In a previous post I’ve elaborated about a paper of E. Majorana (a well-known Italian physicist), on “The value of Statistical Laws in Physics and in Social Sciences”. He argued an analogy between Physics and social sciences, indicating similarities. The more I read on Sociophysics, the more I’m convinced that E. Majorana was right.
Sociophysics is a research avenue aiming at a modelling of large scale social phenomena, like people behaviours, opinion formation, information and cultural dissemination, etc. Idea is investigating the emergence collective phenomena from the interactions of individuals as elementary units in social structures. Typically, Sociophysics works draws inspiration from principles of theoretical condensed matter physics including the Ising model of magnetism, higher order Potts models and continuous XY models. For example phase transition is one of the most studied scale social phenomena (the term indicates an abrupt change in a global system property at a critical level of local interactions, followed by the emergence of a new structure).
About Sociophysics see this paper about technology adoption and Consumer behavior
We also know that behind these interactions there are networks, whose nature influence the emergent phenomena. In this sense, I would also look for complementing Sociophysics with the similar analogy between Physics and the technology evolution of communication networks. Actually, future networks will be everywhere: interactions of large number of (real and virtual) interacting nodes and devices will give rise to complex dynamic networks (of humans and things) with wide extensions.
Consider this example. One of the main challenges of future network deployment and operation processes is exploiting self-configuration (which is deriving and setting automatically the values of sensible configuration parameters for pieces of equipment and devices). The reverse side of the coin is the risk that distributed self-configurations might significantly increase complex interferences of systems’ states. As a matter of fact, the cascading and nesting of automatic control-loops and mechanisms (a typical approach for implementing self-management) can determine the emergence of non linear network behaviours (e.g. this also known as network phase-transitions). From this perspective, phase transition analysis appears to be a promising approach for determining network stability, and efficient network operating points for various global properties.
Modeling and making simulations-emulations of networks by taking these two complementary viewpoints (Sociophysics and Netphysics) might provide a wider systemic understanding of future networks. To my knowledge there is very little being done from this perspective.