2,325 research outputs found

    Feature Selection via Coalitional Game Theory

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    We present and study the contribution-selection algorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the multiperturbation shapley analysis (MSA), a framework that relies on game theory to estimate usefulness. The algorithm iteratively estimates the usefulness of features and selects them accordingly, using either forward selection or backward elimination. It can optimize various performance measures over unseen data such as accuracy, balanced error rate, and area under receiver-operator-characteristic curve. Empirical comparison with several other existing feature selection methods shows that the backward elimination variant of CSA leads to the most accurate classification results on an array of data sets

    Feature Selection via Coalitional Game Theory

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    Game-theoretic Resource Allocation Methods for Device-to-Device (D2D) Communication

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    Device-to-device (D2D) communication underlaying cellular networks allows mobile devices such as smartphones and tablets to use the licensed spectrum allocated to cellular services for direct peer-to-peer transmission. D2D communication can use either one-hop transmission (i.e., in D2D direct communication) or multi-hop cluster-based transmission (i.e., in D2D local area networks). The D2D devices can compete or cooperate with each other to reuse the radio resources in D2D networks. Therefore, resource allocation and access for D2D communication can be treated as games. The theories behind these games provide a variety of mathematical tools to effectively model and analyze the individual or group behaviors of D2D users. In addition, game models can provide distributed solutions to the resource allocation problems for D2D communication. The aim of this article is to demonstrate the applications of game-theoretic models to study the radio resource allocation issues in D2D communication. The article also outlines several key open research directions.Comment: Accepted. IEEE Wireless Comms Mag. 201

    The Core of a Normal Form Game

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    Due to the externalities, in normal form games a deviation changes the payoff of all players inducing a retaliation by the remaining or residual players. The stability of an outcome depends on the expectations potential deviators have about this reaction, but so far no satisfactory theory has been provided. The present paper continues the work of Chander and Tulkens (1997) where deviators consider residual equilibria, but we allow coalitions to form, moreover introduce consistency between the residual solution and the solution of the original game. Optimistic and pessimistic considerations produce a pair of cores. These cores are compared to some existing cooperative concepts such as the gamma- and r-cores and the equilibrium binding agreements. In our final section we discuss the predominance of the grand coalition and suggest a generalisation of the normal form where such a precedence can be removed.externalities, residual game, cohesiveness, partition function

    Information transmission in coalitional voting games

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    A core allocation of a complete information economy can be characterized as one that would not be unanimously rejected in favor of another feasible alternative by any coalition. We use this test of coalitional voting in an incomplete information environment to formalize a notion of resilience. Since information transmission is implicit in the Bayesian equilibria of such voting games, this approach makes it possible to derive core concepts in which the transmission of information among members of a coalition is endogenous. Our results lend support to the credible core of Dutta and Vohra [4] and the core proposed by Myerson [11] as two that can be justified in terms of coalitional votin

    Coalitional Games with Overlapping Coalitions for Interference Management in Small Cell Networks

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    In this paper, we study the problem of cooperative interference management in an OFDMA two-tier small cell network. In particular, we propose a novel approach for allowing the small cells to cooperate, so as to optimize their sum-rate, while cooperatively satisfying their maximum transmit power constraints. Unlike existing work which assumes that only disjoint groups of cooperative small cells can emerge, we formulate the small cells' cooperation problem as a coalition formation game with overlapping coalitions. In this game, each small cell base station can choose to participate in one or more cooperative groups (or coalitions) simultaneously, so as to optimize the tradeoff between the benefits and costs associated with cooperation. We study the properties of the proposed overlapping coalition formation game and we show that it exhibits negative externalities due to interference. Then, we propose a novel decentralized algorithm that allows the small cell base stations to interact and self-organize into a stable overlapping coalitional structure. Simulation results show that the proposed algorithm results in a notable performance advantage in terms of the total system sum-rate, relative to the noncooperative case and the classical algorithms for coalitional games with non-overlapping coalitions

    Efficient computation of the Shapley value for game-theoretic network centrality

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    The Shapley value—probably the most important normative payoff division scheme in coalitional games—has recently been advocated as a useful measure of centrality in networks. However, although this approach has a variety of real-world applications (including social and organisational networks, biological networks and communication networks), its computational properties have not been widely studied. To date, the only practicable approach to compute Shapley value-based centrality has been via Monte Carlo simulations which are computationally expensive and not guaranteed to give an exact answer. Against this background, this paper presents the first study of the computational aspects of the Shapley value for network centralities. Specifically, we develop exact analytical formulae for Shapley value-based centrality in both weighted and unweighted networks and develop efficient (polynomial time) and exact algorithms based on them. We empirically evaluate these algorithms on two real-life examples (an infrastructure network representing the topology of the Western States Power Grid and a collaboration network from the field of astrophysics) and demonstrate that they deliver significant speedups over the Monte Carlo approach. Fo
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