Results 1  10
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15
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 58 (14 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.
Mix and Match
, 2010
"... Consider a matching problem on a graph where disjoint sets of vertices are privately owned by selfinterested agents. An edge between a pair of vertices indicates compatibility and allows the vertices to match. We seek a mechanism to maximize the number of matches despite selfinterest, with agents ..."
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Cited by 23 (8 self)
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Consider a matching problem on a graph where disjoint sets of vertices are privately owned by selfinterested agents. An edge between a pair of vertices indicates compatibility and allows the vertices to match. We seek a mechanism to maximize the number of matches despite selfinterest, with agents that each want to maximize the number of their own vertices that match. Each agent can choose to hide some of its vertices, and then privately match the hidden vertices with any of its own vertices that go unmatched by the mechanism. A prominent application of this model is to kidney exchange, where agents correspond to hospitals and vertices to donorpatient pairs. Here hospitals may game an exchange by holding back pairs and harm social welfare. In this paper we seek to design mechanisms that are strategyproof, in the sense that agents cannot benefit from hiding vertices, and approximately maximize efficiency, i.e., produce a matching that is close in cardinality to the maximum cardinality matching. Our main result is the design and analysis of the eponymous MixandMatch mechanism; we show that this randomized mechanism is strategyproof and provides a 2approximation. Lower bounds establish that the mechanism is near optimal.
Asymptotically optimal strategyproof mechanisms for twofacility games
 in: Proceedings of the 11th ACM Conference on Electronic Commerce (ACMEC), 2010
"... Abstract We consider the problem of locating facilities in a metric space to serve a set of selfish agents. The cost of an agent is the distance between her own location and the nearest facility. The social cost is the total cost of the agents. We are interested in designing strategyproof mechanis ..."
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Cited by 23 (0 self)
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Abstract We consider the problem of locating facilities in a metric space to serve a set of selfish agents. The cost of an agent is the distance between her own location and the nearest facility. The social cost is the total cost of the agents. We are interested in designing strategyproof mechanisms without payment that have a small approximation ratio for social cost. A mechanism is a (possibly randomized) algorithm which maps the locations reported by the agents to the locations of the facilities. A mechanism is strategyproof if no agent can benefit from misreporting her location in any configuration. This setting was first studied by Procaccia and Tennenholtz We first prove an Ω(n) lower bound of the social cost approximation ratio for deterministic strategyproof mechanisms. Our lower bound even holds for the line metric space. This significantly improves the previous constant lower bounds
Sum of Us: Strategyproof Selection from the Selectors
"... We consider directed graphs over a set of n agents, where an edge (i, j) is taken to mean that agent i supports or trusts agent j. Given such a graph and an integer k ≤ n, we wish to select a subset of k agents that maximizes the sum of indegrees, i.e., a subset of k most popular or most trusted age ..."
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Cited by 21 (6 self)
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We consider directed graphs over a set of n agents, where an edge (i, j) is taken to mean that agent i supports or trusts agent j. Given such a graph and an integer k ≤ n, we wish to select a subset of k agents that maximizes the sum of indegrees, i.e., a subset of k most popular or most trusted agents. At the same time we assume that each individual agent is only interested in being selected, and may misreport its outgoing edges to this end. This problem formulation captures realistic scenarios where agents choose among themselves, which can be found in the context of Internet search, social networks like Twitter, or reputation systems like Epinions. Our goal is to design mechanisms without payments that map each graph to a ksubset of agents to be selected and satisfy the following two constraints: strategyproofness, i.e., agents cannot benefit from misreporting their outgoing edges, and approximate optimality, i.e., the sum of indegrees of the selected subset of agents is always close to optimal. Our first main result is a surprising impossibility: for k ∈ {1,...,n − 1}, no deterministic strategyproof mechanism can provide a finite approximation ratio. Our second main result is a randomized strategyproof mechanism with an approximation ratio that
Scheduling without payments
 In SAGT
, 2011
"... We consider mechanisms without payments for the problem of scheduling unrelated machines. Specifically, we consider truthful in expectation randomized mechanisms under the assumption that a machine (player) is bound by its reports: when a machine lies and reports value ˜ti j for a task instead of th ..."
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Cited by 8 (0 self)
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We consider mechanisms without payments for the problem of scheduling unrelated machines. Specifically, we consider truthful in expectation randomized mechanisms under the assumption that a machine (player) is bound by its reports: when a machine lies and reports value ˜ti j for a task instead of the actual one ti j, it will execute for time ˜ti j if it gets the task—unless the declared value ˜ti j is less than the actual value ti j, in which case, it will execute for time ti j. Our main technical result is an optimal mechanism for one task and n players which has approximation ratio (n + 1)/2. We also provide a matching lower bound, showing that no other truthful mechanism can achieve a better approximation ratio. This immediately gives an approximation ratio of (n + 1)/2 and n(n + 1)/2 for social cost and makespan minimization, respectively, for any number of tasks. 1
Strategyproof Facility Location and the Least Squares Objective
"... We consider the problem of locating a public facility on a tree, where a set of n strategic agents report their locations and a mechanism determines, either deterministically or randomly, the location of the facility. The contribution of this paper is twofold. First, we introduce, for the first time ..."
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Cited by 2 (0 self)
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We consider the problem of locating a public facility on a tree, where a set of n strategic agents report their locations and a mechanism determines, either deterministically or randomly, the location of the facility. The contribution of this paper is twofold. First, we introduce, for the first time, a general and clean family of strategyproof (SP) mechanisms for facility location on tree networks. Quite miraculously, all of the deterministic and randomized SP mechanisms that have been previously proposed can be cast as special cases of this family. Thus, the proposed mechanism unifies much of the existing literature on SP facility location problems, and simplifies its analysis. Second, we demonstrate the strength of the proposed family of mechanisms by proving new bounds on the approximation of the minimum sum of squares (miniSOS) objective on line and tree networks. For lines, we devise a randomized mechanism that gives 1.5approximation, and show, through a subtle analysis, that no other randomized SP mechanism can provide a better approximation. For general trees, we construct a randomized mechanism that gives 1.83approximation. This result provides a separation between deterministic and randomized mechanisms, as it is complemented by a lower bound of 2 for any deterministic mechanism. We believe that the devised family of mechanisms will prove useful in studying approximation bounds for additional objectives.
Strategyproof facility location for concave cost functions
 In EC
, 2013
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Mechanism design without money
, 2012
"... Mechanism design is a field that deals with designing algorithms for making decisions based on the preferences of the agents in such a way that the outcome is guaranteed to be good for society and the agents are not incentivised to misreport their preferences. An appropriate mechanism manages to tur ..."
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Mechanism design is a field that deals with designing algorithms for making decisions based on the preferences of the agents in such a way that the outcome is guaranteed to be good for society and the agents are not incentivised to misreport their preferences. An appropriate mechanism manages to turn a group of selfinterested agents into a group collectively satisfied with the decision. Most of the research on the subject is based on enforcing taxes and subsidies to compensate agents, but monetary transactions are not always applicable — for instance, buying and selling organs for transplantation is illegal. Therefore, it is important to know what can be achieved without utilizing payments. This thesis provides a broad survey of both classic and recent results in the field and points out the most important challenges and achievements.