文献类型：期刊文献

英文题名：Emerging social brain: A collective self-motivated Boltzmann machine

作者：Tao, Yong[1,2];Sornette, Didier[2,3,4,5];Lin, Li[6]

机构：[1]Southwest Univ, Coll Econ & Management, Chongqing, Peoples R China;[2]Swiss Fed Inst Technol, Dept Management Technol & Econ, Zurich, Switzerland;[3]Southern Univ Sci & Technol, Acad Adv Interdisciplinary Studies, Inst Risk Anal Predict & Management, Shenzhen 518055, Peoples R China;[4]Tokyo Inst Technol, Inst Innovat Res, Tokyo Tech World Res Hub Initiat WRHI, Tokyo, Japan;[5]Univ Geneva, Swiss Finance Inst, 40 Blvd Du Pont Arve, CH-1211 Geneva 4, Switzerland;[6]East China Univ Sci & Technol, Sch Business, Shanghai, Peoples R China

年份：2021

卷号：143

外文期刊名：CHAOS SOLITONS & FRACTALS

收录：;EI(收录号：20205309699956);WOS:【SSCI(收录号:WOS:000620178200026)，SCI-EXPANDED(收录号:WOS:000620178200026)】；

基金：This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. SWU14094 44), the Social Science Planning Project of Chongqing (Grant No. 2019PY40), and the State Scholarship Fund granted by the China Scholarship Council.

语种：英文

外文关键词：Boltzmann machine; Swarm intelligence; Social brain; Boltzmann distribution; Self-reference; Self-organization

摘要：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 brainlike structure. (c) 2020 Elsevier Ltd. All rights reserved.