Advances in Complex Systems

Real-world financial dynamics daily do challenge the credibility of the Efficient Market Hypothesis, the pillar of the whole martingale-based modern financial theory stating that at any time asset prices discount all past information. As a matter of fact, the empirical evidence accumulated so far indicates that current models cannot explain the complexity of financial market movements, to the extent that a strand of skeptical thought, the Behavioral Finance, has been booming. The question whether a model exists which is able to make consistent the two paradigms is a living matter that financial markets demand to address. The paper deals with a parsimonious stochastic model able to include as special cases both market efficiency and "psychological" phenomena such as the underreaction and the overreaction, peculiar features of the behavioral finance. The great readability of the model, its capability to agree the controversial results provided by literature on efficient markets and the simplicity of the financial intuition it offers are discussed.

Empirical findings from social psychology show that sometimes people show favoritism toward in-group members in order to reach a global consensus, even against individuals' own preferences (e.g., altruistically or deontically). Here we integrate ideas and findings on in-group favoritism, opinion dynamics, and radicalization using an agent-based model entitled cooperative bounded confidence (CBC). We investigate the interplay of homophily, rejection, and in-group cooperation drivers on the formation of opinion clusters and the emergence of extremist, radical opinions. Our model is the first to explicitly explore the effect of in-group favoritism on the macro-level, collective behavior of opinions. We compare our model against the two-dimentional bounded confidence model with rejection mechanism, proposed by Huet et al. [Adv. Complex Syst.13(3) (2010) 405–423], and find that the number of opinion clusters and extremists is reduced in our model. Moreover, results show that group influence can never dominate homophilous and rejecting encounters in the process of opinion cluster formation. We conclude by discussing implications of our model for research on collective behavior of opinions emerging from individuals' interaction.

By incorporating multi-community attachments into the continuous opinion formation model, we investigate the driving force for reaching consensus in networked systems. A phase transition from fragmentation to consensus is determined by the multiplex network structures and the coupling of different communities. A moderate number of layers and weak coupling are beneficial for consensus. The evolutionary dynamics in the present model is governed by the prolonged evolutionary time, which results from the disparities of individual opinions in different communities. Increasing the multiple layers is similar to promoting coupling between different communities. A functional relation between the convergence time and the coupling coefficient is found.

Random walks are one of the best investigated dynamical processes on graphs. A particularly fascinating phenomenon is the scaling relationship of fluctuations σ with the average flux 〈f〉. Here we analyze how network topology and nodes with finite capacity lead to deviations from a simple scaling law σ ~ 〈f〉^α. Sources of randomness are the random walk itself (internal noise) and the fluctuation of the number of walkers (external noise). We obtained exact results for the extreme case of a star network which are indicative of the behavior of large scale systems with a broad degree distribution. The latter are subsequently studied using Monte Carlo simulations. We find that the network heterogeneity amplifies the effects of external noise. By computing the "effective" scaling of each node we show that multiple scaling relationships can coexist in a graph with a heterogeneous degree distribution at an intermediate level of external noise. Finally, we analyze the effect of a finite capacity of nodes for random walkers and find that this also can lead to a heterogeneous scaling of fluctuations.

The possibility of spreading by migration, colonizing new spatial domains suitable for development and reproduction, can substantially relieve a biological population from the risk of extinction. By means of a realistic computational model based on empirical data, we study this phenomenon for the human head louse, Pediculus humanus capitis. In particular, we show that a lice colony infesting a single isolated host is prone to extinction by stochastic population fluctuations within an interval of several months, while migration over a relatively small group of hosts in contact with each other is enough to insure the prevalence of the infestation for indefinitely long periods. We characterize the interplay of the size of the host group with the host-to-host contagion probability, which controls a transition between extinction of the lice population and a situation where the infestation is endemic.