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58
Truthful assignment without money
 In Proceedings of the 11th ACM Conference on Electronic Commerce (EC
, 2010
"... We study the design of truthful mechanisms that do not use payments for the generalized assignment problem (GAP) and its variants. An instance of the GAP consists of a bipartite graph with jobs on one side and machines on the other. Machines have capacities and edges have values and sizes; the goal ..."
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We study the design of truthful mechanisms that do not use payments for the generalized assignment problem (GAP) and its variants. An instance of the GAP consists of a bipartite graph with jobs on one side and machines on the other. Machines have capacities and edges have values and sizes; the goal is to construct a welfare maximizing feasible assignment. In our model of private valuations, motivated by impossibility results, the value and sizes on all jobmachine pairs are public information; however, whether an edge exists or not in the bipartite graph is a job’s private information. That is, the selfish agents in our model are the jobs, and their private information is their edge set. We want to design mechanisms that are truthful without money (henceforth strategyproof), and produce assignments whose welfare
Can Approximation Circumvent GibbardSatterthwaite?
"... The GibbardSatterthwaite Theorem asserts that any reasonable voting rule cannot be strategyproof. A large body of research in AI deals with circumventing this theorem via computational considerations; the goal is to design voting rules that are computationally hard, in the worstcase, to manipulate ..."
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Cited by 24 (6 self)
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The GibbardSatterthwaite Theorem asserts that any reasonable voting rule cannot be strategyproof. A large body of research in AI deals with circumventing this theorem via computational considerations; the goal is to design voting rules that are computationally hard, in the worstcase, to manipulate. However, recent work indicates that the prominent voting rules are usually easy to manipulate. In this paper, we suggest a new CSoriented approach to circumventing GibbardSatterthwaite, using randomization and approximation. Specifically, we wish to design strategyproof randomized voting rules that are close, in a standard approximation sense, to prominent scorebased (deterministic) voting rules. We give tight lower and upper bounds on the approximation ratio achievable via strategyproof randomized rules with respect to positional scoring rules, Copeland, and Maximin.
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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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
Optimal EnvyFree Cake Cutting
 PROCEEDINGS OF THE TWENTYFIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2011
"... We consider the problem of fairly dividing a heterogeneous divisible good among agents with different preferences. Previous work has shown that envyfree allocations, i.e., where each agent prefers its own allocation to any other, may not be efficient, in the sense of maximizing the total value of t ..."
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Cited by 22 (10 self)
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We consider the problem of fairly dividing a heterogeneous divisible good among agents with different preferences. Previous work has shown that envyfree allocations, i.e., where each agent prefers its own allocation to any other, may not be efficient, in the sense of maximizing the total value of the agents. Our goal is to pinpoint the most efficient allocations among all envyfree allocations. We provide tractable algorithms for doing so under different assumptions regarding the preferences of the agents.
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
Strategyproof Allocation of Multiple Items between Two Agents without Payments or Priors
, 2010
"... We investigate the problem of allocating items (private goods) among competing agents in a setting that is both priorfree and paymentfree. Specifically, we focus on allocating multiple heterogeneous items between two agents with additive valuation functions. Our objective is to design strategyproo ..."
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We investigate the problem of allocating items (private goods) among competing agents in a setting that is both priorfree and paymentfree. Specifically, we focus on allocating multiple heterogeneous items between two agents with additive valuation functions. Our objective is to design strategyproof mechanisms that are competitive against the most efficient (firstbest) allocation. We introduce the family of linear increasingprice (LIP) mechanisms. The LIP mechanisms are strategyproof, priorfree, and paymentfree, and they are exactly the increasingprice mechanisms satisfying a strong responsiveness property. We show how to solve for competitive mechanisms within the LIP family. For the case of two items, we find a LIP mechanism whose competitive ratio is near optimal (the achieved competitive ratio is 0.828, while any strategyproof mechanism is at most 0.841competitive). As the number of items goes to infinity, we prove a negative result that any increasingprice mechanism (linear or nonlinear) has a maximal competitive ratio of 0.5. Our results imply that in some cases, it is possible to design good allocation mechanisms without payments and without priors.
Tighter bounds for facility games
 Proc.5th Internat.Workshop Internet Network Econom.(WINE
, 2009
"... Abstract. In one dimensional facility games, public facilities are placed based on the reported locations of the agents, where all the locations of agents and facilities are on a real line. The cost of an agent is measured by the distance from its location to the nearest facility. We study the appro ..."
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Abstract. In one dimensional facility games, public facilities are placed based on the reported locations of the agents, where all the locations of agents and facilities are on a real line. The cost of an agent is measured by the distance from its location to the nearest facility. We study the approximation ratio of social welfare for strategyproof mechanisms, where no agent can benefit by misreporting its location. In this paper, we use the total cost of agents as social welfare function. We study two extensions of the simplest version as in [9]: two facilities and multiple locations per agent. In both cases, we analyze randomized strategyproof mechanisms, and give the first lower bound of 1.045 and 1.33, respectively. The latter lower bound is obtained by solving a related linear programming problem, and we believe that this new technique of proving lower bounds for randomized mechanisms may find applications in other problems and is of independent interest. We also improve several approximation bounds in [9], and confirm a conjecture in [9]. 1