Self Organised Criticality in Economic and Social Networks

Abstract

Diverse approaches of innovation diffusion, in the presence of increasing returns, have been outlined or explored in the recent literature. We propose, four ourselves, to take into account the idea that agents, in the situation to adopt or not an innovation or a new technological standard, are “situated” within a social network, that is the support of influence effects. Our approach aim is here to explore the role of learning processes into the propagation dynamics within a network structure. In a recent model, formally represented by a neural network, we have introduced a relational learning that constitutes a way to set up an endogenous network evolution. We prove the existence of a self organised criticality phenomenon, where some agents acquire key-positions within the network that bring them a strong structural capacity of influence over the whole population of potential adopters. In this paper, we study the way how network auto-organisation can lead, under given conditions, to a critical state characterised by macroscopic effects generated from microscopic impulses at the level of the individual agent. It is the peculiar structure of those critical networks that allow macroscopic “avalanches” to take place, on which the diffusion process is likely to lean. We analyse the way learning leads endogenously to such a critical state and how it strikes against the finite size of the network.