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Prediction and prevention of disproportionally dominant agents in complex networks

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 ot...

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Autores principales: Lera, Sandro Claudio, Pentland, Alex, Sornette, Didier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959489/
https://www.ncbi.nlm.nih.gov/pubmed/33067387
http://dx.doi.org/10.1073/pnas.2003632117
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author Lera, Sandro Claudio
Pentland, Alex
Sornette, Didier
author_facet Lera, Sandro Claudio
Pentland, Alex
Sornette, Didier
author_sort Lera, Sandro Claudio
collection PubMed
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 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.
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spelling pubmed-79594892021-03-22 Prediction and prevention of disproportionally dominant agents in complex networks Lera, Sandro Claudio Pentland, Alex Sornette, Didier Proc Natl Acad Sci U S A Physical Sciences 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. National Academy of Sciences 2020-11-03 2020-10-16 /pmc/articles/PMC7959489/ /pubmed/33067387 http://dx.doi.org/10.1073/pnas.2003632117 Text en Copyright © 2020 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Physical Sciences
Lera, Sandro Claudio
Pentland, Alex
Sornette, Didier
Prediction and prevention of disproportionally dominant agents in complex networks
title Prediction and prevention of disproportionally dominant agents in complex networks
title_full Prediction and prevention of disproportionally dominant agents in complex networks
title_fullStr Prediction and prevention of disproportionally dominant agents in complex networks
title_full_unstemmed Prediction and prevention of disproportionally dominant agents in complex networks
title_short Prediction and prevention of disproportionally dominant agents in complex networks
title_sort prediction and prevention of disproportionally dominant agents in complex networks
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959489/
https://www.ncbi.nlm.nih.gov/pubmed/33067387
http://dx.doi.org/10.1073/pnas.2003632117
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