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dc.contributor.author
Tao, Yong
dc.contributor.author
Sornette, Didier
dc.contributor.author
Lin, Li
dc.date.accessioned
2021-01-11T10:12:34Z
dc.date.available
2021-01-03T03:38:34Z
dc.date.available
2021-01-11T10:12:34Z
dc.date.issued
2021-02
dc.identifier.issn
0960-0779
dc.identifier.issn
1873-2887
dc.identifier.other
10.1016/j.chaos.2020.110543
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/458868
dc.description.abstract
Boltzmann machines are unsupervised-learning neural networks, which have contributed to the opening of the field of deep learning architectures. Here we show that, using the modern theory of economic growth, when the number of agents in a free-market society with equal opportunity exceeds a threshold value, a Boltzmann-like income distribution emerges, where the entropy plays the role of swarm intelligence in humans and quantifies its cumulative technological progress. Theoretically, we further show that the emergence of a Boltzmann-like income distribution in a society of optimizing agents reflects the spontaneous organization of a human society to form a Boltzmann machine in which each person plays a role analogous to that of a neuron within a brain-like architecture. This Boltzmann machine exhibits three essential brain-like features, namely the McCulloch-Pitts learning rule, unsupervised-learning, and self-motivation, and satisfies in addition the minimum free-energy principle of the brain theory. Empirically, we investigate the household income data from 66 free-market countries and Hong Kong SAR, and find that, for all of the countries, the income structure for low and middle classes (about 95% of populations) is accurately described by a Boltzmann-like distribution. We suggest that this is a statistical signature that our social networks are going through a critical evolution in the form of a kind of brain-like structure. © 2020 Elsevier Ltd
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
Boltzmann machine
en_US
dc.subject
Swarm intelligence
en_US
dc.subject
Social brain
en_US
dc.subject
Boltzmann distribution
en_US
dc.subject
Self-reference
en_US
dc.subject
Self-organization
en_US
dc.title
Emerging social brain: A collective self-motivated Boltzmann machine
en_US
dc.type
Journal Article
dc.date.published
2020-12-28
ethz.journal.title
Chaos, Solitons & Fractals
ethz.journal.volume
143
en_US
ethz.pages.start
110543
en_US
ethz.size
10 p.
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Amsterdam
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 (emeritus) / Sornette, Didier (emeritus)
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 (emeritus) / Sornette, Didier (emeritus)
ethz.date.deposited
2021-01-03T03:38:38Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-01-11T10:12:42Z
ethz.rosetta.lastUpdated
2022-03-29T04:46:26Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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