Rise of nations: Why do empires expand and fall?

S. Vakulenko, Dmitry Lyakhov, A. G. Weber, D. Lukichev, Dominik L. Michels

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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 ventually ends up in the "control trap."
Original languageEnglish (US)
Pages (from-to)093108
Issue number9
StatePublished - Sep 1 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-05
Acknowledgements: This work has been partially supported by the Government of the Russian Federation (Grant No. 08-08), the King Abdullah University of Science and Technology (KAUST baseline funding), and the United States National Institutes of Health (Grant No. RO1 OD010936, formerly No. RR07801).


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