Rise of nations: Why do empires expand and fall?
Author(s) -
S. A. Vakulenko,
Dmitry Lyakhov,
Andreas Weber⋆,
Dmitry V. Lukichev,
Dominik L. Michels
Publication year - 2020
Publication title -
chaos an interdisciplinary journal of nonlinear science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.971
H-Index - 113
eISSN - 1089-7682
pISSN - 1054-1500
DOI - 10.1063/5.0004795
Subject(s) - controllability , attractor , pairwise comparison , stability (learning theory) , computer science , complex network , noise (video) , field (mathematics) , statistical physics , topology (electrical circuits) , mathematics , physics , artificial intelligence , pure mathematics , mathematical analysis , combinatorics , machine learning , world wide web , image (mathematics)
We consider centralized networks composed of multiple satellites arranged around a few dominating super-egoistic centers. These so-called empires are organized using a divide and rule framework enforcing strong center–satellite interactions while keeping the pairwise interactions between the satellites sufficiently weak. We present a stochastic stability analysis, in which we consider these dynamical systems as stable if the centers have sufficient resources while the satellites have no value. Our model is based on a Hopfield type network that proved its significance in the field of artificial intelligence. Using this model, it is shown that the divide and rule framework provides important advantages: it allows for completely controlling the dynamics in a straight-forward way by adjusting center–satellite interactions. Moreover, it is shown that such empires should only have a single ruling center to provide sufficient stability. To survive, empires should have switching mechanisms implementing adequate behavior models by choosing appropriate local attractors in order to correctly respond to internal and external challenges. By an analogy with Bose–Einstein condensation, we show that if the noise correlations are negative for each pair of nodes, then the most stable structure with respect to noise is a globally connected network. For social systems, we show that controllability by their centers is only possible if the centers evolve slowly. Except for short periods when the state approaches a certain stable state, the development of such structures is very slow and negatively correlated with the size of the system’s structure. Hence, increasing size eventually ends up in the “control trap.”
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