Information Evolution in Complex Networks

Many biological phenomena or social events critically depend on how information evolves in complex networks. A seeming paradox of the information evolution is the coexistence of local randomness, manifested as the stochastic distortion of information content during individual-individual diffusion, and global regularity, illustrated by specific non-random patterns of information content on the network scale. The current research pursues to understand the underlying mechanisms of such coexistence. Applying network dynamics and information theory, we discover that a certain amount of information, determined by the selectivity of networks to the input information, frequently survives from random distortion. Other information will inevitably experience distortion or dissipation, whose speeds are shaped by the diversity of information selectivity in networks. The discovered laws exist irrespective of noise, but the noise accounts for their intensification. We further demonstrate the ubiquity of our discovered laws by applying them to analyze the emergence of neural tuning properties in the primary visual and medial temporal cortices of animal brains and the emergence of extreme opinions in social networks

Radicalization phenomena: Phase transitions, extinction processes and control of violent activities

In this work we study a simple mathematical model to analyze the emergence and control of radicalization phenomena. The population consisits of core and sensitive subpopulations, and their ways of life may be at least partially incompatible. In such a case, if a conflict exist, core agents act as inflexible individuals about the issue. On the other hand, the sensitive agents choose between two options: live peacefully with core population, or oppose it. This kind of modeling was recently considered by Galam and Javarone (2016) with constant pairwise couplings. Here, we consider the more general case with time-dependent transition rates, with the aim of study the impact of such time dependence on the critical behavior of the model. The analytical and numerical results show that the nonequilibrium active-absorbing phase transition can be suppressed in some cases, with the destruction of the absorbing phase where the radical agents disappear of the population in the stationary states.Comment: 12 pages, 5 figures, to appear in IJMP

Steering opinion dynamics via containment control

In this paper, we model the problem of influencing the opinions of groups of individuals as a containment control problem, as in many practical scenarios, the control goal is not full consensus among all the individual opinions, but rather their containment in a certain range, determined by a set of leaders. As in classical bounded confidence models, we consider individuals affected by the confirmation bias, thus tending to influence and to be influenced only if their opinions are sufficiently close. However, here we assume that the confidence level, modeled as a proximity threshold, is not constant and uniform across the individuals, as it depends on their opinions. Specifically, in an extremist society, the most radical agents (i.e., those with the most extreme opinions) have a higher appeal and are capable of influencing nodes with very diverse opinions. The opposite happens in a moderate society, where the more connected (i.e., influential) nodes are those with an average opinion. In three artificial societies, characterized by different levels of extremism, we test through extensive simulations the effectiveness of three alternative containment strategies, where leaders have to select the set of followers they try to directly influence. We found that, when the network size is small, a stochastic time-varying pinning strategy that does not rely on information on the network topology proves to be more effective than static strategies where this information is leveraged, while the opposite happens for large networks where the relevance of the topological information is prevalent

Emergence of extreme opinions in social networks

The emergence and spreading of “extreme opinions” are studied in networks with agents sharing mild opinions. The turning extreme shift is driven by social groupmeetings.The extremization process is apprehended according to the social psychology phenomenon of group polarization and illustrated in the case of terrorism. In particular the focus is on the dynamics of emergence of “passive supporters” from which terrorists can then be recruited. Becoming a passive supporter being considered as taking an extreme opinion, group polarization is shown to play an important role for increasing the transition probabilities frommild opinion (e.g., anti-western feeling) to its extreme form (e.g., passive supporter or terrorist). Accordingly a simple agent-based model is defined to implement interactions among agents on networks. Three opinions are considered, pro-western opinion, anti-western opinion and extreme anti-western opinion. The latter may lead people to become passive supporters and, potentially, terrorists. Results of simulations show that a substantial fraction of anti-western agents adopt the extreme opinion exhibiting an emergent phenomenon which may shed some new light on real social phenomena of political violence