Social learning with time-varying weights

This paper investigates a non-Bayesian social learning model, in which each individual updates her beliefs based on private signals as well as her neighbors＇ beliefs. The private signM is involved in the updating process through Bayes＇ rule, and the neighbors＇ beliefs are embodied in through a weighted average form, where the weights are time-varying. The authors prove that agents eventually have correct forecasts for upcoming signals, and all the beliefs of agents reach a consensus. In addition, if there exists no state that is observationally equivalent to the true state from the point of view of all agents, the authors show that the consensus belief of the whole group eventually reflects the true state.     

Soft-control for collective opinion of weighted DeGroot model

The DeGroot model is a classic model to study consensus of opinion in a group of individuals(agents). Consensus can be achieved under some circumstances. But when the group reach consensus with a convergent opinion value which is not what we expect, how can we intervene the system and change the convergent value? In this paper a mechanism named soft control is first introduced in opinion dynamics to guide the group's opinion when the population are given and evolution rules are not allowed to change. According to the idea of soft control, one or several special agents,called shills, are added and connected to one or several normal agents in the original group. Shills act and are treated as normal agents. The authors prove that the change of convergent opinion value is decided by the initial opinion and influential value of the shill, as well as how the shill connects to normal agents. An interesting and counterintuitive phenomenon is discovered: Adding a shill with an initial opinion value which is smaller(or larger) than the original convergent opinion value dose not necessarily decrease(or increase) the convergent opinion value under some conditions. These conditions are given through mathematical analysis and they are verified by the numerical tests. The authors also find out that the convergence speed of the system varies when a shill is connected to different normal agents. Our simulations show that it is positively related to the degree of the connected normal agent in scale-free networks.     

群体性突发事件中非一致信任水平舆论传播建模与分析

群体性突发事件是影响社会稳定的重要因素,正确认识和理解舆论传播的演化机制, 可以建立快速、高效的应急体系和措施, 从而保证群体性突发事件的客观舆论导向. 针对群体中个体属性的多样性, 在Krause有界信任模型基础上提出了非一致有界信任模型, 进行了一系列计算机仿真实验. 结果表明: 当社会网络中个体的信任水平各不相同时, 要使社会网络的群体观点达到一致, 远比相同信任水平时要难; 而且, 社会网络中个体的数量与舆论传播的最终观点簇之间存在正相关关系.     

突发事件下群体抢购行为的舆论传播机理研究

本文从舆论传播的角度,采用有界信任规则,通过引入突发事件的不确定性环境噪音因子,构建用于分析群体抢购行为的噪音异质有界信任模型.然后,通过计算机仿真,分析了信任水平不同的子群体比例变化和突发事件所引发的不确定性大小对群体观点演化的影响.研究结果将为突发事件下群体抢购行为舆论引导机制的建立和有效实施提供理论依据.     

Opinion Dynamics Induced by Agents with Particular Goal

The authors investigate the opinion dynamics in a setting where some special agents induce public opinions towards their desired direction, with Particular Goal(PG agents for short) to manipulate beliefs. Based on the bounded confidence model, the authors find PG agents can significantly improve the level of consensus. The authors also study how opinion pattern is influenced by varying the model in terms of changing the network structure, different parameters, and PG agents choosing strategy. Th...     

舆论演化的社会影响级联效应模型

舆论建模跳出传统的基于最近邻("person-person")的交互范式,引入次近邻("personperson-person")的影响,刻画网络中邻居的邻居对观点改变的作用,提出舆论演化的社会影响级联模型,分析其在可变聚类系数网络上舆论的演化性质.通过调节网络聚类系数,使用异步更新的方式,观察网络集聚特性对舆论演化的影响.结果表明,1)相比于传统的最近邻影响模型,社会影响级联模型的社会强化作用更大,系统更容易达成共识,初始状态中主流观点的影响将被放大;2)舆论演化结果与网络集聚性和初始状态相关:当系统初始状态p_+≠p_,系统观点演化达到稳态后,网络聚类系数越大,越容易产生主流观点;当初始观点p_+=p_时,即正、负力量势均力敌时,系统共识则难以确定.这种情况和现实社会舆论的演化结果符合.     

基于云模型与证据理论的共识分析方法

针对综合集成研讨厅中发散型研讨具有连续性、模糊性、复杂性等特点, 提出了一种基于云模型与证据理论的共识分析方法. 该方法利用云模型根据研讨信息间语义关系求取每条发言表达的意见; 并利用证据理论根据某专家所有时刻发言表达的意见推理该专家意见; 最后根据求得的各专家意见形成定性的专家意见分布图和定量的共识水平、关注水平. 实例表明该方法是有效的, 它能使研讨专家实时了解当前共识状态, 提高群体研讨效率.     

Second-order consensus in networks of dynamic agents with communication time-delays

This paper proposes second-order consensus protocols with time-delays and gives the measure of the robustness of the protocols to the time-delay existing in the network of agents with second-order dynamics. By employing a frequency domain method, it is proven that the information states and their time derivatives of all the agents in the network achieve consensus asymptotically, respectively, for appropriate communication timedelay if the topology of weighted network is connected. Particularly, a tight uppe...     

Output consensus for heterogeneous nonlinear multi-agent systems based on T-S fuzzy model

In this paper, the output consensus problem of general heterogeneous nonlinear multi-agent systems subject to different disturbances is considered. A kind of Takagi-Sukeno fuzzy modeling method is used to describe the nonlinear agents’ dynamics. Based on the model, a distributed fuzzy observer and controller are designed based on parallel distributed compensation scheme and internal reference models such that the heterogeneous nonlinear multi-agent systems can achieve output consensus. Then a necessary and sufficient condition is presented for the output consensus problem. And it is shown that the consensus trajectory of the global fuzzy model is determined by the network topology and the initial states of the internal reference models. Finally, some simulations are given to illustrate and verify the effectiveness of the proposed scheme.     

基于不对称人际影响的舆论涌现模型研究

人际关系影响是舆论形成的重要因素。引入个体意见的不确定性以及个体间的信任因子,建立了个体间的不对称影响函数,以此为基础实现了一个舆论涌现的Agent仿真模型,仿真结果反映模型是合理有效的。对仿真结果的分析说明:个体意见的不确定性和个体间的相互信任,是影响最终舆论涌现形态与涌现速度的重要因素。     

观点动力学研究现状及进展述评

观点作为一种舆情,意见,态度的表现形式广泛存在于人们的生活中,研究观点的演变对理清观点演变机制,促进舆情合理治理具有重要意义。鉴于现有研究缺乏对二元观点动力学的相关介绍以及割裂了二元与群体观点动力学之间的联系,未能全面揭示观点动力学研究现状,因此,对国内外观点动力学研究现状进行了梳理和总结。其中,从研究方法和交互特征视角对二元观点动力学模型及其研究现状进行了介绍,从个体特征,行为特征,观点特征,外部环境,观点动力学的应用视角对群体观点动力学研究成果进行了梳理。最后,基于现有研究,明确了观点动力学未来需要进一步解决的问题:实证视角下的观点演化机制,意见的强化和争议的消减,观点的演化与群体决策之间的联系。     

SOFT CONTROL ON COLLECTIVE BEHAVIOR OF A GROUP OF AUTONOMOUS AGENTS BY A SHILL AGENT

This paper asks a new question： how can we control the collective behavior of self-organized multi-agent systems？ We try to answer the question by proposing a new notion called ＇Soft Control＇ which keeps the local rule of the existing agents in the system. We show the feasibility of soft control by a case study. Consider the simple but typical distributed multi-agent model proposed by Vicsek et al. for flocking of birds： each agent moves with the same speed but with different headings which are updated using a local rule based on the average of its own heading and the headings of its neighbors. Most studies of this model are about the self-organized collective behavior, such as synchronization of headings. We want to intervene in the collective behavior （headings） of the group by soft control. A specified method is to add a special agent, called a ＇Shill＇, which can be controlled by us but is treated as an ordinary agent by other agents. We construct a control law for the shill so that it can synchronize the whole group to an objective heading. This control law is proved to be effective analytically and numerieally. Note that soft control is different from the approach of distributed control. It is a natural way to intervene in the distributed systems. It may bring out many interesting issues and challenges on the control of complex systems.     

基于多智能代理系统仿真的网络社区意见交流机制

通过构建基于多智能代理的意见交互机制模型,对新古典经济范式下的理性代理模型进行拓展。通过构建时空交流域主方程和利用MAS仿真及平均域的方法描述网络社区的意见交流过程。研究表明,在影响网络社区观点稳态结构的因素中,社区分歧参数、社区温度、意见领袖和外部域的附加影响均存在临界值;较快的信息交流速度和适当的社区温度有助于系统达到稳定状态;意见领袖或外部影响作用可以对智能代理行为产生重要影响,在网络社区中形成观点多数派。     

Mean Square Average Generalized Consensus of Multi-Agent Systems Under Time-Delays and Stochastic Disturbances

Compared with the traditional consensus problem, this paper deals with the mean square average generalized consensus(MSAGC) of multi-agent systems under fixed directed topology, where all agents are affected by stochastic disturbances. Distributed protocol depending on delayed time information from neighbors is designed. Based on Lyapunov stability theory, together with results from matrix theory and It o s derivation theory, the linear matrix inequalities approach is used to establish sufficient conditions to ensure MSAGC of multi-agent systems. Finally, numerical simulations are provided to illustrate the theoretical results.     

Data-Driven Leader-Follower Output Synchronization for Networked Non-Linear Multi-Agent Systems with Switching Topology and Time-Varying Delays

This paper studies the output synchronization problem for a class of networked non-linear multi-agent systems with switching topology and time-varying delays. To synchronize the outputs, a leader is introduced whose connectivity to the followers varies with time, and a novel data-driven consensus protocol based on model free adaptive control is proposed, where the reference input of each follower is designed to be the time-varying average of the neighboring agents’ outputs. Both the case when the leader is with a prescribed reference input and the case otherwise are considered. The proposed protocol allows for time-varying delays, switching topology, and does not use the agent structure or the dynamics information implicitly or explicitly. Sufficient conditions are derived to guarantee the closed-loop stability, and conditions for consensus convergence are obtained, where only a joint spanning tree is required. Numerical simulations and practical experiments are conducted to demonstrate the effectiveness of the proposed protocol.     

A Coupled Dynamic Model of Brand Acceptance and Promotive Information Spreading

When people try to decide to buy or not to, they are often influenced by both their inherent opinions and the social marketing activities e.g. advertising, social news with strong point of view. Then people will make their final choice, or even convince other people to buy. After all, this is the brand acceptance formation process. Factually, the dynamics of brand acceptance is essentially an interwoven dynamics of endogenous opinion dynamics disturbed by an information diffusion process. To have a better understanding of the dynamics of brand acceptance, we propose and analyze a coupled agent-based dynamic model that combines the Majority-Rule-based Voter model in opinion dynamics with the SI Model for information spreading to analyze the dynamics of brand acceptance in social media. We focus on two important parameters in diffusion dynamics: the decayed transmission rate (β) and the diffusion frequency (f). When the system is stable, the order parameter of the system is the duration time (τ). In the absence of opinion interaction, the simulation results indicate that, when a brand tries to occupy a larger market share through social marketing approaches, it is always effective to let the opponent to be the propaganda target. While with the Majority-Rule-based Voter Model included, we observe that the opinion interaction could have a dual function, which shows that a brand holding a small market share in the first place needs to adopt diverse marketing approaches according to different marketing environment types.     

Bipartite Consensus of Discrete-Time Double-Integrator Multi-Agent Systems with Measurement Noise

The effects of measurement noise are investigated in the context of bipartite consensus of multi-agent systems. In the system setting, discrete-time double-integrator dynamics are assumed for the agent, and measurement noise is present for the agent receiving the state information from its neighbors. Time-varying stochastic bipartite consensus protocols are designed in order to lessen the harmful effects of the noise. Consequently, the state transition matrix of the closed-loop system is analyzed, and sufficient and necessary conditions for the proposed protocol to be a mean square bipartite consensus protocol are given with the help of linear transformation and algebraic graph theory. It is proven that the signed digraph to be structurally balanced and having a spanning tree are the weakest communication assumptions for ensuring bipartite consensus. In particular, the proposed protocol is a mean square bipartite average consensus one if the signed digraph is also weight balanced.     

基于模糊语言群体决策的一致性协调技术

本文提供语言群体决策的匹配集结方法 ,发展了一致性达成过程的协调技术 ,它包括语言一致度、语言距离及学习模型 .最后讨论一个实例及计算步骤 .     

基于有界信任模型的地铁突发事件信息传播

地铁突发事件一直是社会关注的热点问题,事件中的信息传播和控制往往也会决定突发事件的严重程度.本文以地铁突发事件为背景,考虑了地铁突发事件的时间有限性和空间有限性特征,在传统有界信任模型的基础上,引入了局部沟通和有限理性两个因素,并根据突发事件发生的时间考虑了地铁停靠前和停靠后两个不同的阶段,建立了地铁突发事件信息传播模型.通过大量仿真实验,发现在地铁突发事件中,局部沟通和有限理性都会导致乘客观点剧烈波动,使得整个系统长时间处于极不稳定的状态;而乘客密度对乘客的观点收敛起正向作用,密度越大,观点越容易收敛.而在列车停靠后,乘客越向车门方向集中,观点波动幅度越大,并在最后急剧收敛成一致,但疏散速率越高,反而会减缓乘客观点的波动幅度和收敛速度.本文的研究结果为地铁突发事件中的现象分析提供了理论依据,对地铁突发事件信息的控制具有重要意义.     

基于一种概率阈值模型的群体观点动向研究

考虑群体观点动向中个体差异特征,社会影响压力及外生因素影响,本文提出了一种描述群体观点动态的概率阈值模型.并运用此模型仿真证实了群体观点极化是社会影响和结构平衡共同作用的结果,从认知理论,社会认同,结构平衡等社会学理论角度,用自下而上的自主体仿真分析方法揭示了群体宏观模式形成与微观结构平衡之间的密切联系.