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71
A short introduction to computational social choice
 Proc. 33rd Conference on Current Trends in Theory and Practice of Computer Science
, 2007
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An Approximation Algorithm for MaxMin Fair Allocation of Indivisible goods
 In Proc. of the ACM Symposium on Theory of Computing (STOC
"... In this paper, we give the first approximation algorithm for the problem of maxmin fair allocation of indivisible goods. An instance of this problem consists of a set of k people and m indivisible goods. Each person has a known linear utility function over the set of goods which might be different ..."
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Cited by 59 (2 self)
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In this paper, we give the first approximation algorithm for the problem of maxmin fair allocation of indivisible goods. An instance of this problem consists of a set of k people and m indivisible goods. Each person has a known linear utility function over the set of goods which might be different from the others’. The goal is to distribute the goods among the people and maximize the minimum utility received by them. 1 The approximation ratio of our algorithm is Ω ( √ k log3). As a crucial part of our k algorithm, we design and analyze an iterative method for rounding a fractional matching on a tree which might be of independent interest. We also provide better bounds when we are allowed to exclude a small fraction of the people from the problem.
The Combinatorial Assignment Problem: Approximate Competitive Equilibrium from Equal Incomes
, 2008
"... The combinatorial assignment problem has three principal features: (i) agents require bundles of indivisible objects; (ii) monetary transfers are prohibited; and (iii) the market administrator cares about both e ¢ ciency and fairness. An example of this problem is the assignment of course schedules ..."
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Cited by 59 (11 self)
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The combinatorial assignment problem has three principal features: (i) agents require bundles of indivisible objects; (ii) monetary transfers are prohibited; and (iii) the market administrator cares about both e ¢ ciency and fairness. An example of this problem is the assignment of course schedules to students. Impossibility theorems have established that the only e ¢ cient and strategyproof mechanisms in this environment are dictatorships. Any nondictatorship solution will involve compromise of e ¢ ciency or strategyproofness. This paper proposes a solution to the combinatorial assignment problem. Since we lack attainable criteria of fairness for this environment, I begin by formalizing two such criteria. The maximin share guarantee, based on the idea of divideandchoose, generalizes and weakens fair share. Envy bounded by a single good weakens envy freeness. Both criteria recognize that indivisibilities complicate fair division, but exploit the fact that bundles of indivisible objects are somewhat divisible. Dictatorships fail both criteria. Second, I propose a speci…c mechanism, the Approximate Competitive Equilibrium from Equal Incomes Mechanism, which satis…es the fairness criteria while maintaining attractive compromises of e ¢ ciency and strategyproofness. An exact CEEI may not exist due to indivisibilities and complementarities.
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 52 (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
Efficiency and envyfreeness in fair division of indivisible goods: Logical representation and complexity
 In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI2005
, 2005
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Negotiating socially optimal allocations of resources
 2006) 315–348. P.E. Dunne, Y. Chevaleyre / Theoretical Computer Science 396
, 2008
"... A multiagent system may be thought of as an artificial society of autonomous software agents and we can apply concepts borrowed from welfare economics and social choice theory negotiation framework where agents can agree on multilateral deals to exchange bundles of indivisible resources. We then ana ..."
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Cited by 47 (20 self)
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A multiagent system may be thought of as an artificial society of autonomous software agents and we can apply concepts borrowed from welfare economics and social choice theory negotiation framework where agents can agree on multilateral deals to exchange bundles of indivisible resources. We then analyse how these deals affect social welfare for different instances of the basic framework and different interpretations of the concept of social welfare itself. In particular, we show how certain classes of deals are both sufficient and necessary to guarantee that a socially optimal allocation of resources will be reached eventually. 1.
Online stochastic packing applied to display ad allocation.
 In Proceedings of the 18th Annual European Conference on Algorithms: Part I, ESA’10,
, 2010
"... Abstract. Inspired by online ad allocation, we study online stochastic packing integer programs from theoretical and practical standpoints. We first present a nearoptimal online algorithm for a general class of packing integer programs which model various online resource allocation problems includ ..."
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Cited by 42 (4 self)
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Abstract. Inspired by online ad allocation, we study online stochastic packing integer programs from theoretical and practical standpoints. We first present a nearoptimal online algorithm for a general class of packing integer programs which model various online resource allocation problems including online variants of routing, ad allocations, generalized assignment, and combinatorial auctions. As our main theoretical result, we prove that a simple dual trainingbased algorithm achieves a (1−o(1))approximation guarantee in the random order stochastic model. This is a significant improvement over logarithmic or constantfactor approximations for the adversarial variants of the same problems (e.g. factor 1 − 1 e for online ad allocation, and log(m) for online routing). We then focus on the online display ad allocation problem and study the efficiency and fairness of various trainingbased and online allocation algorithms on data sets collected from reallife display ad allocation system. Our experimental evaluation confirms the effectiveness of trainingbased algorithms on real data sets, and also indicates an intrinsic tradeoff between fairness and efficiency.
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 29 (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.
Making Decisions Based on the Preferences of Multiple Agents
"... People often have to reach a joint decision even though they have conflicting preferences over the alternatives. Examples range from the mundane—such as allocating chores among the members of a household—to the sublime—such as electing a government and thereby charting the course for a country. The ..."
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Cited by 28 (8 self)
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People often have to reach a joint decision even though they have conflicting preferences over the alternatives. Examples range from the mundane—such as allocating chores among the members of a household—to the sublime—such as electing a government and thereby charting the course for a country. The joint decision can be reached by an informal negotiating process or by a carefully specified protocol. Philosophers, mathematicians, political scientists, economists, and others have studied the merits of various protocols for centuries. More recently, especially over the course of the past decade or so, computer scientists have also become deeply involved in this study. The perhaps surprising arrival of computer scientists on this scene is due to a variety of reasons, including the following. 1. Computer networks provide a new platform for communicating