Dynamics of bounded confidence opinion in heterogeneous social networks: Concord against partial antagonism

Bounded confidence models of opinion dynamics in social networks have been actively studied in recent years, in particular, opinion formation and extremism propagation along with other aspects of social dynamics. In this work, after an analysis of limitations of the Deffuant-Weisbuch (DW) bounded confidence, relative agreement model, we propose the mixed model that takes into account two psychological types of individuals. Concord agents (C-agents) are friendly people; they interact in a way that their opinions always get closer. Agents of the other psychological type show partial antagonism in their interaction (PA-agents). Opinion dynamics in heterogeneous social groups, consisting of agents of the two types, was studied on different social networks: Erdös-Rényi random graphs, small-world networks and complete graphs. Limit cases of the mixed model, pure C- and PA-societies, were also studied. We found that group opinion formation is, qualitatively, almost independent of the topology of networks used in this work. Opinion fragmentation, polarization and consensus are observed in the mixed model at different proportions of PA- and C-agents, depending on the value of initial opinion tolerance of agents. As for the opinion formation and arising of “dissidents”, the opinion dynamics of the C-agents society was found to be similar to that of the DW model, except for the rate of opinion convergence. Nevertheless, mixed societies showed dynamics and bifurcation patterns notably different to those of the DW model. The influence of biased initial conditions over opinion formation in heterogeneous social groups was also studied versus the initial value of opinion uncertainty, varying the proportion of the PA- to C-agents. Bifurcation diagrams showed an impressive evolution of collective opinion, in particular, radical changes of left to right consensus or vice versa at an opinion uncertainty value equal to 0.7 in the model with the PA/C mixture of population near 50/50.     

Neural networks and bounded rationality

Traditionally the emphasis in neural network research has been on improving their performance as a means of pattern recognition. Here we take an alternative approach and explore the remarkable similarity between the under-performance of neural networks trained to behave optimally in economic situations and observed human performance in the laboratory under similar circumstances. In particular, we show that neural networks are consistent with observed laboratory play in two very important senses. Firstly, they select a rule for behavior which appears very similar to that used by laboratory subjects. Secondly, using this rule they perform optimally only approximately 60% of the time.     

A model of the effects of authority on consensus formation in adaptive networks: Impact on network topology and robustness

Opinions of individuals in real social networks are arguably strongly influenced by external determinants, such as the opinions of those perceived to have the highest levels of authority. In order to model this, we have extended an existing model of consensus formation in an adaptive network by the introduction of a parameter representing each agent’s level of ‘authority’, based on their opinion relative to the overall opinion distribution. We found that introducing this model, along with a randomly varying opinion convergence factor, significantly impacts the final state of converged opinions and the number of interactions required to reach that state. We also determined the relationship between initial and final network topologies for this model, and whether the final topology is robust to node removals. Our results indicate firstly that the process of consensus formation with a model of authority consistently transforms the network from an arbitrary initial topology to one with distinct measurements in mean shortest path, clustering coefficient, and degree distribution. Secondly, we found that subsequent to the consensus formation process, the mean shortest path and clustering coefficient are less affected by both random and targeted node disconnection. Speculation on the relevance of these results to real world applications is provided.     

An opinion diffusion model with clustered early adopters

We propose an opinion dynamic model which studies the adoption process of new opinions or ideas by agents. The proposed model allows the observation range of an agent to be expanded in square lattices. The agent’s opinion update process is not only influenced by a neighbor’s choice but also by the whole environment that can be observed. The model shows a different result with the normal CODA model: if adopters’ initial opinions equal 0.6 and individual observation probability α$\alpha$ equals 0.65, then diffusions with clustered early adopters are 3% faster than those with randomly scattered ones. Introducing the bounded confidence concept into our model leads to appearance of freezing effect in opinion dynamics.     

Opinion dynamics in networks with heterogeneous confidence and influence

We propose a discrete-time model of opinion dynamics. The neighborhood relationship is decided by confidence radius and influence radius of each agent. We investigate the influence of heterogeneity in confidence/influence distribution on the behavior of the network. The simulations suggest that the heterogeneity of single confidence or influence networks can promote the opinions to achieve consensus. It is shown that the heterogeneous influence radius systems converge in fewer time steps and more often in finite time than the heterogeneous confidence radius systems. We find that heterogeneity does not always promote consensus, and there is an optimal heterogeneity so that the relative size of the largest consensus cluster reaches maximum in heterogeneous confidence and influence networks.     

Continuous opinion model in small-world directed networks

In the compromise model of continuous opinions proposed by Deffuant et al., the states of two agents in a network can start to converge if they are neighbors and if their opinions are sufficiently close to each other, below a given threshold of tolerance ?. In directed networks, if agent i is a neighbor of agent j,j need not be a neighbor of i. In Watts-Strogatz networks we performed simulations to find the averaged number of final opinions 〈F〉 and their distribution as a function of ? and of the network structural disorder. In directed networks 〈F〉 exhibits a rich structure, being larger than in undirected networks for higher values of ?, and smaller for lower values of ?.     

Epidemic spreading in communities with mobile agents

We propose a model of mobile agents to study the epidemic spreading in communities with different densities of agents, which aims to simulate the realistic situation of multiple cities. The model addresses the epidemic process from a community with threshold λ_c1 less than the infection rate λ to a community with threshold λ_c2 larger than λ through both direct and indirect contacts. By both theoretic analysis and numerical simulations we show that it is possible to sustain the epidemic spreading in the community with λ_c2 through contact with another community, provided that the latter is connected with an infected community. This result suggests that for effectively controlling the epidemic spread, we should also pay attention to the risk caused by the infection through indirect contact.     

Social power and opinion formation in complex networks

In this paper we investigate the effects of social power on the evolution of opinions in model networks as well as in a number of real social networks. A continuous opinion formation model is considered and the analysis is performed through numerical simulation. Social power is given to a proportion of agents selected either randomly or based on their degrees. As artificial network structures, we consider scale-free networks constructed through preferential attachment and Watts–Strogatz networks. Numerical simulations show that scale-free networks with degree-based social power on the hub nodes have an optimal case where the largest number of the nodes reaches a consensus. However, given power to a random selection of nodes could not improve consensus properties. Introducing social power in Watts–Strogatz networks could not significantly change the consensus profile.     

Effects of attachment preferences on coevolution of opinions and networks

In the coevolution of network structures and opinion formation, we investigate the effects of a mixed population with distinctive relinking preferences on both the convergence time and the network structures. It has been found that a heterogeneous network structure is easier to be reached with more high-degree-preferential (HDP) nodes. There exists high correlation between the convergence time and the network heterogeneity. The heterogeneous degree distribution caused by preferential attachment accelerates the convergence to a consensus state and the shortened convergence time inhibits the occurrence of the following disquieting situation that occurs in a continuously evolving network: with preferential attachment and long-time evolvement, most of the nodes would become separated and only a few leaders would have immediate neighbors. Analytical calculations based on mean field theory reveal that both the transition point p_tr and the consensus time τ depend upon the standard deviation of the degree distribution σ_d. p_tr increases while τ decreases with the rise of σ_d. Functions of p_tr=〈k〉/(〈k〉+1) and are found. Theoretical analyses are in accordance with simulation data.     

Chaos in social learning with multiple true states

Most existing social learning models assume that there is only one underlying true state. In this work, we consider a social learning model with multiple true states, in which agents in different groups receive different signal sequences generated by their corresponding underlying true states. Each agent updates his belief by combining his rational self-adjustment based on the external signals he received and the influence of his neighbors according to their communication. We observe chaotic oscillation in the belief evolution, which implies that neither true state could be learnt correctly by calculating the largest Lyapunov exponents and Hurst exponents.     

The effects of nonlinear interactions and network structure in small group opinion dynamics

We present a model of opinion dynamics in social networks in which an individual's opinion evolves under the action of (i) a linear force which tends to restore the opinion back towards the individual's natural bias that is his or her initial opinion and (ii) a nonlinear coupling with other individuals which acts to bring opinions closer together but wanes for high opinion discrepancies. Bifurcation analysis for the case of a two-person group shows that a critical value for the difference in natural biases exists which demarcates regimes of qualitatively different behavior. For low to moderate natural bias differences, the dynamics are qualitatively similar to linear theory. For high bias differences, the system takes on a binary nature and is marked by discontinuous transitions between deadlock and consensus as well as hysteresis as the coupling is varied. The coupling required to force consensus grows extremely rapidly with the natural bias difference indicating that trying to achieve group consensus solely via increasing the communications rate becomes fruitless as the biases become extremely divergent. We also show that, for high bias differences, a triad broker network topology can reduce group discord more effectively than a clique, contrary to linear theory.     

Asset price dynamics in a financial market with heterogeneous trading strategies and time delays

In this paper we present a continuous time dynamical model of heterogeneous agents interacting in a financial market where transactions are cleared by a market maker. The market is composed of fundamentalist, trend following and contrarian agents who process market information with different time delays. Each class of investors is characterized by path dependent risk aversion. We also allow for the possibility of evolutionary switching between trend following and contrarian strategies. We find that the system shows periodic, quasi-periodic and chaotic dynamics as well as synchronization between technical traders. Furthermore, the model is able to generate time series of returns that exhibit statistical properties similar to those of the S&P 500 index, which is characterized by excess kurtosis, volatility clustering and long memory.     

Formation of share market prices under heterogeneous beliefs and common knowledge

Financial economic models often assume that investors know (or agree on) the fundamental value of the shares of the firm, easing the passage from the individual to the collective dimension of the financial system generated by the Share Exchange over time. Our model relaxes that heroic assumption of one unique “true value” and deals with the formation of share market prices through the dynamic formation of individual and social opinions (or beliefs) based upon a fundamental signal of economic performance and position of the firm, the forecast revision by heterogeneous individual investors, and their social mood or sentiment about the ongoing state of the market pricing process. Market clearing price formation is then featured by individual and group dynamics that make its collective dimension irreducible to its individual level. This dynamic holistic approach can be applied to better understand the market exuberance generated by the Share Exchange over time.     

Short-time behaviour of demand and price viewed through an exactly solvable model for heterogeneous interacting market agents

We introduce a stochastic heterogeneous interacting-agent model for the short-time non-equilibrium evolution of excess demand and price in a stylized asset market. We consider a combination of social interaction within peer groups and individually heterogeneous fundamentalist trading decisions which take into account the market price and the perceived fundamental value of the asset. The resulting excess demand is coupled to the market price. Rigorous analysis reveals that this feedback may lead to price oscillations, a single bounce, or monotonic price behaviour. The model is a rare example of an analytically tractable interacting-agent model which allows us to deduce in detail the origin of these different collective patterns. For a natural choice of initial distribution, the results are independent of the graph structure that models the peer network of agents whose decisions influence each other.     

不对称结构的分布式负载有界波电磁脉冲模拟器

 设计、建造了一台不对称结构、分布式负载有界波电磁脉冲（EMP）模拟器（MDES-60）。模拟器平行极板间区域长5 m，宽2 m，高1 m。测试结果显示模拟器工作空间电场幅值分布均匀、波形后沿基本无反射。表明通过缩小前过渡段的锥角、加宽下极板调整特性阻抗及采用分布式负载等措施取得了很好效果。配套不同的脉冲源，该模拟器可模拟IEC61000-2-9、Bell实验室、1976年出版物等多种脉宽标准的EMP波形，场强幅度范围15~60 kV/m，可用于短线缆响应实验或小型电子设备的考核效应实验。     

Social networks: Ideas for future research from the vantage point of political science

In this short piece, Bunce and Csanadi draw upon their expertise in political science and political economy to offer some observations about the analysis of social networks. Using both examples and questions they highlight the importance of structural variations in networks, including differences in the motivations behind network formation; the subsequent development of networks, including extension, contraction and duration; and the effects of individual decision-makers on network dynamics and, at the same time, the effects of network structure and dynamics on individual decision-makers.     

Analyses of a heterogeneous lattice hydrodynamic model with low and high-sensitivity vehicles

Basic lattice model is extended to study the heterogeneous traffic by considering the optimal current difference effect on a unidirectional single lane highway. Heterogeneous traffic consisting of low- and high-sensitivity vehicles is modeled and their impact on stability of mixed traffic flow has been examined through linear stability analysis. The stability of flow is investigated in five distinct regions of the neutral stability diagram corresponding to the amount of higher sensitivity vehicles present on road. In order to investigate the propagating behavior of density waves non linear analysis is performed and near the critical point, the kink antikink soliton is obtained by driving mKdV equation. The effect of fraction parameter corresponding to high sensitivity vehicles is investigated and the results indicates that the stability rise up due to the fraction parameter. The theoretical findings are verified via direct numerical simulation.     

Joint signal alignment precoding and physical network coding for heterogeneous networks

Mobile traffic in cellular based networks is increasing exponentially, mainly due to the use of data intensive services like video. One effective way to cope with these demands is to reduce the cell-size by deploying small-cells along the coverage area of the current macro-cell system. The deployment of small-cells significantly improves the indoor coverage. Nevertheless, as additional spectrum licenses are difficult and expensive to acquire, it is expected that the macro and small-cells will coexist under the same spectrum. The coexistence of the two systems results in cross-tier/inter-system interference. In this context, we consider the application of joint signal alignment (SA) and physical network coding (PNC) for the uplink of heterogeneous networks, in order to cancel the interference generated from small-cells at the macro-cell user terminal. The joint design of SA and PNC allows to serve more users than the case where only PNC or interference alignment (IA) is employed individually. We compare our proposed joint SA-PNC schemes with the recently designed IA based techniques for the uplink heterogeneous systems. Simulation results show that the proposed SA-PNC is quite efficient to remove the inter-tier/system interference while allowing to increase the overall data rate, by serving more users, as compared with the IA based methods     

Coevolutionary,coexisting learning and teaching agents model for prisoner’s dilemma games enhancing cooperation with assortative heterogeneous networks

Unlike other natural network systems, assortativity can be observed in most human social networks, although it has been reported that a social dilemma situation represented by the prisoner’s dilemma favors dissortativity to enhance cooperation. We established a new coevolutionary model for both agents’ strategy and network topology, where teaching and learning agents coexist. Remarkably, this model enables agents’ enhancing cooperation more than a learners-only model on a time-frozen scale-free network and produces an underlying assortative network with a fair degree of power-law distribution. The model may imply how and why assortative networks are adaptive in human society.     

RAT selection for IoT devices in HetNets: Reinforcement learning with hybrid SMDP algorithm

Due to the increasing deployment of heterogeneous networks (HetNets), the selection of which radio access technologies (RATs) for Internet of Things (IoT) devices such as user equipments (UEs) has recently received extensive attention in mobility management research. Most of existing RAT selection methods only optimize the selection strategies from the UE side or network side, which results in heavy network congestion, poor user experience and system utility degradation. In this paper the UE side and the network side are considered comprehensively, based on the game theory (GT) model we propose a reinforcement learning with assisted network information algorithm to overcome the crucial points. The assisted information is formulated as a semi-Markov decision process (SMDP) provided for UEs to make accurate decisions, and we adopt the iteration approach to reach the optimal policy. Moreover, we investigate the impacts of different parameters on the system utility and handover performance. Numerical results validate that our proposed algorithm can mitigate unnecessary handovers and improve system throughputs.