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Author: Grafiati

Published: 11 March 2023

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1

Brandt, Felix, Martin Bullinger, and Patrick Lederer. "On the Indecisiveness of Kelly-Strategyproof Social Choice Functions." Journal of Artificial Intelligence Research 73 (April 1, 2022): 1093–130. http://dx.doi.org/10.1613/jair.1.13449.

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Social choice functions (SCFs) map the preferences of a group of agents over some set of alternatives to a non-empty subset of alternatives. The Gibbard-Satterthwaite theorem has shown that only extremely restrictive SCFs are strategyproof when there are more than two alternatives. For set-valued SCFs, or so-called social choice correspondences, the situation is less clear. There are miscellaneous -- mostly negative -- results using a variety of strategyproofness notions and additional requirements. The simple and intuitive notion of Kelly-strategyproofness has turned out to be particularly compelling because it is weak enough to still allow for positive results. For example, the Pareto rule is strategyproof even when preferences are weak, and a number of attractive SCFs (such as the top cycle, the uncovered set, and the essential set) are strategyproof for strict preferences. In this paper, we show that, for weak preferences, only indecisive SCFs can satisfy strategyproofness. In particular, (i) every strategyproof rank-based SCF violates Pareto-optimality, (ii) every strategyproof support-based SCF (which generalize Fishburn's C2 SCFs) that satisfies Pareto-optimality returns at least one most preferred alternative of every voter, and (iii) every strategyproof non-imposing SCF returns the Condorcet loser in at least one profile. We also discuss the consequences of these results for randomized social choice.

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2

Dhull, Komal, Steven Jecmen, Pravesh Kothari, and Nihar B. Shah. "Strategyproofing Peer Assessment via Partitioning: The Price in Terms of Evaluators’ Expertise." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 10, no. 1 (October 14, 2022): 53–63. http://dx.doi.org/10.1609/hcomp.v10i1.21987.

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Strategic behavior is a fundamental problem in a variety of real-world applications that require some form of peer assessment, such as peer grading of homeworks, grant proposal review, conference peer review of scientific papers, and peer assessment of employees in organizations. Since an individual's own work is in competition with the submissions they are evaluating, they may provide dishonest evaluations to increase the relative standing of their own submission. This issue is typically addressed by partitioning the individuals and assigning them to evaluate the work of only those from different subsets. Although this method ensures strategyproofness, each submission may require a different type of expertise for effective evaluation. In this paper, we focus on finding an assignment of evaluators to submissions that maximizes assigned evaluators' expertise subject to the constraint of strategyproofness. We analyze the price of strategyproofness: that is, the amount of compromise on the assigned evaluators' expertise required in order to get strategyproofness. We establish several polynomial-time algorithms for strategyproof assignment along with assignment-quality guarantees. Finally, we evaluate the methods on a dataset from conference peer review.

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3

Guo, Xiaoxi, Sujoy Sikdar, Lirong Xia, Yongzhi Cao, and Hanpin Wang. "Favoring Eagerness for Remaining Items: Designing Efficient, Fair, and Strategyproof Mechanisms." Journal of Artificial Intelligence Research 76 (January 21, 2023): 287–339. http://dx.doi.org/10.1613/jair.1.13878.

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In the assignment problem, the goal is to assign indivisible items to agents who have ordinal preferences, efficiently and fairly, in a strategyproof manner. In practice, first-choice maximality, i.e., assigning a maximal number of agents their top items, is often identified as an important efficiency criterion and measure of agents' satisfaction. In this paper, we propose a natural and intuitive efficiency property, favoring-eagerness-for-remaining-items (FERI), which requires that each item is allocated to an agent who ranks it highest among remaining items, thereby implying first-choice maximality. Using FERI as a heuristic, we design mechanisms that satisfy ex-post or ex-ante variants of FERI together with combinations of other desirable properties of efficiency (Pareto-efficiency), fairness (strong equal treatment of equals and sd-weak-envy-freeness), and strategyproofness (sd-weak-strategyproofness). We also explore the limits of FERI mechanisms in providing stronger efficiency, fairness, or strategyproofness guarantees through impossibility results.

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4

Brandt, Felix, and Christian Geist. "Finding Strategyproof Social Choice Functions via SAT Solving." Journal of Artificial Intelligence Research 55 (March 4, 2016): 565–602. http://dx.doi.org/10.1613/jair.4959.

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A promising direction in computational social choice is to address research problems using computer-aided proving techniques. In particular with SAT solvers, this approach has been shown to be viable not only for proving classic impossibility theorems such as Arrow's Theorem but also for finding new impossibilities in the context of preference extensions. In this paper, we demonstrate that these computer-aided techniques can also be applied to improve our understanding of strategyproof irresolute social choice functions. These functions, however, requires a more evolved encoding as otherwise the search space rapidly becomes much too large. Our contribution is two-fold: We present an efficient encoding for translating such problems to SAT and leverage this encoding to prove new results about strategyproofness with respect to Kelly's and Fishburn's preference extensions. For example, we show that no Pareto-optimal majoritarian social choice function satisfies Fishburn-strategyproofness. Furthermore, we explain how human-readable proofs of such results can be extracted from minimal unsatisfiable cores of the corresponding SAT formulas.

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5

Budish, Eric, and Estelle Cantillon. "The Multi-unit Assignment Problem: Theory and Evidence from Course Allocation at Harvard." American Economic Review 102, no. 5 (August 1, 2012): 2237–71. http://dx.doi.org/10.1257/aer.102.5.2237.

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We use theory and field data to study the draft mechanism used to allocate courses at Harvard Business School. We show that the draft is manipulable in theory, manipulated in practice, and that these manipulations cause significant welfare loss. Nevertheless, we find that welfare is higher than under its widely studied strategyproof alternative. We identify a new link between fairness and welfare that explains why the draft performs well despite the costs of strategic behavior, and then design a new draft that reduces these costs. We draw several broader lessons for market design, regarding Pareto efficiency, fairness, and strategyproofness. (JEL D63, D82, I23)

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6

Chida, Takafumi, Toshiya Kaihara, Nobutada Fujii, Daisuke Kokuryo, and Yuma Shiho. "Computational Study on Strategyproofness of Resource Matching in Crowdsourced Manufacturing." International Journal of Automation Technology 14, no. 5 (September 5, 2020): 734–43. http://dx.doi.org/10.20965/ijat.2020.p0734.

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The need for a sustainable society has grown rapidly. This trend requires new production system concepts following an era of mass customization. As one of these new concepts, “crowdsourced manufacturing” has attracted noticeable attention. In such systems, each participant shares their manufacturing resources for ecosystem co-prosperity, providing new value for the next society. To realize such a concept, it is important to (1) match resource requests and resource offers so as to achieve high efficiency, and (2) induce participants to act in a fair way. Previously, some studies showed production efficiency improvements. Nevertheless, relatively few studies have been conducted on induction mechanisms. The purpose of this study is to develop induction mechanisms for participants. Concerning induction mechanisms, we focus on two viewpoints: (a) matching stability, and (b) “strategyproofness.” These viewpoints are well-known concepts in the market design research field. We previously proposed a resource matching stability analysis method and mechanism for inducing participants to accept matching plans. Formally, a matching method is “strategyproof” when it is a dominant strategy for all participants to submit their true information. However, it is hard to satisfy this condition. Practically, it would be useful to evaluate the strength of an induction, even if the matching method is not strategyproof. In this study, we propose indices for showing the strength of induction (“strength of strategyproofness”). Subsequently, we evaluate matching methods, and show that participants will state false information to maximize their profit in a system with resource matching methods for the profit maximization of the entire system. As the resource providers, they can obtain greater profit by submitting false information regarding resource usage fees. Then, the profits of the resource requesters are unfairly impaired. Furthermore, we propose a new resource matching method, inspired from the “nucleolus” concept in cooperative game theory. The proposed method reduces the maximum dissatisfaction (i.e., profit loss) of resource requesters and resource providers, based on profit sharing. The computational results show that the proposed method induces participants to submit true information, while maintaining high production efficiency.

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7

Zou, James, Sujit Gujar, and David Parkes. "Tolerable Manipulability in Dynamic Assignment without Money." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 4, 2010): 947–52. http://dx.doi.org/10.1609/aaai.v24i1.7630.

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We study a problem of dynamic allocation without money. Agents have arrivals and departures and strict preferences over items. Strategyproofness requires the use of an arrival-priority serial-dictatorship (APSD) mechanism, which is ex post Pareto efficient but has poor ex ante efficiency as measured through average rank efficiency. We introduce the scoring-rule (SR) mechanism, which biases in favor of allocating items that an agent values above the population consensus. The SR mechanism is not strategyproof but has tolerable manipulability in the sense that: (i) if every agent optimally manipulates, it reduces to APSD, and (ii) it significantly outperforms APSD for rank efficiency when only a fraction of agents are strategic. The performance of SR is also robust to mistakes by agents that manipulate on the basis of inaccurate information about the popularity of items.

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8

Bichler, Martin, Zhen Hao, Richard Littmann, and Stefan Waldherr. "Strategyproof auction mechanisms for network procurement." OR Spectrum 42, no. 4 (July 25, 2020): 965–94. http://dx.doi.org/10.1007/s00291-020-00597-7.

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Abstract Deferred-acceptance auctions can be seen as heuristic algorithms to solve $${{\mathcal {N}}}{{\mathcal {P}}}$$ N P -hard allocation problems. Such auctions have been used in the context of the Incentive Auction by the US Federal Communications Commission in 2017, and they have remarkable incentive properties. Besides being strategyproof, they also prevent collusion among participants. Unfortunately, the worst-case approximation ratio of these algorithms is very low in general, but it was observed that they lead to near-optimal solutions in experiments on the specific allocation problem of the Incentive Auction. In this work, which is inspired by the telecommunications industry, we focus on a strategic version of the minimum Steiner tree problem, where the edges are owned by bidders with private costs. We design several deferred-acceptance auctions (DAAs) and compare their performance to the Vickrey–Clarke–Groves (VCG) mechanism as well as several other approximation mechanisms. We observe that, even for medium-sized inputs, the VCG mechanisms experiences impractical runtimes and that the DAAs match the approximation ratios of even the best strategy-proof mechanisms in the average case. We thus provide another example of an important practical mechanism design problem, where empirics suggest that carefully designed deferred-acceptance auctions with their superior incentive properties need not come at a cost in terms of allocative efficiency. Our experiments provide insights into the trade-off between solution quality and runtime and into the additional premium to be paid in DAAs to gain weak group-strategyproofness rather than just strategyproofness.

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9

Aziz, Haris, Hau Chan, Barton Lee, Bo Li, and Toby Walsh. "Facility Location Problem with Capacity Constraints: Algorithmic and Mechanism Design Perspectives." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 1806–13. http://dx.doi.org/10.1609/aaai.v34i02.5547.

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We consider the facility location problem in the one-dimensional setting where each facility can serve a limited number of agents from the algorithmic and mechanism design perspectives. From the algorithmic perspective, we prove that the corresponding optimization problem, where the goal is to locate facilities to minimize either the total cost to all agents or the maximum cost of any agent is NP-hard. However, we show that the problem is fixed-parameter tractable, and the optimal solution can be computed in polynomial time whenever the number of facilities is bounded, or when all facilities have identical capacities. We then consider the problem from a mechanism design perspective where the agents are strategic and need not reveal their true locations. We show that several natural mechanisms studied in the uncapacitated setting either lose strategyproofness or a bound on the solution quality %on the returned solution for the total or maximum cost objective. We then propose new mechanisms that are strategyproof and achieve approximation guarantees that almost match the lower bounds.

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10

Haeringer, Guillaume, and Hanna Hałaburda. "Monotone strategyproofness." Games and Economic Behavior 98 (July 2016): 68–77. http://dx.doi.org/10.1016/j.geb.2016.05.007.

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11

Meir, Reshef, and Jeffrey S. Rosenschein. "Strategyproof classification." ACM SIGecom Exchanges 10, no. 3 (December 2011): 21–25. http://dx.doi.org/10.1145/2325702.2325708.

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12

Xu, Xinping, Bo Li, Minming Li, and Lingjie Duan. "Two-facility Location Games with Minimum Distance Requirement." Journal of Artificial Intelligence Research 70 (February 17, 2021): 719–56. http://dx.doi.org/10.1613/jair.1.12319.

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We study the mechanism design problem of a social planner for locating two facilities on a line interval [0, 1], where a set of n strategic agents report their locations and a mechanism determines the locations of the two facilities. We consider the requirement of a minimum distance 0 ≤ d ≤ 1 between the two facilities. Given the two facilities are heterogeneous, we model the cost/utility of an agent as the sum of his distances to both facilities. In the heterogeneous two-facility location game to minimize the social cost, we show that the optimal solution can be computed in polynomial time and prove that carefully choosing one optimal solution as output is strategyproof. We also design a strategyproof mechanism minimizing the maximum cost. Given the two facilities are homogeneous, we model the cost/utility of an agent as his distance to the closer facility. In the homogeneous two-facility location game for minimizing the social cost, we show that any deterministic strategyproof mechanism has unbounded approximation ratio. Moreover, in the obnoxious heterogeneous two-facility location game for maximizing the social utility, we propose new deterministic group strategyproof mechanisms with provable approximation ratios and establish a lower bound (7 − d)/6 for any deterministic strategyproof mechanism. We also design a strategyproof mechanism maximizing the minimum utility. In the obnoxious homogeneous two-facility location game for maximizing the social utility, we propose deterministic group strategyproof mechanisms with provable approximation ratios and establish a lower bound 4/3. Besides, in the two-facility location game with triple-preference, where each facility may be favorable, obnoxious, indifferent for any agent, we further motivate agents to report both their locations and preferences towards the two facilities truthfully, and design a deterministic group strategyproof mechanism with an approximation ratio 4.

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13

Mennle, Timo, and Sven Seuken. "Partial strategyproofness: Relaxing strategyproofness for the random assignment problem." Journal of Economic Theory 191 (January 2021): 105144. http://dx.doi.org/10.1016/j.jet.2020.105144.

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14

Mutuswami, Suresh. "Strategyproofness, Non-Bossiness and Group Strategyproofness in a cost sharing model." Economics Letters 89, no. 1 (October 2005): 83–88. http://dx.doi.org/10.1016/j.econlet.2005.05.012.

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15

Procaccia, Ariel. "Can Approximation Circumvent Gibbard-Satterthwaite?" Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 4, 2010): 836–41. http://dx.doi.org/10.1609/aaai.v24i1.7619.

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The Gibbard-Satterthwaite 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 worst-case, to manipulate. However, recent work indicates that the prominent voting rules are usually easy to manipulate. In this paper, we suggest a new CS-oriented approach to circumventing Gibbard-Satterthwaite, using randomization and approximation. Specifically, we wish to design strategyproof randomized voting rules that are close, in a standard approximation sense, to prominent score-based (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.

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16

Meir, Reshef, Ariel D. Procaccia, and Jeffrey S. Rosenschein. "Algorithms for strategyproof classification." Artificial Intelligence 186 (July 2012): 123–56. http://dx.doi.org/10.1016/j.artint.2012.03.008.

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17

Chambers, Christopher P., and Nicolas S. Lambert. "Dynamic Belief Elicitation." Econometrica 89, no. 1 (2021): 375–414. http://dx.doi.org/10.3982/ecta15293.

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At an initial time, an individual forms a belief about a future random outcome. As time passes, the individual may obtain, privately or subjectively, further information, until the outcome is eventually revealed. How can a protocol be devised that induces the individual, as a strict best response, to reveal at the outset his prior assessment of both the final outcome and the information flows he anticipates and, subsequently, what information he privately receives? The protocol can provide the individual with payoffs that depend only on the outcome realization and his reports. We develop a framework to design such protocols, and apply it to construct simple elicitation mechanisms for common dynamic environments. The framework is general: we show that strategyproof protocols exist for any number of periods and large outcome sets. For these more general settings, we build a family of strategyproof protocols based on a hierarchy of choice menus, and show that any strategyproof protocol can be approximated by a protocol of this family.

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18

Breton, Michel Le, and Arunava Sen. "Separable Preferences, Strategyproofness, and Decomposability." Econometrica 67, no. 3 (May 1999): 605–28. http://dx.doi.org/10.1111/1468-0262.00038.

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19

Brandt, Felix. "Set-monotonicity implies Kelly-strategyproofness." Social Choice and Welfare 45, no. 4 (February 17, 2015): 793–804. http://dx.doi.org/10.1007/s00355-015-0881-8.

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20

Mitra, Manipushpak, and Suresh Mutuswami. "Group strategyproofness in queueing models." Games and Economic Behavior 72, no. 1 (May 2011): 242–54. http://dx.doi.org/10.1016/j.geb.2010.06.011.

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21

Birmpas, Georgios, Evangelos Markakis, and Guido Schäfer. "Cost Sharing over Combinatorial Domains." ACM Transactions on Economics and Computation 10, no. 1 (March 31, 2022): 1–26. http://dx.doi.org/10.1145/3505586.

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We study the problem of designing cost-sharing mechanisms for combinatorial domains. Suppose that multiple items or services are available to be shared among a set of interested agents. The outcome of a mechanism in this setting consists of an assignment, determining for each item the set of players who are granted service, together with respective payments. Although there are several works studying specialized versions of such problems, there has been almost no progress for general combinatorial cost-sharing domains until recently [ 9 ]. Still, many questions about the interplay between strategyproofness, cost recovery, and economic efficiency remain unanswered. The main goal of our work is to further understand this interplay in terms of budget balance and social cost approximation. Towards this, we provide a refinement of cross-monotonicity (which we term trace-monotonicity ) that is applicable to iterative mechanisms. The trace here refers to the order in which players become finalized. On top of this, we also provide two parameterizations (complementary to a certain extent) of cost functions, which capture the behavior of their average cost-shares. Based on our trace-monotonicity property, we design an Iterative Ascending Cost-Sharing Mechanism, which is applicable to the combinatorial cost-sharing setting with symmetric submodular valuations. Using our first cost function parameterization, we identify conditions under which our mechanism is weakly group-strategyproof, \( O(1) \) -budget-balanced, and \( O(H_n) \) -approximate with respect to the social cost. Furthermore, we show that our mechanism is budget-balanced and \( H_n \) -approximate if both the valuations and the cost functions are symmetric submodular; given existing impossibility results, this is best possible. Finally, we consider general valuation functions and exploit our second parameterization to derive a more fine-grained analysis of the Sequential Mechanism introduced by Moulin. This mechanism is budget balanced by construction, but in general, only guarantees a poor social cost approximation of \( n \) . We identify conditions under which the mechanism achieves improved social cost approximation guarantees. In particular, we derive improved mechanisms for fundamental cost-sharing problems, including Vertex Cover and Set Cover.

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22

Papai, Szilvia. "Strategyproof Assignment by Hierarchical Exchange." Econometrica 68, no. 6 (November 2000): 1403–33. http://dx.doi.org/10.1111/1468-0262.00166.

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23

Bahel, Eric, and Yves Sprumont. "Strategyproof Choice of Social Acts." American Economic Review 110, no. 2 (February 1, 2020): 596–627. http://dx.doi.org/10.1257/aer.20171553.

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We model uncertain social prospects as acts mapping states of nature to (social ) outcomes. A social choice function (or SCF ) assigns an act to each profile of subjective expected utility preferences over acts. An SCF is strategyproof if no agent ever has an incentive to misrepresent her beliefs about the states of nature or her valuation of the outcomes. It is unanimous if it picks the feasible act that all agents find best whenever such an act exists. We offer a characterization of the class of strategyproof and unanimous SCFs in two settings. In the setting where all acts are feasible, the chosen act must yield the favorite outcome of some ( possibly different) agent in every state of nature. The set of states in which an agent’s favorite outcome is selected may vary with the reported belief profile; it is the union of all states assigned to her by a collection of constant, bilaterally dictatorial, or bilaterally consensual assignment rules. In a setting where each state of nature defines a possibly different subset of available outcomes, bilaterally dictatorial or consensual rules can only be used to assign control rights over states characterized by identical sets of available outcomes. (JEL D71, D81, R53)

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24

Li, Zongpeng, and Xiaowen Chu. "On Achieving Group-Strategyproof Multicast." IEEE Transactions on Parallel and Distributed Systems 23, no. 5 (May 2012): 913–23. http://dx.doi.org/10.1109/tpds.2011.197.

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25

Pápai, Szilvia. "Strategyproof exchange of indivisible goods." Journal of Mathematical Economics 39, no. 8 (November 2003): 931–59. http://dx.doi.org/10.1016/s0304-4068(03)00053-3.

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26

P�pai, Szilvia. "Strategyproof single unit award rules." Social Choice and Welfare 18, no. 4 (October 1, 2001): 785–98. http://dx.doi.org/10.1007/s003550000084.

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27

Papai, Szilvia. "Strategyproof and Nonbossy Multiple Assignments." Journal of Public Economic Theory 3, no. 3 (July 2001): 257–71. http://dx.doi.org/10.1111/1097-3923.00066.

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28

Fragiadakis, Daniel, Atsushi Iwasaki, Peter Troyan, Suguru Ueda, and Makoto Yokoo. "Strategyproof Matching with Minimum Quotas." ACM Transactions on Economics and Computation 4, no. 1 (January 5, 2016): 1–40. http://dx.doi.org/10.1145/2841226.

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29

Pápai, Szilvia. "Strategyproof multiple assignment using quotas." Review of Economic Design 5, no. 1 (March 1, 2000): 91–105. http://dx.doi.org/10.1007/s100580050049.

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30

Bordes, Georges, Gilbert Laffond, and Michel Le Breton. "Euclidean preferences, option sets and strategyproofness." SERIEs 2, no. 4 (July 23, 2011): 469–83. http://dx.doi.org/10.1007/s13209-011-0075-2.

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31

Mennle, Timo, and Sven Seuken. "Relaxing strategyproofness in one-sided matching." ACM SIGecom Exchanges 13, no. 1 (November 25, 2014): 64–67. http://dx.doi.org/10.1145/2692375.2692381.

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32

Chen, Yiling, Chara Podimata, Ariel D. Procaccia, and Nisarg Shah. "Strategyproof linear regression in high dimensions." ACM SIGecom Exchanges 17, no. 1 (May 7, 2019): 54–60. http://dx.doi.org/10.1145/3331033.3331038.

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Gopinathan, A., Zongpeng Li, and Baochun Li. "Group Strategyproof Multicast in Wireless Networks." IEEE Transactions on Parallel and Distributed Systems 22, no. 5 (May 2011): 708–15. http://dx.doi.org/10.1109/tpds.2010.184.

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34

Terzopoulou, Zoi, and Ulle Endriss. "Strategyproof judgment aggregation under partial information." Social Choice and Welfare 53, no. 3 (April 20, 2019): 415–42. http://dx.doi.org/10.1007/s00355-019-01191-9.

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35

Fotakis, Dimitris, and Christos Tzamos. "Strategyproof facility location with concave costs." ACM SIGecom Exchanges 12, no. 2 (November 25, 2014): 46–49. http://dx.doi.org/10.1145/2692359.2692370.

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36

Kothari, Anshul, David C. Parkes, and Subhash Suri. "Approximately-strategyproof and tractable multiunit auctions." Decision Support Systems 39, no. 1 (March 2005): 105–21. http://dx.doi.org/10.1016/j.dss.2004.08.009.

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37

Rosenthal, Edward C. "Information and strategyproofness in joint project selection." Journal of Public Economics 68, no. 2 (May 1998): 207–21. http://dx.doi.org/10.1016/s0047-2727(97)00089-3.

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38

Aziz, Haris, Florian Brandl, Felix Brandt, and Markus Brill. "On the tradeoff between efficiency and strategyproofness." Games and Economic Behavior 110 (July 2018): 1–18. http://dx.doi.org/10.1016/j.geb.2018.03.005.

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39

Flammini, Michele, Bojana Kodric, and Giovanna Varricchio. "Strategyproof Mechanisms for Friends and Enemies Games." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 1950–57. http://dx.doi.org/10.1609/aaai.v34i02.5565.

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We investigate strategyproof mechanisms for Friends and Enemies Games, a subclass of Hedonic Games in which every agent classifies any other one as a friend or as an enemy. In this setting, we consider the two classical scenarios proposed in the literature, called Friends Appreciation (FA) and Enemies Aversion (EA). Roughly speaking, in the former each agent gives priority to the number of friends in her coalition, while in the latter to the number of enemies.We provide strategyproof mechanisms for both settings. More precisely, for FA we first present a deterministic n-approximation mechanism, and then show that a much better result can be accomplished by resorting to randomization. Namely, we provide a randomized mechanism whose expected approximation ratio is 4, and arbitrarily close to 4 with high probability. For EA, we give a simple (1+√2)n-approximation mechanism, and show that its performance is asymptotically tight by proving that it is NP-hard to approximate the optimal solution within O(n1−ɛ) for any fixed ɛ > 0.Finally, we show how to extend our results in the presence of neutrals, i.e., when agents can also be indifferent about other agents, and we discuss anonymity.

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40

Flammini, Michele, Bojana Kodric, and Giovanna Varricchio. "Strategyproof mechanisms for Friends and Enemies Games." Artificial Intelligence 302 (January 2022): 103610. http://dx.doi.org/10.1016/j.artint.2021.103610.

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41

Fotakis, Dimitris, and Christos Tzamos. "Strategyproof Facility Location for Concave Cost Functions." Algorithmica 76, no. 1 (July 14, 2015): 143–67. http://dx.doi.org/10.1007/s00453-015-0026-6.

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42

Borodin, Allan, Mark Braverman, Brendan Lucier, and Joel Oren. "Strategyproof Mechanisms for Competitive Influence in Networks." Algorithmica 78, no. 2 (June 2, 2016): 425–52. http://dx.doi.org/10.1007/s00453-016-0169-0.

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Alon, Noga, Michal Feldman, Ariel D. Procaccia, and Moshe Tennenholtz. "Strategyproof Approximation of the Minimax on Networks." Mathematics of Operations Research 35, no. 3 (August 2010): 513–26. http://dx.doi.org/10.1287/moor.1100.0457.

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Kamble, Vijay, and Jean Walrand. "Strategyproof Mechanisms for Purchasing a Shared Resource." ACM SIGMETRICS Performance Evaluation Review 42, no. 3 (December 8, 2014): 4–7. http://dx.doi.org/10.1145/2695533.2695536.

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Mukherjee, Conan. "On group strategyproof and optimal object allocation." Economic Theory Bulletin 8, no. 2 (June 16, 2020): 289–304. http://dx.doi.org/10.1007/s40505-020-00184-7.

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Sprumont, Yves. "A note on strategyproofness in Edgeworth-Box economies." Economics Letters 49, no. 1 (July 1995): 45–50. http://dx.doi.org/10.1016/0165-1765(94)00646-j.

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Bade, Sophie. "Fairness and group-strategyproofness clash in assignment problems." Journal of Economic Theory 165 (September 2016): 257–62. http://dx.doi.org/10.1016/j.jet.2016.04.012.

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Chun, Youngsub, Manipushpak Mitra, and Suresh Mutuswami. "Egalitarian equivalence and strategyproofness in the queueing problem." Economic Theory 56, no. 2 (December 25, 2013): 425–42. http://dx.doi.org/10.1007/s00199-013-0793-8.

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Hossain, Safwan, Evi Micha, and Nisarg Shah. "The Surprising Power of Hiding Information in Facility Location." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 2168–75. http://dx.doi.org/10.1609/aaai.v34i02.5592.

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Abstract:

Facility location is the problem of locating a public facility based on the preferences of multiple agents. In the classic framework, where each agent holds a single location on a line and can misreport it, strategyproof mechanisms for choosing the location of the facility are well-understood.We revisit this problem in a more general framework. We assume that each agent may hold several locations on the line with different degrees of importance to the agent. We study mechanisms which elicit the locations of the agents and different levels of information about their importance. Further, in addition to the classic manipulation of misreporting locations, we introduce and study a new manipulation, whereby agents may hide some of their locations. We argue for its novelty in facility location and applicability in practice. Our results provide a complete picture of the power of strategyproof mechanisms eliciting different levels of information and with respect to each type of manipulation. Surprisingly, we show that in some cases hiding locations can be a strictly more powerful manipulation than misreporting locations.

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Sun, Yu-E., He Huang, Xiang-Yang Li, Yang Du, Miaomiao Tian, Hongli Xu, and Mingjun Xiao. "Privacy-preserving strategyproof auction mechanisms for resource allocation." Tsinghua Science and Technology 22, no. 2 (April 2017): 119–34. http://dx.doi.org/10.23919/tst.2017.7889635.

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