2011
DOI: 10.1613/jair.3225
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Redistribution Mechanisms for Assignment of Heterogeneous Objects

Abstract: There are p heterogeneous objects to be assigned to n competing agents (n > p) each with unit demand. It is required to design a Groves mechanism for this assignment problem satisfying weak budget balance, individual rationality, and minimizing the budget imbalance. This calls for designing an appropriate rebate function. When the objects are identical, this problem has been solved which we refer as WCO mechanism. We measure the performance of such mechanisms by the redistribution index. We first prove an impo… Show more

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Cited by 16 publications
(28 citation statements)
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“…as net surplus without regard to the absolute value of the payment amounts. A rewriting of Moulin's objective (9) as…”
Section: Design For the Worst Casementioning
confidence: 99%
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“…as net surplus without regard to the absolute value of the payment amounts. A rewriting of Moulin's objective (9) as…”
Section: Design For the Worst Casementioning
confidence: 99%
“…Guo and Conitzer [11] and Moulin [20] studied rebate design in the case of discrete goods. Gujar and Narahari [9,10] studied rebate design for the allocation of m heterogeneous discrete goods among n agents. Chorppath et al [5] studied rebate design in the divisible goods setting.…”
Section: Introductionmentioning
confidence: 99%
“…So far, several multi-unit auction mechanisms for budget constrained participants have been proposed [1], [2], [6], [15]. In addition, redistribution mechanisms have recently attracted considerable attention from the AI and agent research community [3], [4], [7], [9], [10], [11], [12]. However, to the best of our knowledge, no redistribution mechanism has been developed for budget-constrained participants, although having a budget constraint is quite realistic and natural.…”
Section: Introductionmentioning
confidence: 99%
“…. This notion of worst-case optimality was also studied for more general settings(Gujar & Narahari, 2011;Guo, 2011Guo, , 2012. 4.…”
mentioning
confidence: 99%