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58
Strategyproof approximation mechanisms for location on networks
 CoRR abs/0907.2049. N
, 2010
"... We consider the problem of locating a facility on a network, represented by a graph. A set of strategic agents have different ideal locations for the facility; the cost of an agent is the distance between its ideal location and the facility. A mechanism maps the locations reported by the agents to t ..."
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We consider the problem of locating a facility on a network, represented by a graph. A set of strategic agents have different ideal locations for the facility; the cost of an agent is the distance between its ideal location and the facility. A mechanism maps the locations reported by the agents to the location of the facility. Specifically, we are interested in social choice mechanisms that do not utilize payments. We wish to design mechanisms that are strategyproof, in the sense that agents can never benefit by lying, or, even better, group strategyproof, in the sense that a coalition of agents cannot all benefit by lying. At the same time, our mechanisms must provide a small approximation ratio with respect to one of two optimization targets: the social cost or the maximum cost. We give an almost complete characterization of the feasible truthful approximation ratio under both target functions, deterministic and randomized mechanisms, and with respect to different network topologies. Our main results are: We show that a simple randomized mechanism is group strategyproof and gives a (2 − 2/n)approximation for the social cost, where n is the number of agents, when the network is a circle (known as a ring in the case of computer networks); we design a novel “hybrid” strategyproof randomized mechanism that provides a tight approximation ratio of 3/2 for the maximum
Competitive repeated allocation without payments
 In Proceedings of the Fifth Workshop on Internet and Network Economics
, 2009
"... Abstract. We study the problem of allocating a single item repeatedly among multiple competing agents, in an environment where monetary transfers are not possible. We design (BayesNash) incentive compatible mechanisms that do not rely on payments, with the goal of maximizing expected social welfare ..."
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Abstract. We study the problem of allocating a single item repeatedly among multiple competing agents, in an environment where monetary transfers are not possible. We design (BayesNash) incentive compatible mechanisms that do not rely on payments, with the goal of maximizing expected social welfare. We first focus on the case of two agents. We introduce an artificial payment system, which enables us to construct repeated allocation mechanisms without payments based on oneshot allocation mechanisms with payments. Under certain restrictions on the discount factor, we propose several repeated allocation mechanisms based on artificial payments. For the simple model in which the agents ’ valuations are either high or low, the mechanism we propose is 0.94competitive against the optimal allocation mechanism with payments. For the general case of any prior distribution, the mechanism we propose is 0.85competitive. We generalize the mechanism to cases of three or more agents. For any number of agents, the mechanism we obtain is at least 0.75competitive. The obtained competitive ratios imply that for repeated allocation, artificial payments may be used to replace real monetary payments, without incurring too much loss in social welfare. 1
Incentive compatible two player cake cutting
 In Proc. 8th WINE
, 2012
"... Abstract. We characterize methods of dividing a cake between two bidders in a way that is incentivecompatible and Paretoefficient. In our cake cutting model, each bidder desires a subset of the cake (with a uniform value over this subset), and is allocated some subset. Our characterization procee ..."
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Abstract. We characterize methods of dividing a cake between two bidders in a way that is incentivecompatible and Paretoefficient. In our cake cutting model, each bidder desires a subset of the cake (with a uniform value over this subset), and is allocated some subset. Our characterization proceeds via reducing to a simple onedimensional version of the problem, and yields, for example, a tight bound on the social welfare achievable.
Mix and Match: A Strategyproof Mechanism for MultiHospital Kidney Exchange
, 2012
"... As kidney exchange programs are growing, manipulation by hospitals becomes more of an issue. Assuming that hospitals wish to maximize the number of their own patients who receive a kidney, they may have an incentive to withhold some of their incompatible donorpatient pairs and match them internally ..."
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Cited by 8 (0 self)
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As kidney exchange programs are growing, manipulation by hospitals becomes more of an issue. Assuming that hospitals wish to maximize the number of their own patients who receive a kidney, they may have an incentive to withhold some of their incompatible donorpatient pairs and match them internally, thus harming social welfare. We study mechanisms for twoway exchanges that are strategyproof, i.e., make it a dominant strategy for hospitals to report all their incompatible pairs. We establish lower bounds on the welfare loss of strategyproof mechanisms, both deterministic and randomized, and propose a randomized mechanism that guarantees at least half of the maximum social welfare in the worst case. Simulations using realistic distributions for blood types and other parameters suggest that in practice our mechanism performs much closer to optimal.
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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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
Approximation algorithms and mechanism design for minimax approval voting
 In Proceedings of the 24th AAAI Conference on Artificial Intelligence
, 2010
"... We consider approval voting elections in which each voter votes for a (possibly empty) set of candidates and the outcome consists of a set of k candidates for some parameter k, e.g., committee elections. We are interested in the minimax approval voting rule in which the outcome represents a compromi ..."
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We consider approval voting elections in which each voter votes for a (possibly empty) set of candidates and the outcome consists of a set of k candidates for some parameter k, e.g., committee elections. We are interested in the minimax approval voting rule in which the outcome represents a compromise among the voters, in the sense that the maximum distance between the preference of any voter and the outcome is as small as possible. This voting rule has two main drawbacks. First, computing an outcome that minimizes the maximum distance is computationally hard. Furthermore, any algorithm that always returns such an outcome provides incentives to voters to misreport their true preferences. In order to circumvent these drawbacks, we consider approximation algorithms, i.e., algorithms that produce an outcome that approximates the minimax distance for any given instance. Such algorithms can be considered as alternative voting rules. We present a polynomialtime 2approximation algorithm that uses a natural linear programming relaxation for the underlying optimization problem and deterministically rounds the fractional solution in order to compute the outcome; this result improves upon the previously best known algorithm that has an approximation ratio of 3. We are furthermore interested in approximation algorithms that are resistant to manipulation by (coalitions of) voters, i.e., algorithms that do not motivate voters to misreport their true preferences in order to improve their distance from the outcome. We complement previous results in the literature with new upper and lower bounds on strategyproof and groupstrategyproof algorithms.
An improved 2agent kidney exchange mechanism
"... Abstract. We study a mechanism design version of matching computation in graphs that models the game played by hospitals participating in pairwise kidney exchange programs. We present a new randomized matching mechanism for two agents which is truthful in expectation and has an approximation ratio o ..."
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Abstract. We study a mechanism design version of matching computation in graphs that models the game played by hospitals participating in pairwise kidney exchange programs. We present a new randomized matching mechanism for two agents which is truthful in expectation and has an approximation ratio of 3/2 to the maximum cardinality matching. This is an improvement over a recent upper bound of 2 [Ashlagi et al., EC 2010] and, furthermore, our mechanism beats for the first time the lower bound on the approximation ratio of deterministic truthful mechanisms. We complement our positive result with new lower bounds. Among other statements, we prove that the weaker incentive compatibility property of truthfulness in expectation in our mechanism is necessary; universally truthful mechanisms that have an inclusionmaximality property have In an attempt to address the wide need for kidney transplantation and the scarcity of cadaver kidneys, several countries have launched, or are considering,
Mechanism Design for Fair Division: Allocating Divisible Items without Payments
"... We revisit the classic problem of fair division from a mechanism design perspective, using Proportional Fairness as a benchmark. In particular, we aim to allocate a collection of divisible items to a set of agents while incentivizing the agents to be truthful in reporting their valuations. For the v ..."
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Cited by 7 (2 self)
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We revisit the classic problem of fair division from a mechanism design perspective, using Proportional Fairness as a benchmark. In particular, we aim to allocate a collection of divisible items to a set of agents while incentivizing the agents to be truthful in reporting their valuations. For the very large class of homogeneous valuations, we design a truthful mechanism that provides every agent with at least a 1/e â 0.368 fraction of her Proportionally Fair valuation. To complement this result, we show that no truthful mechanism can guarantee more than a 0.5 fraction, even for the restricted class of additive linear valuations. We also propose another mechanism for additive linear valuations that works really well when every item is highly demanded. To guarantee truthfulness, our mechanisms discard a carefully chosen fraction of the allocated resources; we conclude by uncovering interesting connections between our mechanisms and known mechanisms that use money instead.
Multidimensional Singlepeaked Consistency and its Approximations
"... Singlepeakedness is one of the most commonly used domain restrictions in social choice. However, the extent to which agent preferences are singlepeaked in practice, and the extent to which recent proposals for approximate singlepeakedness can further help explain voter preferences, is unclear. In ..."
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Cited by 7 (1 self)
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Singlepeakedness is one of the most commonly used domain restrictions in social choice. However, the extent to which agent preferences are singlepeaked in practice, and the extent to which recent proposals for approximate singlepeakedness can further help explain voter preferences, is unclear. In this article, we assess the ability of both singledimensional and multidimensional approximations to explain preference profiles drawn from several realworld elections. We develop a simple branchandbound algorithm that finds multidimensional, singlepeaked axes that best fit a given profile, and which works with several forms of approximation. Empirical results on two election data sets show that preferences in these elections are far from singlepeaked in any onedimensional space, but are nearly singlepeaked in two dimensions. Our algorithms are reasonably efficient in practice, and also show excellent anytime performance. 1
Strategyproof Classification with Shared Inputs
"... Strategyproof classification deals with a setting where a decisionmaker must classify a set of input points with binary labels, while minimizing the expected error. The labels of the input points are reported by selfinterested agents, who might lie in order to obtain a classifier that more closely ..."
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Cited by 7 (4 self)
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Strategyproof classification deals with a setting where a decisionmaker must classify a set of input points with binary labels, while minimizing the expected error. The labels of the input points are reported by selfinterested agents, who might lie in order to obtain a classifier that more closely matches their own labels, thus creating a bias in the data; this motivates the design of truthful mechanisms that discourage false reports. Previous work [Meir et al., 2008] investigated both decisiontheoretic and learningtheoretic variations of the setting, but only considered classifiers that belong to a degenerate class. In this paper we assume that the agents are interested in a shared set of input points. We show that this plausible assumption leads to powerful results. In particular, we demonstrate that variations of a truthful random dictator mechanism can guarantee approximately optimal outcomes with respect to any class of classifiers. 1