This talk will be presented by Sandro Lera from MIT Connection Science and will take place at 3 PM via Zoom.
Join here: https://us06web.zoom.us/j/87873172878?pwd=MmpZSWdReTh1d2RHUTBxalpiUndGQT09
Title: “Prediction and prevention of disproportional dominance 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,” WTA) 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 four-dimensional phase diagram that separates the growing system into two regimes: one where the “fit get richer” and one where, eventually, the WTA. By calibrating the system’s parameters with maximum likelihood, its distance from the unfavorable 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 other’s 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.
About Sandro Lera:
Sandro received his BSc & MSc in Physics from ETH Zurich, and his PhD from the Centre of Future Resilient Systems (Singapore) under the supervision of Didier Sornette
At MIT Connection Science, Sandro is developing tools to predict and prevent formation of disproportional inequality in socio-economic systems. More generally, his work is focused on the description of complex systems with tools from statistical physics, and applications of machine learning in algorithmic trading.
MIT Connection Science is world-wide alliance of progressive companies, nations, and multilateral organizations seeking to understand how to create data, analytical, and digital network systems that can improve the world.