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.     

PageRank model of opinion formation on social networks

We propose the PageRank model of opinion formation and investigate its rich properties on real directed networks of the Universities of Cambridge and Oxford, LiveJournal, and Twitter. In this model, the opinion formation of linked electors is weighted with their PageRank probability. Such a probability is used by the Google search engine for ranking of web pages. We find that the society elite, corresponding to the top PageRank nodes, can impose its opinion on a significant fraction of the society. However, for a homogeneous distribution of two opinions, there exists a bistability range of opinions which depends on a conformist parameter characterizing the opinion formation. We find that the LiveJournal and Twitter networks have a stronger tendency to a totalitarian opinion formation than the university networks. We also analyze the Sznajd model generalized for scale-free networks with the weighted PageRank vote of electors.     

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.     

Opinion dynamics in networked command and control organizations

An opinion dynamics model for a Command and Control (C2) organization is essential for simulating combat system effectiveness. However, few studies have addressed opinion evolution in C2 simulation. With the goal of overcoming this research gap, this paper proposes an opinion exchange model, which is illustrated through a practical example of an Armored Division network. The model is divided into homogeneous and heterogeneous aspects: the former is mainly characterized by communication rules and types, while the latter is extended with the influence of multi-level opinion leaders. After carrying out the simulation of the two main models, the results show that the opinion evolution of the hierarchical leveled C2 organization with descending influence is much more complex and unpredictable than that of social networks.     

Public debates driven by incomplete scientific data: The cases of evolution theory, global warming and H1N1 pandemic influenza

Public debates driven by incomplete scientific data where nobody can claim absolute certainty, due to the current state of scientific knowledge, are studied. The cases of evolution theory, global warming and H1N1 pandemic influenza are investigated. The first two are of controversial impact while the third is more neutral and resolved. To adopt a cautious balanced attitude based on clear but inconclusive data appears to be a lose-out strategy. In contrast overstating arguments with incorrect claims which cannot be scientifically refuted appears to be necessary but not sufficient to eventually win a public debate. The underlying key mechanisms of these puzzling and unfortunate conclusions are identified using the Galam sequential probabilistic model of opinion dynamics (Galam, 2002 [4], Galam, 2005 [18], Galam and Jacobs, 2007 [19]). It reveals that the existence of inflexible agents and their respective proportions are the instrumental parameters to determine the faith of incomplete scientific data in public debates. Acting on one’s own inflexible proportion modifies the topology of the flow diagram, which in turn can make irrelevant initial supports. On the contrary focusing on open-minded agents may be useless given some topologies. When the evidence is not as strong as claimed, the inflexibles rather than the data are found to drive the opinion of the population. The results shed a new but disturbing light on designing adequate strategies to win a public debate.     

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 ?.     

How much information is needed to be the majority during the binary-state opinion formation?

In this paper, we try to propose a toy model, which follows the majority rule with the Fermi function, to uncover the role of the heterogeneous interaction between individuals in opinion formation. In order to do this, we define the impact factor I_Fi$I{F}_i$, says individual i$i$, as the exponential function of its connectivity _ki$k_i$ with the tunable parameter β$\beta$. β$\beta$ also shows the public information that can be collected by individuals in the system. We realize our model in scale-free networks with mean connectivity 〈k〉$〈k〉$. We find that much more public information (β>_β2$\beta >{\beta }_2$) and less public information (β<_β1$\beta <{\beta }_1$) cannot let either of the two opinions be the majority during the opinion formation. Furthermore, _β1${\beta }_1$ is a constant and equal to −0.76(±0.04)$-0.76(\pm 0.04)$, and _β2${\beta }_2$ decreases as a power-law function of the mean connectivity 〈k〉$〈k〉$ of the network. Our work can provide some perspectives and tools to understand the diversity of opinion in social networks.     

PageRank model of opinion formation on Ulam networks

We consider a PageRank model of opinion formation on Ulam networks, generated by the intermittency map and the typical Chirikov map. The Ulam networks generated by these maps have certain similarities with such scale-free networks as the World Wide Web (WWW), showing an algebraic decay of the PageRank probability. We find that the opinion formation process on Ulam networks has certain similarities but also distinct features comparing to the WWW. We attribute these distinctions to internal differences in network structure of the Ulam and WWW networks. We also analyze the process of opinion formation in the frame of generalized Sznajd model which protects opinion of small communities.     

Trust in the CODA model: Opinion dynamics and the reliability of other agents

A model for the joint evolution of opinions and how much the agents trust each other is presented, using the framework of the Continuous Opinions and Discrete Actions (CODA) model. Instead of a fixed probability that the other agents will decide in the favor of the best choice, each agent considers that other agents might be one of two types: trustworthy or untrustworthy. Each agent its opinion and also the probability for each one of the other agents it interacts with being trustworthy. The dynamics of opinions and the evolution of the trust between the agents are studied. Clear evidences of the existence of two phases, one with strong polarization and the other tending to agreement, are observed. The transition shows signs of being a first-order transition. This happens despite the fact that the trust network evolves much slower than the opinion on the central issue.