Non-Poisson Properties In Human Activities And Social Systems

How to dig out and understand the universal statistical properties in complex systems is an important issue in the researches of complex sciences. Non-Poisson property, illustrating the well-known power-law distribution, is one of the most wide-spread laws in complex systems. Since the simple form, special properties and rich physical meaning, it attracts much attention of many researchers. In recent several years, the non-Poisson properties are widely found in much number of human daily activities, including not only several types of temporal distributions, but also many spatial and intensity distributions. In this paper, we will investigate the non-Poisson properties in human behaviors and social systems from the empirical analysis, the modeling studies for the underlying mechanisms, and the effects on social dynamics, in which the modeling studies for the mechanisms of these non-Poisson properties are the focal point of this paper.For the temporal properties of human behaviors, we firstly analyze the patterns of human communication activities using short-messages and instant-messages, and observe multi-level scaling properties. In the modeling studies, different with the mainstream of queuing theory, we propose a novel human dynamics model based on the assumption of adaptive interest, and successfully explain the emergence of non-Poisson properties in human behaviors. Moreover, many other possible mechanisms that could impacts on human activities, such as the self-similar time planning, the effect of hierarchical organization, and the criticality in social contacts, are investigated in this paper.For the spatial mobility patterns of human daily activities, considering the hierarchical organization of real-world traffic systems, we propose a simple model and successfully generate the scaling displacement distributions and the localized properties observed in empirical studies. We further make a model based on the cascading movement to simulate human mobility patterns, and achieved many properties in agreement with the empirical observations. As a correlated issue, we propose a deterministic walk model to understand the scaling mobility patterns in animal behaviors, and found a new mechanism on the emergence of ordered collective movements, which provides a bridge to connect the individual scaling mobility patterns and the ordered collective movements. Furthermore, we study the epidemics spreading process based on the hierarchical traffic systems to investigate the effect of human mobility patterns on social dynamical systems.In this paper, the scaling laws in epidemic spreading are discussed using both the empirical analysis and the modeling studies. We discover Zipf’s law and Heaps’ law in the spatial distributions on the spreading processes of several epidemics, such as influenza A (H1N1), SARS and the bird cases of avian influenza. By the metapopulation-based modeling studies, we indicate that the effect of epidemic control or other adaptively changed repressive factors are the source of these scaling properties, which is also a novel mechanism in the generation of scaling laws.Except the power-law distributions, several other types of non-Poisson properties are also discussed in this paper. We construct the parameter-tuning networks to observe operator’s activity patterns in the tuning of experimental equipment, and find the stretched exponent degree distributions, which can be explained by an active walk model with the optimization process of output. We also find a new type of non-Poisson property which is called "bilinear effect" in several social systems, for instance, the lifetime distributions of ancient dynasties, and initially explain it by a hierarchical network model. Several other dynamical processes, such as the creation-annihilation cyclic games and the prisoner’s dilemma games with the effect of consumption, are also discussed in this paper.