[Mingli Lecture Hall 2021 Issue 15] Assistant Professor Sandro Lera, Southern University of Science and Technology:Prediction and Prevention of Disproportionally Influential Agents in Complex Networks

Time: April 7 (Wednesday) afternoon: 15:30-16:30

Location: Main Building 326

Speaker: Assistant Professor Sandro Lera, Southern University of Science and Technology

Speaker profile:

Dr. Sandro Lera, Assistant Professor at Risks X, AAIS and Division of InformationSystems Management Engineering of SUSTech visiting scholar atthe Massachusetts Institute of Technology (MIT).He received his PhD from ETH Zurich under supervision of Prof Didier Sornette. His work is focused on the prediction of extreme events in socio-economic systems with tools from statistical physics and machine learning.He has an industry background in algorithmic trading, developing quantitative tradingstrategies for several companies and across different asset classes.

Report Content Description:

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 combining the theory with big data in two applications: the social trading platform eToroand US supply-chain networks.

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. We analyze (US) supply chain data in context of our model, and find 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.

Our results call for a more wholistic approach, with important implications for the structure of regulatory matters such as antitrust policies, taxation law, subsidies, or development aid.

(Organized by: Department of Management Science and Logistics, Scientific Research and Academic Exchange Center)