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dc.contributor.author
Lera, Sandro C.
dc.contributor.author
Pentland, Alex
dc.contributor.author
Sornette, Didier
dc.date.accessioned
2020-11-17T10:11:02Z
dc.date.available
2020-11-13T18:55:50Z
dc.date.available
2020-11-17T10:11:02Z
dc.date.issued
2020-11-03
dc.identifier.issn
0027-8424
dc.identifier.issn
1091-6490
dc.identifier.other
10.1073/pnas.2003632117
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/451352
dc.identifier.doi
10.3929/ethz-b-000451352
dc.description.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. Copyright © 2020 the Author(s). Published by PNAS.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
National Academy of Sciences
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.title
Prediction and prevention of disproportionally dominant agents in complex networks
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.date.published
2020-10-16
ethz.journal.title
Proceedings of the National Academy of Sciences of the United States of America
ethz.journal.volume
117
en_US
ethz.journal.issue
44
en_US
ethz.journal.abbreviated
Proc Natl Acad Sci U S A
ethz.pages.start
27090
en_US
ethz.pages.end
27095
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Washington, DC
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03738 - Sornette, Didier / Sornette, Didier
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03738 - Sornette, Didier / Sornette, Didier
ethz.date.deposited
2020-11-13T18:56:09Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-11-17T10:11:14Z
ethz.rosetta.lastUpdated
2022-03-29T04:02:45Z
ethz.rosetta.versionExported
true
ethz.COinS
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