The modeling of coalition for environmental protection within co-utility framework

The game theoretic modelling of coalition for environmental protection within the framework of a new concept of co-utility is discussed. The co-utility concept contains mainly two elements. Firstly, agents can increase their payoffs by collaborating with each-other. Secondly, the outcome of collaboration is robust towards internal and external disturbances. The advantages of using of co-utility are two-fold. Primarily, the co-utility concept is broad and can serve as an umbrella concept in all applications where agents have a space for simultaneous improvement of payoffs. Secondly the co-utility concept can be associated with different stability concept such as myopic or farsighted stability. The myopic and farsighted co-utile sets are defined and their element-co-utile outcomes are found.     

The cultural Red King effect

Why do minority groups tend to be discriminated against when it comes to situations of bargaining and resource division? In this article, I explore an explanation for this disadvantage that appeals solely to the dynamics of social interaction between minority and majority groups—the cultural Red King effect (Bruner, 2017). As I show, in agent-based models of bargaining between groups, the minority group will tend to get less as a direct result of the fact that they frequently interact with majority group members, while majority group members meet them only rarely. This effect is strengthened by certain psychological phenomenon—risk aversion and in-group preference—is robust on network models, and is strengthened in cases where preexisting norms are discriminatory. I will also discuss how this effect unifies previous results on the impacts of institutional memory on bargaining between groups.     

SYSTEM OF VECTOR QUASI-EQUILIBRIUM PROBLEMS AND ITS APPLICATIONS

A new system of vector quasi-equilibrium problems is introduced and its existence of solution is proved. As applications, some existence results of weak Pareto equilibrium for both constrained multicriteria games and multicriteria games without constrained correspondences are also shown.     

Micromechanics-based elastic damage modeling of particulate composites with weakened interfaces

A micromechanical framework is proposed to predict the effective elastic behavior and weakened interface evolution of particulate composites. The Eshelby’s tensor for an ellipsoidal inclusion with slightly weakened interface [Qu, J., 1993a. Eshelby tensor for an elastic inclusion with slightly weakened interfaces. Journal of Applied Mechanics 60 (4), 1048–1050; Qu, J., 1993b. The effect of slightly weakened interfaces on the overall elastic properties of composite materials. Mechanics of Materials 14, 269–281] is adopted to model spherical particles having imperfect interfaces in the composites and is incorporated into the micromechanical framework. Based on the Eshelby’s micromechanics, the effective elastic moduli of three-phase particulate composites are derived. A damage model is subsequently considered in accordance with the Weibull’s probabilistic function to characterize the varying probability of evolution of weakened interface between the inclusion and the matrix. The proposed micromechanical elastic damage model is applied to the uniaxial, biaxial and triaxial tensile loadings to predict the various stress–strain responses. Comparisons between the present predictions with other numerical and analytical predictions and available experimental data are conducted to assess the potential of the present framework.     

基于生命期模型的无线传感器网络信道分配博弈算法

针对无线传感器网络中节点因干扰过大导致重传能耗增加, 进而节点过早失效、网络生命期缩短的问题, 根据网络拓扑信息和路由信息设计节点的负载模型, 从而构建了节点的生命期模型. 然后利用博弈论将路径增益、交叉干扰和节点生命期等性能参数融入到效益函数中, 构建信道分配博弈模型. 理论分析证明该博弈模型存在纳什均衡. 进而运用最佳回应策略, 在所构建的信道分配博弈模型的基础上, 设计了一种优化网络生命期的抗干扰信道分配算法. 该算法使节点在选择信道时避免与网络中交叉干扰较大的节点和生命期较小的节点使用相同信道, 实现干扰小、能耗低且均衡的信道选择. 理论分析与仿真结果证明该算法最终能够快速地收敛到纳什均衡, 且具有较小的信息复杂度, 从而减小算法本身的通信能耗. 同时, 该算法具有良好的抗干扰性和信道均衡性, 能够有效地延长网络生命期.     

A hybrid shuffled complex evolution approach based on differential evolution for unconstrained optimization

Numerous optimization methods have been proposed for the solution of the unconstrained optimization problems, such as mathematical programming methods, stochastic global optimization approaches, and metaheuristics. In this paper, a metaheuristic algorithm called Modified Shuffled Complex Evolution (MSCE) is proposed, where an adaptation of the Downhill Simplex search strategy combined with the differential evolution method is proposed. The efficiency of the new method is analyzed in terms of the mean performance and computational time, in comparison with the genetic algorithm using floating-point representation (GAF) and the classical shuffled complex evolution (SCE-UA) algorithm using six benchmark optimization functions. Simulation results and the comparisons with SCE-UA and GAF indicate that the MSCE improves the search performance on the five benchmark functions of six tested functions.     

Quantum-inspired space search algorithm (QSSA) for global numerical optimization

This work presents the evolutionary quantum-inspired space search algorithm (QSSA) for solving numerical optimization problems. In the proposed algorithm, the feasible solution space is decomposed into regions in terms of quantum representation. As the search progresses from one generation to the next, the quantum bits evolve gradually to increase the probability of selecting the regions that render good fitness values. Through the inherent probabilistic mechanism, the QSSA initially behaves as a global search algorithm and gradually evolves into a local search algorithm, yielding a good balance between exploration and exploitation. To prevent a premature convergence and to speed up the overall search speed, an overlapping strategy is also proposed. The QSSA is applied to a series of numerical optimization problems. The experiments show that the results obtained by the QSSA are quite competitive compared to those obtained using state-of-the-art IPOP-CMA-ES and QEA.     

Embedding processes in combinatorial game theory

Berlekamp asked the question “What is the habitat of ∗2?” (See Guy, 1996 [6].) It is possible to generalize the question and ask “For a game G, what is the largest n such that ∗n is a position of G?” This leads to the concept of the nim dimension. In Santos and Silva (2008) [8] a fractal process was proposed for analyzing the previous questions. For the same purpose, in Santos and Silva (2008) [9], an algebraic process was proposed. In this paper we implement a third idea related to embedding processes. With Alan Parr’s traffic lights, we exemplify the idea of estimating the “difficulty” of the game and proving that its nim dimension is infinite.     

Some remarks on a game of Telgársky

In [3] R. Telgársky (1975) asked: does the first player have a winning strategy in the game G(F,X×X) if the first player has a winning strategy in the game G(F,X)? I give a positive answer to this question and prove that this result is also true for spaces where the first player has a winning strategy in game G(K,X) where K=1, F, C, for σC if X is P-space and for DC if X is collectionwise-normal space. The last result is related to the Telgársky's (1983) conjecture discussed in [1]. These results are not true for infinite product of spaces.