Prediction and Prevention of Disproportionally Dominant Agents in Complex Networks

Abstract

We develop an early warning system and subsequent optimal intervention policy to avoid the formation of disproportional dominance (`winner-takes-all') in growing complex networks. This is modeled as a system of interacting agents, whereby the rate at which an agent establishes connections to others is proportional to its already existing number of connections and its intrinsic fitness. We derive an exact 4-dimensional phase diagram that separates the growing system into two regimes: one where the `fit-get-richer' (FGR) and one where, eventually, the `winner-takes-all' (WTA). By calibrating the system's parameters with maximum likelihood, its distance from the WTA regime can be monitored in real time. This is demonstrated by applying the theory to the eToro social trading platform where users mimic each others trades. If the system state is within or close to the WTA regime, we show how to efficiently control the system back into a more stable state along a geodesic path in the space of fitness distributions. It turns out that the common measure of penalizing the most dominant agents does not solve sustainably the problem of drastic inequity. Instead, interventions that first create a critical mass of high-fitness individuals followed by pushing the relatively low-fitness individuals upward is the best way to avoid swelling inequity and escalating fragility.