An evolutionary game theory model of binary opinion formation

A basic characteristic of most opinion models is that people tend to agree or compromise in the opinion interaction, which could be hopefully described by cooperative games in the evolutionary game theory framework. This paper presents game theory methods to model the formation of binary opinions: cooperative games are proposed to model the interaction rules of general people who tend to find an agreement; minority games are proposed to model the behaviors of contrarians; opinion preference is considered by varying the payoff values. The Majority Voter model could be restored from the proposed games. The game theory models show evolutionary results similar to traditional opinion models. Specially, the evolution of opinions with consideration of contrarians is in accordance with the Galam model. Furthermore, influences of evolving rule, network topology and initial distribution of opinions are studied through numerical simulations. Discussions about methods to promote or hinder the consensus state at the best equilibrium point are given.     

Zero-determinant strategy:An underway revolution in game theory

Repeated games describe situations where players interact with each other in a dynamic pattern and make decisions according to outcomes of previous stage games. Very recently, Press and Dyson have revealed a new class of zero-determinant(ZD) strategies for the repeated games, which can enforce a fixed linear relationship between expected payoffs of two players, indicating that a smart player can control her unwitting co-player’s payoff in a unilateral way [Proc. Acad. Natl. Sci.USA 109, 10409(2012)]. The theory of ZD strategies provides a novel viewpoint to depict interactions among players,and fundamentally changes the research paradigm of game theory. In this brief survey, we first introduce the mathematical framework of ZD strategies, and review the properties and constrains of two specifications of ZD strategies, called pinning strategies and extortion strategies. Then we review some representative research progresses, including robustness analysis,cooperative ZD strategy analysis, and evolutionary stability analysis. Finally, we discuss some significant extensions to ZD strategies, including the multi-player ZD strategies, and ZD strategies under noise. Challenges in related research fields are also listed.     

Reputation-based co-evolutionary model promotes cooperation in prisoner's dilemma game

Indirect reciprocity is a fascinating topic in the field of social cooperation. In this paper, we propose a novel updating strategy based on the critical reputation-aware calculation. The joint of reputation allows players to make decisions not only on current payoffs but also from a third party, which improves the status of cooperators in the prisoner's dilemma game and provides a possibility for surviving. Experiments show that the discrepancies in initial fitness caused by reputation will support cooperators in occupying a high proportion in communities. Interestingly, we find that the massive scale of reputation fluctuation helps to enhance the cooperative effect, and newly name this character as “quasi-time lag”. The simulations show that the promotion of our proposed mechanism is effective and robust on different network topologies. This work provides a new perspective for the study of the cooperative game.     

The roles of heterogeneous investment mechanism in the public goods game on scale-free networks

The public goods game is an important theoretical model for investigating the emergence of cooperation in the multi-player social dilemma. It has been proven that scale-free networks can significantly promote cooperation, but fail to sustain cooperation when the player obtains the normalized payoff. In this paper, we introduce heterogeneous investment mechanism into the public goods game on scale-free networks, and study the evolution of cooperation in both cases of accumulated and normalized payoff. Our research reveals that the heterogeneous investment mechanism can obviously facilitate cooperation as the adjusted parameter α increases. The increase of α allows cooperators to emerge under lower values of r. In the case of accumulated payoff, cooperators always firmly occupy the hubs, and the population keeps high cooperation level. In the case of normalized payoff, the increase of α changes the situation that the hubs are easily invaded by defectors, and inhibits the spread of defectors.     

Preferential selection promotes cooperation in a spatial public goods game

We introduce a preferential selection mechanism into a spatial public goods game where players are located on a square lattice. Each individual chooses one of its neighbors as a reference with a probability proportional to , where P_y is the neighbor’s payoff and A (≥0) is a tunable parameter. It is shown that the introduction of such a preferential selection can remarkably promote the emergence of cooperation over a wide range of the multiplication factor. We find that the mean payoffs of cooperators along the boundary are higher than that of defectors and cooperators form larger clusters as A increases. The extinction thresholds of cooperators and defectors for different values of noise are also investigated.     

Memory-based evolutionary game on small-world network with tunable heterogeneity

Most papers about evolutionary games on graph assume agents have no memory. Yet, in the real world, interaction history can also affect an agent’s decision. So we introduce a memory-based agent model and investigate the Prisoner’s Dilemma game on a Heterogeneous Newman-Watts small-world network based on a Genetic Algorithm, focusing on heterogeneity’s role in the emergence of cooperative behaviors. In contrast with previous results, we find that a different heterogeneity parameter domain range imposes an entirely different impact on the cooperation fraction. In the parameter range corresponding to networks with extremely high heterogeneity, the decrease in heterogeneity greatly promotes the proportion of cooperation strategy, while in the remaining parameter range, which relates to relatively homogeneous networks, the variation of heterogeneity barely affects the cooperation fraction. Also our study provides a detailed insight into the microscopic factors that contribute to the performance of cooperation frequency.     

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.     

Network selection algorithm based on link quality parameters for heterogeneous wireless networks

Next generation wireless networks consist of heterogeneous access technologies. In order to provide global ubiquitous communication, it is required to provide a framework in which user can move across multiple access interfaces while maintaining its ongoing communication at perceived quality of service. Given the scenario of multiple access networks, it is further required to select the optimum network out of multiple candidate networks to meet the requirements of the ongoing session. The selection of optimum network in such heterogeneous environment is generally based on network conditions and user preference. In this paper, we propose an algorithm for network selection based on averaged received signal strength, outage probability and distance. The proposed algorithm comprises of two stages. Assuming that network conditions are dominant in network selection, in first stage, overlapping region is identified through distance estimation. Network selection algorithm based on averaged received signal strength plus outage is invoked in second stage to select the optimum network. Numerical results are obtained through a simulation model of two disparate networks – GSM and UMTS. It has been shown that the proposed algorithm offers 68% improved performance in terms of network selection rate.     

Spatially correlated heterogeneous aspirations to enhance network reciprocity

Perc & Wang demonstrated that aspiring to be the fittest under conditions of pairwise strategy updating enhances network reciprocity in structured populations playing 2×2 Prisoner’s Dilemma games (Z. Wang, M. Perc, Aspiring to the fittest and promoted of cooperation in the Prisoner’s Dilemma game, Physical Review E 82 (2010) 021115; M. Perc, Z. Wang, Heterogeneous aspiration promotes cooperation in the Prisoner’s Dilemma game, PLOS one 5 (12) (2010) e15117). Through numerical simulations, this paper shows that network reciprocity is even greater if heterogeneous aspirations are imposed. We also suggest why heterogeneous aspiration fosters network reciprocity. It distributes strategy updating speed among agents in a manner that fortifies the initially allocated cooperators’ clusters against invasion. This finding prompted us to further enhance the usual heterogeneous aspiration cases for heterogeneous network topologies. We find that a negative correlation between degree and aspiration level does extend cooperation among heterogeneously structured agents.     

Escort evolutionary game theory

A family of replicator-like dynamics, called the escort replicator dynamic, is constructed using information-geometric concepts and generalized information divergences from information geometry and statistical thermodynamics. A single-formula Lyapunov function is given that covers the entire class of dynamics, which includes the replicator dynamic and the projection dynamic, as well as several new dynamics. A further class is discussed that allows for more variation, such as variable intensities of selection.     

Particle swarm optimization based network selection in heterogeneous wireless environment

Deployment of heterogeneous wireless networks is spreading throughout the world as users want to be connected anytime, anywhere, and anyhow. Meanwhile, users are increasingly interested in multimedia applications such as audio, video streaming and Voice over IP (VoIP), which require strict Quality of Service (QoS) support. Provisioning of Always Best Connected (ABC) network with such constraints is a challenging task. Considering the availability of various access technologies, it is difficult for a network operator to find reliable criteria to select the best network that ensures user satisfaction while reducing multiple network selection. Designing an efficient Network selection algorithm, in this type of environment, is an important research problem. In this paper, we propose a novel network selection algorithm utilizing signal strength, available bit rate, signal to noise ratio, achievable throughput, bit error rate and outage probability metrics as criteria for network selection. The selection metrics are combined with PSO for relative dynamic weight optimization. The proposed algorithm is implemented in a typical heterogeneous environment of EDGE (2.5G) and UMTS (3G). Switching rate of the user between available networks has been used as the performance metric. Moreover, a utility function is used to maintain desired QoS during transition between networks, which is measured in terms of the throughput. It is shown here that PSO based approach yields optimal network selection in heterogeneous wireless environment.     

Channel indicator-based intelligent network selection scheme for device-to-device communication

Device-to-device (D2D) communication has captured the researchers attention working in data-intensive applications. It has various benefits, such as low communication latency, load balancing, high spectral efficiency, and many more. However, despite these benefits, it has significant issues like efficient resource allocation, device discovery, and interference mitigation. Various solutions have given by the researchers to tackle these issues and the research community accepts them well. Here, we are targeting the issues associated with the device discovery, i.e., the base station assisted discovery. The initial step for D2D communication is the device discovery that the base station can perform. But, if the channel indicator parameters of the base station are not good, then the device discovery and further data sharing will be affected. Thus, there is a need for the best base station selection that improves the efficiency of the overall network. Many network selection solutions (for cellular networks) are available in the literature, but none of it talked about in the D2D communication scenario. So, motivated by this, this paper proposes an AI-based intelligent and efficient network selection scheme for D2D users to improve the device discovery experience and overall system’s sum rate. We then evaluate the performance of the proposed scheme using various evaluation metrics, such as accuracy, precision, recall, receiver operating curve (ROC), computation time, and sum rate.     

基于演化博弈的社交网络模型演化研究

社会网络研究的兴起,为网络演化规律研究提供了有效工具,但大多数研究集中从宏观机制评估网络演化的动态过程. 本文基于公共品博弈,通过演化博弈与网络拓扑共演化方式,从微观角度提出了多社区动态网络演化模型（dMCPGG）. 即以节点间演化博弈为动力,修改节点间边的关系,驱动网络拓扑演化. 考虑到网络异质性,采用基于拓扑势的偏好规则更准确全面的描述节点影响力. 通过数值模拟和仿真实验,验证了本模型的合理性,不仅重现了无标度网络及随机网络的节点度、聚类系数及平均路径长度的结构特性,还准确捕捉到真实社交网络的演化过程. 关键词： 公共品博弈 拓扑势 偏好选择 共演化     

基于演化博弈论的行人与机动车冲突演化机理研究

行人与机动车冲突时,各自都会在经过简单判断后以一定的概率选择通过.本文根据人车冲突的实际情景提出基础收益、冲突损失、等待损失以及互让损失的概念,据此构建行人与机动车的冲突博弈矩阵,并依据演化分析范式,建立人车冲突演化的动力学模型.对不同交通情形下均衡点的位置、稳定性以及系统演化机理进行深入分析,发现不同的行人与机动车的冲突损失和等待损失相对大小,对应系统的演化方向不同,可能的演化方向包括"人让车","车让人","人让车,同时车让人"以及"人不让车,车不让人".此外,定义机会损失的交通概念,据此分析系统关于行人与机动车的互让损失以及机会损失的灵敏度,发现行人或机动车互让损失的增加对于各自通过概率有着上升促进和下降抑制作用,而机会损失的作用恰好与互让损失相反.本文建立的动力学模型可以为人车冲突演化方向的宏观调控提供理论依据.     

Evolutionary game on a stochastic growth network

In some real complex systems the structures are difficult to map or changing over time. To explore the evolution of strategies on these complex systems, it is not realistic enough to specify their structures or topological properties in advance. In this paper, we address the evolutionary game on a stochastic growth network adopting the prisoner’s dilemma game. We introduce a growing rate q$q$ to control the ratio of network growth to strategy evolution. A large q$q$ denotes that the network grows faster than strategy evolution. Simulation results show that a fast growing rate is helpful to promote the average payoffs of both cooperators and defectors. Moreover, this parameter also significantly influences the cooperation frequency on the resulting networks. The coexisting mechanisms in this paper may provide a beneficial insight for understanding the emergence of complex topological structures and game behaviors in numerous real systems.     

Three-option evolutionary minority game on small-world network

In this paper, we study the three-option evolutionary minority game with imitation on small-world networks. Numerical results show that the performance of the system depends on the ways of modifying the gene values as well as the points awarded to the agents belonging to the intermediate populated group. Better cooperation can be obtained through local communication within the agents.     

Partner selection method in cooperative spatial modulation (CSM) system

In this work, to achieve higher capacity, spatial modulation (SM) is utilized in a cooperative scheme in two different models of Cooperative SM, where SM is applied either at the source or the selected partner through an antenna array. A theoretical model of SM capacity is defined based on closed form lower and upper bound of the SM capacity. Based on the theoretical analysis of the channel capacity in both CSM systems the simplest and easier to implement selection criteria have been suggested to select a single partner to maximize CSM capacity. The CSM system in which SM is applied through the partner’s antenna array is providing significant enhancement over the other CSM model and cooperative multiple-input-multiple-output (CO-MIMO) networks based on multiple partners. Moreover, the system with a single randomly selected partner equipped with four antennas, behaves similarly as the best configuration of CO-MIMO based on the cooperation of four partners with the computational complexity linearly increases with the number of available partners to help. All of the theoretical findings are verified through simulation studies.     

Quantum game theory and open access publishing

The digital revolution of the information age and in particular the sweeping changes of scientific communication brought about by computing and novel communication technology, potentiate global, high grade scientific information for free. The arXiv, for example, is the leading scientific communication platform, mainly for mathematics and physics, where everyone in the world has free access on. While in some scientific disciplines the open access way is successfully realized, other disciplines (e.g. humanities and social sciences) dwell on the traditional path, even though many scientists belonging to these communities approve the open access principle. In this paper we try to explain these different publication patterns by using a game theoretical approach. Based on the assumption, that the main goal of scientists is the maximization of their reputation, we model different possible game settings, namely a zero sum game, the prisoners’ dilemma case and a version of the stag hunt game, that show the dilemma of scientists belonging to “non-open access communities”. From an individual perspective, they have no incentive to deviate from the Nash equilibrium of traditional publishing. By extending the model using the quantum game theory approach it can be shown, that if the strength of entanglement exceeds a certain value, the scientists will overcome the dilemma and terminate to publish only traditionally in all three settings.     

一种基于势博弈的无线传感器网络拓扑控制算法

在实际的应用中,无线传感器网络常常由大量电池资源有限的传感器节点组成.如何降低网络功耗,最大化网络生存时间,是传感器网络拓扑控制技术的重要研究目标.随着传感节点的运行,节点的能量分布可能越来越不均衡,需要在考虑该因素的情况下,动态地调整节点的网络负载以均衡节点的能耗,达到延长网络生存时间的目的.该文引入博弈理论和势博弈的概念,综合考虑节点的剩余能量和节点发射功率等因素,设计了一种基于势博弈的拓扑控制模型,并证明了该模型纳什均衡的存在性.通过构造兼顾节点连通性和能耗均衡性的收益函数,以确保降低节点功耗的同时维持网络的连通性.通过提高邻居节点的平均剩余能量值以实现将剩余能量多的节点选择作为自身的邻居节点,提高节点能耗的均衡性.在此基础上,提出了一种分布式的能耗均衡拓扑控制算法.理论分析证明了该算法能保持网络的连通性.与现有基于博弈理论的DIA算法和MLPT算法相比,本算法形成的拓扑负载较重、剩余能量较小的瓶颈节点数量较少,节点剩余能量的方差较小,网络生存时间更长.     

Evolutionary snowdrift game on heterogeneous Newman--Watts small-world network

We study the evolutionary snowdrift game in a heterogeneous Newman-Watts small-world network. The heterogeneity of the network is controlled by the number of hubs. It is found that the moderate heterogeneity of the network can promote the cooperation best. Besides, we study how the hubs affect the evolution of cooperative behaviours of the heterogeneous Newman-Watts small-world network. Simulation results show that both the initial states of hubs and the connections between hubs can play an important role. Our work gives a further insight into the effect of hubs on the heterogeneous networks.