Models and frameworks for studying social behaviors

Studies on social systems and human behavior are typically considered domain of humanities and psychology. However, it appears that recently these issues have attracted a strong interest also from the scienti�c community belonging to the hard sciences {in particular from physics, computer science and mathematics. The network theory o�ers powerful tools to study social systems and human behavior. In particular, complex networks have gained a lot of prestige as general framework for representing and analyze real systems. From an historical perspective, complex networks are rooted in graph theory {which in turn is dated back to 1736, when Leonhard Euler wrote the paper on the seven bridges of K�onigsberg. After Euler's work, di�erent mathematicians (e.g. Cayley) focused their research on graphs {opening the possibility of applying their results to deal with theoretical and real problems. As a result, complex networks emerged as multidisciplinary approach for studying complex systems. From a computational perspective, models based on complex networks allows to extract information on complex systems composed by a great number of interacting elements. A variety of systems can be modelled as a complex network (e.g. social networks, the World Wide Web, internet, biological systems, and ecological systems). To summarize, any such system should give the possibility of viewing its elements as simple (at some degree of abstraction), while assuming the existence of nonlinear interactions, the absence of a central control, and emergent behavior. Nowadays, scientists belonging to di�erent communities use complex networks as a framework for dealing with their preferred research issues, from a theoretical and/or pratical perspective. This work is aimed at illustrating some models, based on complex networks, deemed useful to represent social behaviors like competitive dynamics, groups formation, and emergence of linguistics phenomena.