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The mechanism design theory can be applied not only in the economy but also in many fields, such as politics and military affairs, which has important practical and strategic significance for countries in the period of system innovation and transformation. As Nobel Laureate Paul said, the complexity of the real economy makes it difficult for "Unorganized Markets" to ensure supply-demand balance and the efficient allocation of resources. When traditional economic theory cannot explain and calculate the complex scenes of reality, we require a high-performance computing solution based on traditional theory to evaluate the mechanisms, meanwhile, get better social welfare. The mechanism design theory is undoubtedly the best option. Different from other existing works, which are based on the theoretical exploration of optimal solutions or single perspective analysis of scenarios, this paper focuses on the more real and complex markets. It explores to discover the common difficulties and feasible solutions for the applications. Firstly, we review the history of traditional mechanism design and algorithm mechanism design. Subsequently, we present the main challenges in designing the actual data-driven market mechanisms, including the inherent challenges in the mechanism design theory, the challenges brought by new markets and the common challenges faced by both. In addition, we also comb and discuss theoretical support and computer-aided methods in detail. This paper guides cross-disciplinary researchers who wish to explore the resource allocation problem in real markets for the first time and offers a different perspective for researchers struggling to solve complex social problems. Finally, we discuss and propose new ideas and look to the future. [ABSTRACT FROM AUTHOR]

This paper studies the problem of dividing m indivisible items among n agents fairly and efficiently. Specifically, this research concentrates on pairwise maximin share (PMMS), which is defined to be the maximum value that an agent can guarantee for herself if she were to repartition the items with another agent and receive the bundle with the minimum value. PMMS is an important concept in the fair division. However, whether PMMS for indivisible items exists is still open. This work concentrates on PMMS by proving the existence of PMMS on linear graphs with binary valuation functions. Besides, this paper designs an algorithm to approximate PMMS in the case where different agents have identical valuations among the same items, achieving a ratio strictly greater than 0.8, which outperforms the state-of-the-art ratio of 0.781 from Kurokawa [22]. The time complexity of our FFD-based algorithm is O(mn + mlog m). [ABSTRACT FROM AUTHOR]

Truthful task scheduling mechanisms are designed to cope with the selfishness of the participating agents. They assume that the agents are selfish; each agent's goal is to maximize its own profit. However, this is not always the case; an agent may want to cause losses to the other agents besides maximizing its profit. Such an agent is said to be an antisocial agent. An antisocial agent will try to gain as much profit as possible relative to the other agents. This paper presents an antisocial strategy which can be used by the antisocial agents to inflict losses on the other agents participating in a task scheduling mechanism on related machines. This paper also studies, by simulation, the effect of different parameters, such as the degree of antisociality on the relative losses that can be inflicted on the participating agents. [ABSTRACT FROM AUTHOR]

In a classic optimization problem, the complete input data is assumed to be known to the algorithm. This assumption may not be true anymore in optimization problems motivated by the Internet where part of the input data is private knowledge of independent selfish agents. The goal of algorithmic mechanism design is to provide (in polynomial time) a solution to the optimization problem and a set of incentives for the agents such that disclosing the input data is a dominant strategy for the agents. In the case of NP-hard problems, the solution computed should also be a good approximation of the optimum. In this paper we focus on mechanism design for multiobjective optimization problems. In this setting we are given a main objective function and a set of secondary objectives which are modeled via budget constraints. Multiobjective optimization is a natural setting for mechanism design as many economical choices ask for a compromise between different, partially conflicting goals. The main contribution of this paper is showing that two of the main tools for the design of approximation algorithms for multiobjective optimization problems, namely, approximate Pareto sets and Lagrangian relaxation, can lead to truthful approximation schemes. By exploiting the method of approximate Pareto sets, we devise truthful deterministic and randomized multicriteria fully polynomial-time approximation schemes (FPTASs) for multiobjective optimization problems whose exact version admits a pseudopolynomial-time algorithm, as, for instance, the multibudgeted versions of minimum spanning tree, shortest path, maximum (perfect) matching,and matroid intersection. Our construction also applies to multidimensional knapsack and multiunit combinatorial auctions. Our FPTASs compute a (1+ε)-approximate solution violating each budget constraint by a factor (1+ ε). When feasible solutions induce an independence system, i.e., when subsets of feasible solutions are feasible as well, we present a PTAS (not violating any constraint), which combines the approach above with a novel monotone way to guess the heaviest elements in the optimum solution. Finally, we present a universally truthful Las Vegas PTAS for minimum spanning tree with a single budget constraint, where one wants to compute a minimum cost spanning tree whose length is at most a given value L. This result is based on the Lagrangian relaxation method, in combination with our monotone guessing step and with a random perturbation step (ensuring low expected running time). This result can be derandomized in the case of integral lengths. All the mentioned results match the best known approximation ratios, which are, however, obtained by nontruthful algorithms. [ABSTRACT FROM AUTHOR]

This paper presents inapproximability results for paradigmatic multi-dimensional truthful mechanism design problems. We first show a lower bound of 2 − 1 n for the scheduling problem with n 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 universally-truthful randomized mechanisms, regardless of any computational assumptions on the running time of these mechanisms. Moreover, it holds even for the wider class of truthfulness-in-expectation mechanisms. We then turn to Bayesian settings and show a lower bound of 1.2 for Bayesian Incentive-Compatible (BIC) mechanisms. No lower bounds for truthful mechanisms in multi-dimensional settings which incorporate randomness were previously known. Next, we define the workload-minimization problem in networks. We prove lower bounds for the inter-domain routing setting presented by Feigenbaum, Papadimitriou, Sami, and Shenker. Finally, we prove lower bounds for Max–Min fairness, Min–Max fairness, and envy minimization. [ABSTRACT FROM AUTHOR]

This paper provides a literature overview on (direct revelation) algorithmic mechanism design in the context of machine scheduling problems. Here, one takes a game-theoretic perspective and assumes that part of the relevant data of the machine scheduling problem is private information of selfish players (usually machines or jobs) who may try to influence the solution determined by the scheduling algorithm by submitting false data. A central planner is in charge of controlling and designing the algorithm and a rewarding scheme that defines payments among planner and players based on the submitted data. The planner may, for example, want to design algorithm and payments such that reporting the true data always maximizes the utility functions of rationally acting players, because this enables the planner to generate fair solutions with respect to some social criterion that considers the interests of all players. We review the categories and characterizing problem features of machine scheduling settings in the algorithmic mechanism design literature and extend the widely accepted classification scheme of Graham et al. (Ann Discrete Math 5:287-326, 1979) for scheduling problems to include aspects relating to mechanism design. Based on this hierarchical scheme, we give a systematic overview of recent contributions in this field of research. [ABSTRACT FROM AUTHOR]

Data object replication onto distributed servers can potentially alleviate bottlenecks, reduce network traffic, increase scalability, add robustness, and decrease user perceived access time. The decision of selecting data object and server pairs requires solving a constraint optimization problem that in general is NP-complete. In this paper, we abstract the distributed database system as an agent-based model, wherein agents continuously compete for allocation and reallocation of data objects. Each agent aims to replicate objects onto its server such that the communication cost is minimized. However, these agents do not have a global view of the system. Thereby, the optimization process becomes highly localized. Such localized optimization may severely affect the overall system performance. To cope with such localized optimization, we propose a 'semi-distributed' axiomatic game theoretical mechanism. The mechanism's control is unique in its decision making process, wherein all the heavy processing is done on the servers of the distributed system and the central body is only required to take a binary decision: (0) not to replicate or (1) to replicate. The cost model used by the agents in the mechanism for the purpose of identifying beneficial data objects is tailored made so that even though the agents take decisions based on their local knowledge domain, the effect is translated into a system-wide performance enhancement. The proposed mechanism is extensively compared against seven well-known conventional and three game theoretical replica allocation methods, namely, branch and bound, greedy, genetic, data-aware replication, tree inspired bottom-up procedure, tree inspired min-max procedure, Benders' decomposition based procedure, game theoretical English auction, game theoretical Dutch auction, and game theoretical selfish replication procedure. The experimental setup incorporates GT-ITM, Inet network topology generators, Soccer World Cup 1998 access logs, and NASA Kennedy Space Center access logs to closely mimic the Web in its infrastructure and user access patterns. The experimental results reveal that the proposed technique despite its non-cooperative nature improves the solution quality and reduces the execution time compared to other techniques. [ABSTRACT FROM AUTHOR]

Differentiated service (DiffServ) is a mechanism to provide the quality-of-service (QoS) with a certain performance guarantee. In this paper, we study how to design DiffServ multicast when every relay link is an independent selfish agent. We assume that each link ei is associated with a (privately known) cost coefficient ci such that the cost of el to provide a transmission service with bandwidth demand χ is ci ⋅ χ. Further, we assume that there is a fixed source node s and a set R of receivers, each of which requires from s data with a minimum bandwidth demand. The DiffServ multicast problem is to compute a link-weighted tree rooted at s and spanning R such that the receivers' demands are met. This generalizes the traditional link-weighted Steiner tree problem. We first show that a previous approximation algorithm does not directly induce a strategyproof mechanism. We then give a new polynomial time algorithm to construct a DiffServ multicast tree whose total cost is no more than eight times the optimal total cost when the cost coefficient of each link is known. Based on this tree, we design a truthful mechanism for DiffServ multicast, i.e., we give a polynomial-time computable payment scheme to compensate all chosen relay links such that each link maximizes its profit when it declares its cost coefficient truthfully. [ABSTRACT FROM AUTHOR]

I conceptualise the informal outlines of a new voting system/mechanism that is more holistic and democratically just; the proposed alternative would incorporate (gender) quotas amongst other fundamental, important aspects. This conceptual outline seeks to resolve the primary (intellectual) obstacles for the adoption of quotas in their current form which, I contend, is their discordance with mainstream conceptions of democratic justice. More precisely, the conceptualised voting system/mechanism seeks to increase the sophistication of preferencespecification in voting procedures (thereby presenting an alternative to the prevailing, privileged paradigm of geospatial-constituency representation). I do not present results in the conventional sense; no data was analysed, no experiments run, and no software implemented. A rationale is provided for why this proposed voting system/mechanism, in addition to potentially solving/tackling some problems identified by feminist scholarship, could also solve an even larger class of fundamental political and democratic problems related to intersectionality (e.g., race, ethnicity, income, class, caste, disability, sexuality, education, age, occupation etc.) through enhancing the translation, representation, and implementation of voters' interests. In terms of conclusions within the paper, they are largely intermediate conclusions as they relate to the investigation of problems as well as the informal conceptual outline of the proposed voting system/mechanism. [ABSTRACT FROM AUTHOR]

In this paper, we study the optional preference model of the facility location game problem with two heterogeneous facilities on a line. The preference of each agent is one of the two facilities or both facilities, and the cost of each agent is a function of the distances to the facilities that the agent prefers. We consider two cost functions: Minimum Distance and Maximum Distance functions. Aiming at minimizing the maximum cost or the social cost of agents, we propose different strategyproof mechanisms without monetary transfers and derive both lower and upper bounds of the approximation ratios with respect to strategyproof mechanisms. In the variant of Minimum Distance, we propose a 2-approximation deterministic strategyproof mechanism for the maximum cost objective, and prove a lower bound of 4/3, while for the social cost objective we propose a (n / 2 +1)-approximation deterministic strategyproof mechanism and prove a lower bound of 2, also a lower bound of 3/2 for randomized mechanisms. In the variant of Maximum Distance, we propose an optimal deterministic strategyproof mechanism for the maximum cost objective and a 2-approximation deterministic strategyproof mechanism for the social cost objective. [ABSTRACT FROM AUTHOR]

Prior-free auctions are robust auctions that assume no distribution over bidders' valuations and provide worst-case (input-by-input) approximation guarantees. In contrast to previous work on this topic, we pursue good prior-free auctions with non-identical bidders. Prior-free auctions can approximate meaningful benchmarks for non-identical bidders only when sufficient qualitative information about the bidder asymmetry is publicly known. We consider digital goods auctions where there is a total ordering of the bidders that is known to the seller, where earlier bidders are in some sense thought to have higher valuations. We use the framework of Hartline and Roughgarden (STOC'08) to define an appropriate revenue benchmark: the maximum revenue that can be obtained from a bid vector using prices that are nonincreasing in the bidder ordering and bounded above by the second-highest bid. This monotone-price benchmark is always as large as the well-known fixed-price benchmark F (2) , so designing prior-free auctions with good approximation guarantees is only harder. By design, an auction that approximates the monotone-price benchmark satisfies a very strong guarantee: it is, in particular, simultaneously near-optimal for essentially every Bayesian environment in which bidders' valuation distributions have nonincreasing monopoly prices, or in which the distribution of each bidder stochastically dominates that of the next. Even when there is no distribution over bidders' valuations, such an auction still provides a quantifiable input-by-input performance guarantee. In this paper, we design a simple O (1) -competitive prior-free auction for digital goods with ordered bidders. We also extend the monotone-price benchmark and our O (1) -competitive prior-free auction to multi-unit settings with limited supply. [ABSTRACT FROM AUTHOR]

Approximation mechanism design without money was first studied in Procaccia and Tennenholtz () by considering a facility location game. In general, a facility is being opened and the cost of an agent is measured by its distance to the facility. In order to achieve a good social cost, a mechanism selects the location of the facility based on the locations reported by agents. It motivates agents to strategically report their locations to get good outcomes for themselves. A mechanism is called strategyproof if no agents could manipulate to get a better outcome by telling lies regardless of any configuration of other agents. The main contribution in this paper is to explore the strategyproof mechanisms without money when agents are distinguishable. There are two main variations on the nature of agents. One is that agents prefer getting closer to the facility, while the other is that agents prefer being far away from the facility. We first consider the model that directly extends the model in Procaccia and Tennenholtz (). In particular, we consider the strategyproof mechanisms without money when agents are weighted. We show that the strategyproof mechanisms in the case of unweighted agents are still the best in the weighted cases. We establish tight lower and upper bounds for approximation ratios on the optimal social utility and the minimum utility when agents prefer to stay close to the facility. We then provide the lower and upper bounds on the optimal social utility and lower bound on the minimum distance per weight when agents prefer to stay far away from the facility. We also extend our study in a natural direction where two facilities must be built on a real line. Secondly, we propose an novel threshold based model to distinguish agents. In this model, we present a strategyproof mechanism that leads to optimal solutions in terms of social cost. [ABSTRACT FROM AUTHOR]

This work contributes an (approximately) incentive-compatible and computationally efficient bargaining mechanism for pricing fog computing resources. In network settings (e.g., fog computing), it is plausible to think that self-interested and incompletely informed players (represented by software agents) will attempt to maximize their own benefits at the expense of others. Hence, it is crucial that fog bargaining mechanisms give incentives to agents for behaving in a manner consistent with the desired outcome where every agent's benefit is maximized. Equilibrium analyses validate that the fog bargaining mechanism in this work is approximately Bayesian incentive compatible because every agent can approximately maximize its expected utility by adhering to the strategy recommended by the bargaining mechanism given that all other agents also adhere to their equilibrium strategies. That is, if every agent in the market adheres to the strategy recommended by the bargaining mechanism, then the strategy profile of the agents forms an approximate Bayesian Nash equilibrium. Given that a fog resource market has a large number of buyers and a large number of sellers, computational efficiency is also imperative since every agent needs to process a huge number of trading alternatives. Computational complexity analyses validate that 1) the procedure for carrying out the bargaining strategy has a linear time complexity, and with every passing round, the search space dwindles but the solutions become progressively better, 2) the number of rounds for each agent to complete bargaining is logarithmic in the number of its opponents, and 3) each agent has a linear message complexity. [ABSTRACT FROM AUTHOR]

In many settings the power of truthful mechanisms is severely bounded. In this paper we use randomization to overcome this problem in the multi-unit auction setting. In particular, we construct a fully polynomial-time approximation scheme (FPTAS) for multi-unit auctions that is truthful in expectation, whereas there is evidence that no polynomial-time truthful deterministic mechanism provides an approximation ratio better than 2. We leverage the FPTAS to show for the first time that truthful in expectation polynomial-time mechanisms are provably stronger than polynomial-time universally truthful mechanisms. Specifically, we show that there is a setting, related to multi-unit auctions, in which (1) there is a nonpolynomial time truthful mechanism that always outputs the optimal solution, and that (2) no universally truthful randomized mechanism can provide an approximation ratio better than 2 in polynomial time, but (3) an FPTAS that is truthful in expectation exists. [ABSTRACT FROM AUTHOR]

Scheduling on related machines (Q||Cmax) is one of the most important problems in the field of algorithmic mechanism design. Each machine is controlled by a selfish agent and her valuation function can be expressed via a single parameter, her speed. Archer and Tardos [Proceedings of the 42nd Annual Symposium on Foundations of Computer Science (FOCS), Las Vegas, NV, 2001, pp. 482--491] showed that, in contrast to other similar problems, a (nonpolynomial) allocation that minimizes the makespan can be truthfully implemented. On the other hand, if we leave out the gametheoretic issues, the complexity of the problem has been completely settled--the problem is strongly NP-hard, while there exists a polynomial-time approximation scheme (PTAS) [D. S. Hochbaum and D. B. Shmoys, SIAM J. Comput., 17 (1988), pp. 539--551, and L. Epstein and J. Sgall, Algorithmica, 39(1) (2004), pp. 43--57]. This problem is the most well studied in single-parameter algorithmic mechanism design. It gives an excellent ground to explore the boundary between truthfulness and efficient computation. Since the work of Archer and Tardos, quite a lot of deterministic and randomized mechanisms have been suggested. Recently, a breakthrough result [P. Dhangwatnotai, S. Dobzinski, S. Dughmi, and T. Roughgarden, Proceedings of the 49th IEEE Symposium of Foundations of Computer Science, Philadelphia, 2008, pp. 15--24] showed that a randomized, truthful-in-expectation PTAS exists. On the other hand, for the deterministic case, the best known approximation factor is 2.8 [A. Kovács, Algorithms-ESA 2005, 13th Annual European Symposium, 2005, pp. 616--627, and A. Kovács, J. Discrete Algorithms, 7 (2009), pp. 327--340]. It has been a major open question whether there exists a deterministic truthful PTAS, or whether truthfulness has an essential, negative impact on the computational complexity of the problem. In this paper we give a definitive answer to this important question by providing a truthful deterministic PTAS. [ABSTRACT FROM AUTHOR]

We consider the sample complexity of revenue maximization for multiple bidders in unrestricted multidimensional settings. Specifically, we study the standard model of n additive bidders whose values for m heterogeneous items are drawn independently. For any such instance and any ε > 0, we show that it is possible to learn an ε-Bayesian Incentive Compatible auction whose expected revenue is within ε of the optimal ε-BIC auction from only polynomially many samples. Our fully nonparametric approach is based on ideas that hold quite generally and completely sidestep the difficulty of characterizing optimal (or near-optimal) auctions for these settings. Therefore, our results easily extend to general multi-dimensional settings, including valuations that are not necessarily even subadditive, and arbitrary allocation constraints. For the cases of a single bidder and many goods, or a single parameter (good) and many bidders, our analysis yields exact incentive compatibility (and for the latter also computational efficiency). Although the single-parameter case is already well understood, our corollary for this case extends slightly the state of the art. [ABSTRACT FROM AUTHOR]

This paper deals with the design of efficiently computable incentive-compatible mechanisms for combinatorial optimization problems with single-minded agents each possibly having multiple private parameters. We focus on approximation algorithms for NP-hard mechanism design problems. These algorithms need to satisfy certain monotonicity properties to ensure truthfulness. Since most of the known approximation techniques do not fulfill these properties, we study alternative techniques. Our first contribution is a quite general method to transform a pseudopolynomial algorithm into a monotone fully polynomial time approximation scheme (FPTAS). This can be applied to various problems like, e.g., knapsack, constrained shortest path, or job scheduling with deadlines. For example, the monotone FPTAS for the knapsack problem gives a very efficient, truthful mechanism for single-minded multiunit auctions. The best previous result for such auctions was a 2-approximation. In addition, we present a monotone PTAS for the generalized assignment problem with any constant number of private parameters per agent. The most efficient way to solve packing integer programs (PIPs) is linear programming-based randomized rounding, which also is in general not monotone. We show that primal-dual greedy algorithms achieve almost the same approximation ratios for PIPs as randomized rounding. The advantage is that these algorithms are inherently monotone. This way, we can significantly improve the approximation ratios of truthful mechanisms for various fundamental mechanism design problems like single-minded combinatorial auctions (CAs), unsplittable flow routing, and multicast routing. Our primal-dual approximation algorithms can also be used for the winner determination in CAs with general bidders specifying their bids through an oracle. [ABSTRACT FROM AUTHOR]

The routing of traffic between Internet domains, or Autonomous Systems (ASes), a task known as interdomain routing, is currently handled by the Border Gateway Protocol (BGP, Rekhter and Li in RFC 4271 of the Internet Engineering Task Force, ). Using BGP, ASes can apply semantically rich routing policies to choose interdomain routes in a distributed fashion. This expressiveness in routing-policy choice supports domains' autonomy in network operations and in business decisions, but it comes at a price: The interaction of locally defined routing policies can lead to unexpected global anomalies, including route oscillations or overall protocol divergence (see, e.g., Varadhan et al. in Comput Networks 32(1):1-16, ). Networking researchers have addressed this problem by devising constraints on policies that guarantee BGP convergence without unduly limiting expressiveness and autonomy (see, e.g., Gao and Rexford in IEEE/ACM Trans Network 9(6):681-692, ; Griffin et al. in Proceedings of 9th ACM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication (SIGCOMM'03), pp. 61-72. ACM Press, New York, ). In addition to taking this engineering or 'protocol- design' approach, researchers have approached interdomain routing from an economic or 'mechanism-design' point of view. It is known that lowest-cost-path (LCP) routing can be implemented in an incentive-compatible, BGP-compatible manner (Feigenbaum et al. in Distribut. Comput 18(1):61-72, ; Shneidman and Parkes in Proceedings of 23rd ACM Symposium on Principles of Distributed Computing (PODC'04), pp. 88-97. ACM Press, New York, ) but that several other natural classes of policies cannot (Feigenbaum et al. in Theor Comput Sci 378(2):175-189, ; Feigenbaum et al. in Distribut Comput 18(4):293-305, ). In this paper, we present the first example of a class of interdomain-routing policies that is more general than LCP routing and for which BGP itself is both incentive-compatible and guaranteed to converge. We also present several steps toward a general theory of incentive-compatible, BGP-compatible interdomain routing. [ABSTRACT FROM AUTHOR]

Abstract: We consider the problem of designing monotone deterministic algorithms for scheduling tasks on related machines in order to minimize the makespan. Several recent papers showed that monotonicity is a fundamental property to design truthful mechanisms for this scheduling problem. We give both theoretical and experimental results. First of all we consider the case of two machines when speeds of the machines are restricted to be powers of a given constant . We prove that algorithm Largest Processing Time (LPT) is monotone for any while it is not monotone for ; algorithm List Scheduling (LS), instead, is monotone only for . In the case of machines we restrict our attention to the class of “greedy-like” monotone algorithms defined in [Vincenzo Auletta, Roberto De Prisco, Paolo Penna, Giuseppe Persiano, Deterministic truthful approximation mechanisms for scheduling related machines, in: Proceedings of 21st Annual Symposium on Theoretical Aspects of Computer Science. STACS ’04, in: Lecture Notes in Computer Science, vol. 2996, Springer, 2004, pp. 608–619]. It has been shown that greedy-like monotone algorithms can be used to design a family of -approximate truthful mechanisms. In particular, in [Vincenzo Auletta, Roberto De Prisco, Paolo Penna, Giuseppe Persiano, Deterministic truthful approximation mechanisms for scheduling related machines, in: Proceedings of 21st Annual Symposium on Theoretical Aspects of Computer Science. STACS ’04, in: Lecture Notes in Computer Science, vol. 2996, Springer, 2004, pp. 608–619], the greedy-like algorithm Uniform is proposed and it is proved that it is monotone when machine speeds are powers of a given integer constant . In this paper we propose a new algorithm, called Uniform_RR, that is still monotone when speeds are powers of a given integer constant and we prove that its approximation factor is not worse than that of Uniform. We also experimentally compare the performance of Uniform, Uniform_RR, LPT, and several other monotone and greedy-like heuristics. [Copyright &y& Elsevier]

Let a communication network be modeled by an undirected graph $G=(V,E)$ of n nodes and m edges, and assume that edges are controlled by selfish agents, which privately hold the length of each owned edge. In this paper we analyze the problem of designing a truthful mechanism for computing one of the most popular network topologies, i.e. the single-source shortest paths tree. More precisely, we study several realistic scenarios, in which each agent can own either a single edge or multiple edges of G. In particular, for the single-edge scenario, we show that in the utilitarian case the problem can be efficiently solved in $O(mn \log{}\alpha(m,n))$ time, while in a meaningful non-utilitarian case, namely that in which agents' valuation functions depend only on the edge lengths, it can be solved in $O(m + n \log n)$ time. On the other hand, for the multiple-edge scenario, in the utilitarian case we show an $O(mn+n^2 \log n)$ time truthful mechanism, while in the same non-utilitarian case we provide an n-approximate truthful mechanism which can be implemented in $O(m n \,\alpha(m,n))$ time. We also show that in the special case in which, for every agent, the owned edges are all incident to the same node, then this latter mechanism has an almost optimal $O(m\,\alpha(m,n))$ runtime. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: We study a new facility game, namely, an obnoxious facility game, on a network where the facility is undesirable and all agents try to be as far away from the facility as possible. The following process is considered: at first the agents declare their locations, then, given these bids, a mechanism selects a place on the network to locate the facility. The aim of the mechanism is to maximize the obnoxious social welfare, i.e., the total distance between the agents and the facility. The objective of each agent is to maximize his/her utility, i.e., the distance from the facility. Thus an agent may lie if, by doing so, he/she can get strictly more benefit. We are interested in mechanisms without money to decide the facility location so that the obnoxious social welfare is maximized and all agents are enforced to report their true locations. In this paper we give a first attempt at this game on different networks. Our main results are the following. When the network is a path, we show a 3-approximation group strategy-proof deterministic mechanism which is best possible if the facility can only take one of the endpoints on the path, and a group strategy-proof randomized mechanism with tight approximation ratio of . When the networks are a circle (known as a ring in the case of computer networks) and a tree, we propose two group strategy-proof deterministic mechanisms that each provides the approximation ratio of 3. Furthermore, when all agents are on a general network, we propose a 4-approximation group strategy-proof deterministic mechanism and a 2-approximation group strategy-proof randomized mechanism. [Copyright &y& Elsevier]

Abstract: In classical mechanism design the outcome of the mechanism is computed by a trusted central party. In this paper, we consider the design of distributed mechanisms in which the outcome is computed by the agents themselves. We propose Distributed MinWork (DMW), a mechanism for solving the problem of scheduling on unrelated machines. We show that DMW is a faithful implementation of the MinWork mechanism, which was proposed by Nisan and Ronen in their seminal work (Nisan and Ronen (2001) ). We show that in addition to being faithful, DMW protects the anonymity of the losing agents and the privacy of their bids. Furthermore, we show that DMW is efficient as it has polynomial communication and computation costs. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we address the question of designing truthful mechanisms for solving optimization problems on dynamic graphs with selfish edges. More precisely, we are given a graph of nodes, and we assume that each edge of is owned by a selfish agent. The strategy of an agent consists in revealing to the system–at each time instant–the cost at the actual time for using its edge. Additionally, edges can enter into and exit from . Among the various possible assumptions which can be made to model how this edge-cost modifications take place, we focus on two settings: (i) the dynamic, in which modifications can happen at any time, and for a given optimization problem on , the mechanism has to maintain efficiently the output specification and the payment scheme for the agents; (ii) the time-sequenced, in which modifications happens at fixed time steps, and the mechanism has to minimize an objective function which takes into consideration both the quality and the set-up cost of a new solution. In both settings, we investigate the existence of exact and approximate truthful (w.r.t. to suitable equilibrium concepts) mechanisms. In particular, for the dynamic setting, we analyze the minimum spanning tree problem, and we show that if edge costs can only decrease and each agent adopts a myopic best response strategy (i.e., its utility is only measured instantaneously), then there exists an efficient dynamic truthful (in myopic best response equilibrium) mechanism for handling a sequence of declarations of edge-cost reductions having runtime , where is the overall number of payment changes. [Copyright &y& Elsevier]

Abstract: In this paper we study optimization problems with verifiable one-parameter selfish agents introduced by Auletta et al. [V. Auletta, R. De Prisco, P. Penna, P. Persiano, The power of verification for one-parameter agents, in: Proceedings of the 31st International Colloquium on Automata, Languages and Programming, ICALP, in: LNCS, vol. 3142, 2004, pp. 171–182]. Our goal is to allocate load among the agents, provided that the secret data of each agent is a single positive real number: the cost they incur per unit load. In such a setting the payment is given after the load completion, therefore if a positive load is assigned to an agent, we are able to verify if the agent declared to be faster than she actually is. We design truthful mechanisms when the agents’ type sets are upper-bounded by a finite value. We provide a truthful mechanism that is -approximate if the underlying algorithm is -approximate and weakly-monotone. Moreover, if type sets are also discrete, we provide a truthful mechanism preserving the approximation ratio of its algorithmic part. Our results improve the existing ones which provide truthful mechanisms dealing only with finite type sets and do not preserve the approximation ratio of the underlying algorithm. Finally, we give applications for our payment schemes. Firstly, we give a full characterization of the problem by using our techniques. Even if our payment schemes need upper-bounded type sets, every instance of can be “mapped” into an instance with upper-bounded type sets preserving the approximation ratio. In conclusion, we turn our attention to binary demand games. In particular, we show that the Minimum Radius Spanning Tree admits an exact truthful mechanism with verification achieving time (and space) complexity of the fastest centralized algorithm for it. This contrasts with a recent truthful mechanism for the same problem [G. Proietti, P. Widmayer, A truthful mechanism for the non-utilitarian minimum radius spanning tree problem, in: Proceedings of the 17th ACM Symposium on Parallelism in Algorithms and Architectures, SPAA, ACM Press, 2005, pp. 195–202] which pays a linear factor with respect to the complexity of the fastest centralized algorithm. Such a result is extended to several binary demand games studied in literature. [Copyright &y& Elsevier]

Replicating data over geographically dispersed web servers reduces network traffic, server load, and more importantly the user-perceived access delays. This paper proposes a unique replica placement technique using the concepts of a supergame. The supergame allows the agents who represent the data objects to continuously compete for the limited available server memory space, so as to acquire the rights to place data objects at the servers. At any given instance in time, the supergame is represented by a game which is a collection of subgames, played concurrently at each server in the system. We derive a resource allocation mechanism which acts as a platform at the subgame level for the agents to compete. This approach allows us to transparently monitor the actions of the agents, who in a noncooperative environment strategically place the data objects to reduce the user access time, latency, which in turn adds reliability and fault-tolerance to the system. We show that this mechanism exhibits Nash equilibrium at the subgame level which in turn conforms to games and supergame Nash equilibrium, respectively, guaranteeing the entire system to be in a continuous self-evolving and selfrepairing mode. The mechanism is extensively evaluated against some well-known algorithms, such as: greedy, branch and bound, game theoretical auctions and genetic algorithms. The experimental results reveal that the mechanism provides excellent solution quality, while maintaining fast execution time. [ABSTRACT FROM AUTHOR]

Abstract: A crucial issue in non-cooperative wireless networks is that of sharing the cost of multicast transmissions to different users residing at the stations of the network. Each station acts as a selfish agent that may misreport its utility (i.e., the maximum cost it is willing to incur to receive the service, in terms of power consumption) in order to maximize its individual welfare, defined as the difference between its true utility and its charged cost. A provider can discourage such deceptions by using a strategyproof cost sharing mechanism, that is a particular public algorithm that, by forcing the agents to truthfully reveal their utility, starting from the reported utilities, decides who gets the service (the receivers) and at what price. A mechanism is said budget balanced (BB) if the receivers pay exactly the (possibly minimum) cost of the transmission, and -approximate budget balanced (-BB) if the total cost charged to the receivers covers the overall cost and is at most times the optimal one, while it is efficient if it maximizes the sum of the receivers’ utilities minus the total cost over all receivers’ sets. In this paper, we first investigate cost sharing strategyproof mechanisms for symmetric wireless networks, in which the powers necessary for exchanging messages between stations may be arbitrary and we provide mechanisms that are either efficient or BB when the power assignments are induced by a fixed universal spanning tree, or -BB ( is the number of receivers), otherwise. Then we consider the case in which the stations lay in a -dimensional Euclidean space and the powers fall as , and provide strategyproof mechanisms that are either -BB or efficient for or . Finally, we show the existence of -BB strategyproof mechanisms in any -dimensional space for every . For the special case of such a result can be improved to achieve -BB mechanisms. [Copyright &y& Elsevier]

We design the first truthful-in-expectation, constant-factor approximation mechanisms for NP-hard cases of the welfare maximization problem in combinatorial auctions with nonidentical items and in combinatorial public projects. Our results apply to bidders with valuations that are nonnegative linear combinations of gross-substitute valuations, a class that encompasses many of the most well-studied subclasses of submodular functions, including coverage functions and weighted matroid rank functions. Our mechanisms have an expected polynomial runtime and achieve an approximation factor of 1 - 1/e. This approximation factor is the best possible for both problems, even for known and explicitly given coverage valuations, assuming P= NP. Recent impossibility results suggest that our results cannot be extended to a significantly larger valuation class. Both of our mechanisms are instantiations of a new framework for designing approximation mechanisms based on randomized rounding algorithms. The high-level idea of this framework is to optimize directly over the (random) output of the rounding algorithm, rather than the usual (and rarely truthful) approach of optimizing over the input to the rounding algorithm. This framework yields truthful-in-expectation mechanisms, which can be implemented efficiently when the corresponding objective function is concave. For bidders with valuations in the cone generated by gross-substitute valuations, we give novel randomized rounding algorithms that lead to both a concave objective function and a (1 - 1/e)-approximation of the optimal welfare. [ABSTRACT FROM AUTHOR]

It is widely believed that computing payments needed to induce truthful bidding is somehow harder than simply computing the allocation. We show that the opposite is true: creating a randomized truthful mechanism is essentially as easy as a single call to a monotone allocation rule. Our main result is a general procedure to take a monotone allocation rule for a single-parameter domain and transform it (via a black-box reduction) into a randomized mechanism that is truthful in expectation and individually rational for every realization. The mechanism implements the same outcome as the original allocation rule with probability arbitrarily close to 1, and requires evaluating that allocation rule only once.We also provide an extension of this result to multiparameter domains and cycle-monotone allocation rules, under mild star-convexity and nonnegativity hypotheses on the type space and allocation rule, respectively. Because our reduction is simple, versatile, and general, it has many applications to mechanism design problems in which re-evaluating the allocation rule is either burdensome or informationally impossible. Applying our result to the multiarmed bandit problem, we obtain truthful randomized mechanisms whose regret matches the information-theoretic lower bound up to logarithmic factors, even though prior work showed this is impossible for truthful deterministic mechanisms. We also present applications to offline mechanism design, showing that randomization can circumvent a communication complexity lower bound for deterministic payments computation, and that it can also be used to create truthful shortest path auctions that approximate the welfare of the VCG allocation arbitrarily well, while having the same running time complexity as Dijkstra's algorithm. [ABSTRACT FROM AUTHOR]

We present an interactive tool compatible with existing software (Rhino/Grasshopper) to design ring structures with a paradoxic mobility, which are self‐collision‐free over the complete motion cycle. Our computational approach allows non‐expert users to create these invertible paradoxic loops with six rotational joints by providing several interactions that facilitate design exploration. In a first step, a rational cubic motion is shaped either by means of a four pose interpolation procedure or a motion evolution algorithm. By using the representation of spatial displacements in terms of dual‐quaternions, the associated motion polynomial of the resulting motion can be factored in several ways, each corresponding to a composition of three rotations. By combining two suitable factorizations, an arrangement of six rotary axes is achieved, which possesses a 1‐parametric mobility. In the next step, these axes are connected by links in a way that the resulting linkage is collision‐free over the complete motion cycle. Based on an algorithmic solution for this problem, collision‐free design spaces of the individual links are generated in a post‐processing step. The functionality of the developed design tool is demonstrated in the context of an architectural and artistic application studied in a master‐level studio course. Two results of the performed design experiments were fabricated by the use of computer‐controlled machines to achieve the necessary accuracy ensuring the mobility of the models. [ABSTRACT FROM AUTHOR]

The problem of pollution control has been mainly studied in the environmental economics literature where the methodology of game theory is applied for the pollution control. To the best of our knowledge this is the first time this problem is studied from the computational point of view. We introduce a new network model for pollution control and present two applications of this model. On a high level, our model comprises a graph whose nodes represent the agents, which can be thought of as the sources of pollution in the network. The edges between agents represent the effect of spread of pollution. The government who is the regulator, is responsible for the maximization of the social welfare and sets bounds on the levels of emitted pollution in both local areas as well as globally in the whole network. We first prove that the above optimization problem is NP-hard even on some special cases of graphs such as trees. We then turn our attention on the classes of trees and planar graphs which model realistic scenarios of the emitted pollution in water and air, respectively. We derive approximation algorithms for these two kinds of networks and provide deterministic truthful and truthful in expectation mechanisms. In some settings of the problem that we study, we achieve the best possible approximation results under standard complexity theoretic assumptions. Our approximation algorithm on planar graphs is obtained by a novel decomposition technique to deal with constraints on vertices. We note that no known planar decomposition techniques can be used here and our technique can be of independent interest. For trees we design a two level dynamic programming approach to obtain an FPTAS. This approach is crucial to deal with the global pollution quota constraint. It uses a special multiple choice, multi-dimensional knapsack problem where coefficients of all constraints except one are bounded by a polynomial of the input size. We furthermore derive truthful in expectation mechanisms on general networks with bounded degree. [ABSTRACT FROM AUTHOR]

The recent "incentive auction" of the US Federal Communications Commission was the first auction to reallocate radio frequencies between two different kinds of uses: from broadcast television to wireless Internet access. The design challenge was not just to choose market rules to govern a fixed set of potential trades but also, to determine the broadcasters' property rights, the goods to be exchanged, the quantities to be traded, the computational procedures, and even some of the performance objectives. An essential and unusual challenge was to make the auction simple enough for human participants while still ensuring that the computations would be tractable and capable of delivering nearly efficient outcomes. [ABSTRACT FROM AUTHOR]

Algorithmic Mechanism Design attempts to marry computation and incentives, mainly by leveraging monetary transfers between designer and selfish agents involved. This is principally because in absence of money, very little can be done to enforce truthfulness. However, in certain applications, money is unavailable, morally unacceptable or might simply be at odds with the objective of the mechanism. For example, in combinatorial auctions (CAs), the paradigmatic problem of the area, we aim at solutions of maximum social welfare but still charge the society to ensure truthfulness. Additionally, truthfulness of CAs is poorly understood already in the case in which bidders happen to be interested in only two different sets of goods. We focus on the design of incentive-compatible CAs without money in the general setting of k-minded bidders. We trade monetary transfers with the observation that the mechanism can detect certain lies of the bidders: i.e., we study truthful CAs with verification and without money. We prove a characterization of truthful mechanisms, which makes an interesting parallel with the well-understood case of CAs with money for single-minded bidders. We then give a host of upper bounds on the approximation ratio obtained by either deterministic or randomized truthful mechanisms when the sets and valuations are private knowledge of the bidders. (Most of these mechanisms run in polynomial time and return solutions with (nearly) best possible approximation guarantees.) We complement these positive results with a number of lower bounds (some of which are essentially tight) that hold in the easier case of public sets. We thus provide an almost complete picture of truthfully approximating CAs in this general setting with multi-dimensional bidders. [ABSTRACT FROM AUTHOR]