Social Brain: A Perspective of Swarm Intelligence in Humans

Abstract

Boltzmann machines are unsupervised-learning neural networks, and have successfully opened the deep learning architectures. Here we show that, as an evolutionary result of natural selection, when the number of agents in a free-market society with equal opportunity exceeds a threshold value, a Boltzmann-like income distribution will emerge, where the entropy plays the role of swarm intelligence in humans. Theoretically, we further show that the emergence of a Boltzmann-like income distribution will spontaneously induce the human society to form a Boltzmann machine in which each person plays the role of neuron. 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) exactly follows the Boltzmann-like distribution. Our results imply that the equal-opportunity classes in a well-functioning free-market society, which occupy the great majority of populations, resemble a brain that has the function of unsupervised learning. This finding reinterprets the super-intelligence of human societies in a perspective of deep learning.