"... We consider auction design in a setting with costly preference elicitation. We motivate the role of proxy agents, that are situated between bidders and the auction, and maintain partial information about agent preferences and compute equilibrium bidding strategies based on the available information. ..."
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Cited by 65 (14 self)
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We consider auction design in a setting with costly preference elicitation. We motivate the role of proxy agents, that are situated between bidders and the auction, and maintain partial information about agent preferences and compute equilibrium bidding strategies based on the available information. The proxy agents can also elicit additional preference information incrementally during an auction. We show that indirect mechanisms, such as proxied ascendingprice auctions, can achieve better allocative efficiency with less preference elicitation than direct mechanisms, such as sealedbid auctions.
Compilation complexity of common voting rules
, 2010
"... In computational social choice, one important problem is to take the votes of a subelectorate (subset of the voters), and summarize them using a small number of bits. This needs to be done in such a way that, if all that we know is the summary, as well as the votes of voters outside the subelectorat ..."
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Cited by 58 (13 self)
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In computational social choice, one important problem is to take the votes of a subelectorate (subset of the voters), and summarize them using a small number of bits. This needs to be done in such a way that, if all that we know is the summary, as well as the votes of voters outside the subelectorate, we can conclude which of the m alternatives wins. This corresponds to the notion of compilation complexity, the minimum number of bits required to summarize the votes for a particular rule, which was introduced by Chevaleyre et al. [IJCAI09]. We study three different types of compilation complexity. The first, studied by Chevaleyre et al., depends on the size of the subelectorate but not on the size of the complement (the voters outside the subelectorate). The second depends on the size of the complement but not on the size of the subelectorate. The third depends on both. We first investigate the relations among the three types of compilation complexity. Then, we give upper and lower bounds on all three types of compilation complexity for the most prominent voting rules. We show that for lapproval (when l ≤ m/2), Borda, and Bucklin, the bounds for all three types are asymptotically tight, up to a multiplicative constant; for lapproval (when l> m/2), plurality with runoff, all Condorcet consistent rules that are based on unweighted majority graphs (including Copeland and voting trees), and all Condorcet consistent rules that are based on the order of pairwise elections (including ranked pairs and maximin), the bounds for all three types are asymptotically tight up to a multiplicative constant when the sizes of the subelectorate and its complement are both larger than m 1+ǫ for some ǫ> 0.
CABOB: A Fast Optimal Algorithm for Winner Determination in Combinatorial Auctions
, 2005
"... Combinatorial auctions where bidders can bid on bundles of items can lead to more economically efficient allocations, but determining the winners is NPcomplete and inapproximable. We present CABOB, a sophisticated optimal search algorithm for the problem. It uses decomposition techniques, upper and ..."
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Cited by 55 (6 self)
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Combinatorial auctions where bidders can bid on bundles of items can lead to more economically efficient allocations, but determining the winners is NPcomplete and inapproximable. We present CABOB, a sophisticated optimal search algorithm for the problem. It uses decomposition techniques, upper and lower bounding (also across components), elaborate and dynamically chosen bidordering heuristics, and a host of structural observations. CABOB attempts to capture structure in any instance without making assumptions about the instance distribution. Experiments against the fastest prior algorithm, CPLEX 8.0, show that CABOB is often faster, seldom drastically slower, and in many cases drastically faster—especially in cases with structure. CABOB’s search runs in linear space and has significantly better anytime performance than CPLEX. We also uncover interesting aspects of the problem itself. First, problems with short bids, which were hard for the first generation of specialized algorithms, are easy. Second, almost all of the CATS distributions are easy, and the run time is virtually unaffected by the number of goods. Third, we test several random restart strategies, showing that they do not help on this problem—the runtime distribution does not have a heavy tail.
Preference Elicitation and Query Learning
 Journal of Machine Learning Research
, 2004
"... In this paper we explore the relationship between "preference elicitation", a learningstyle problem that arises in combinatorial auctions, and the problem of learning via queries studied in computational learning theory. Preference elicitation is the process of asking questions about th ..."
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Cited by 39 (7 self)
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In this paper we explore the relationship between "preference elicitation", a learningstyle problem that arises in combinatorial auctions, and the problem of learning via queries studied in computational learning theory. Preference elicitation is the process of asking questions about the preferences of bidders so as to best divide some set of goods. As a learning problem, it can be thought of as a setting in which there are multiple target concepts that can each be queried separately, but where the goal is not so much to learn each concept as it is to produce an "optimal example". In this work, we prove a number of similarities and differences between twobidder preference elicitation and query learning, giving both separation results and proving some connections between these problems.
Eliciting singlepeaked preferences using comparison queries
 In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems
, 2007
"... Voting is a general method for aggregating the preferences of multiple agents. Each agent ranks all the possible alternatives, and based on this, an aggregate ranking of the alternatives (or at least a winning alternative) is produced. However, when there are many alternatives, it is impractical to ..."
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Cited by 38 (6 self)
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Voting is a general method for aggregating the preferences of multiple agents. Each agent ranks all the possible alternatives, and based on this, an aggregate ranking of the alternatives (or at least a winning alternative) is produced. However, when there are many alternatives, it is impractical to simply ask agents to report their complete preferences. Rather, the agents’ preferences, or at least the relevant parts thereof, need to be elicited. This is done by asking the agents a (hopefully small) number of simple queries about their preferences, such as comparison queries, which ask an agent to compare two of the alternatives. Prior work on preference elicitation in voting has focused on the case of unrestricted preferences. It has been shown that in this setting, it is sometimes necessary to ask each agent (almost) as many queries as would be required to determine an arbitrary ranking of the alternatives. In contrast, in this paper, we focus on singlepeaked preferences. We show that such preferences can be elicited using only a linear number of comparison queries, if either the order with respect to which preferences are singlepeaked is known, or at least one other agent’s complete preferences are known. We show that using a sublinear number of queries does not suffice. We also consider the case of cardinally singlepeaked preferences. For this case, we show that if the alternatives ’ cardinal positions are known, then an agent’s preferences can be elicited using only a logarithmic number of queries; however, we also show that if the cardinal positions are not known, then a sublinear number of queries does not suffice. We present experimental results for all elicitation algorithms. We also consider the problem of only eliciting enough information to determine the aggregate ranking, and show that even for this more modest objective, a sublinear number of queries per agent does not suffice for known ordinal or unknown cardinal positions. Finally, we discuss whether and how these techniques can be applied when preferences are almost singlepeaked. 1 1
Models for iterative multiattribute procurement auctions
 Management Science
"... informs ® doi 10.1287/mnsc.1040.0340 © 2005 INFORMS Multiattribute auctions extend traditional auction settings to allow negotiation over nonprice attributes such as weight, color, and terms of delivery, in addition to price and promise to improve market efficiency in markets with configurable goods ..."
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Cited by 34 (1 self)
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informs ® doi 10.1287/mnsc.1040.0340 © 2005 INFORMS Multiattribute auctions extend traditional auction settings to allow negotiation over nonprice attributes such as weight, color, and terms of delivery, in addition to price and promise to improve market efficiency in markets with configurable goods. This paper provides an iterative auction design for an important special case of the multiattribute allocation problem with special (preferential independent) additive structure on the buyer value and seller costs. Auction Additive&Discrete provides a refined design for a pricebased auction in which the price feedback decomposes to an additive part with a price for each attribute and an aggregate part that appears as a price discount for each supplier. In addition, this design also has excellent information revelation properties that are validated through computational experiments. The auction terminates with an outcome of a modified VickreyClarkeGroves mechanism. This paper also develops Auction NonLinear&Discrete for the more general nonlinear case—a particularly simple design that solves the general multiattribute allocation problem, but requires that the auctioneer maintains prices on bundles of attribute levels. Key words: multiattribute negotiation; iterative auctions; pricebased feedback; VickreyClarkeGroves mechanism; ex post Nash equilibrium; straightforward bidding; procurement History: Accepted by G. Anandalingam and S. Raghavan, special issue editors; received June 6, 2002. This paper was with the authors 8 months for 3 revisions.
ICE: An iterative combinatorial exchange
 In Proceedings of the 6th ACM Conference on Electronic Commerce
, 2005
"... ABSTRACT Combinatorial exchanges (CEs) facilitate trade between multiple buyers and sellers that need to express complex preferences on bundles of items. We present the first design for an iterative combinatorial exchange (ICE). The exchange incorporates a treebased bidding language that is concis ..."
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Cited by 32 (8 self)
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ABSTRACT Combinatorial exchanges (CEs) facilitate trade between multiple buyers and sellers that need to express complex preferences on bundles of items. We present the first design for an iterative combinatorial exchange (ICE). The exchange incorporates a treebased bidding language that is concise and expressive for CEs. Winnerdetermination is directly formulated in terms of the structure of this language. The main innovation is that bidders interaction with proxy agents, and specify lower and upper valuations for trades by annotating the tree with value intervals. The design is entirely symmetric, handling buyers, sellers and mixed buyers and sellers. The proxy approach facilitates early price discovery and ensures useful preference elicitation even in early rounds. Constraint generation is used to generate linear prices without enumerating all possible trades. A proxied interpretation of a revealedpreference activity rule ensures progress. At termination, a VCGbased payment scheme that has been shown to mitigate opportunities for bargaining and strategic behavior is used to determine payments. The exchange is fully implemented, running, and in a validation phase.