Emergence of complex socioeconomic networks driven by individual and collective interests

Abstract

The basic mechanisms that lead to the creation of complex socioeconomic structures remain poorly understood. A combination of network science and game theory can help explain them by examining how simple networks interact to build complex entities, both when connections among individuals are exclusively guided by self-interest or when they result from a mixture of individual and collective motivations. Here we present a theoretical framework where individuals or human groups from different communities connect to each other only if they increase their own eigenvector centrality, a topological measure of wide applications in many different contexts that quantifies the importance of a node within the network. Our analytical and numerical results show that the emergence of interconnected networks is catalyzed by the self-interest of peripheral agents, who are penalized in the long run but transiently benefit from establishing links with nodes from other communities. Moreover, the interconnection process leads to a hierarchical, assortative, and very efficient structure where links across networks involve nodes of the same importance. These findings are robust to the introduction of moderate levels of collective-oriented behavior and compatible with the interconnection dynamics observed in real-world socioeconomic networks.