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Detailed empirical studies of publicly traded business firms have established that the standard deviation of annual sales growth rates decreases with increasing firm sales as a power law, and that the sales growth distribution is non-Gaussian with slowly decaying tails. To explain these empirical facts, a theory is developed that incorporates both the fluctuations of a single firms sales and the statistical differences among many firms. The theory reproduces both the scaling in the standard deviation and the non-Gaussian distribution of growth rates. Earlier models reproduce the same empirical features by splitting firms into somewhat ambiguous subunits; by decomposing total sales into individual transactions, this ambiguity is removed. The theory yields verifiable predictions and accommodates any form of business organization within a firm. Furthermore, because transactions are fundamental to economic activity at all scales, the theory can be extended to all levels of the economy, from individual products to multinational corporations.
Inter-firm organizations, which play a driving role in the economy of a country, can be represented in the form of a customer-supplier network. Such a network exhibits a heavy-tailed degree distribution, disassortative mixing and a prominent communit
We introduce a model for the adaptive evolution of a network of company ownerships. In a recent work it has been shown that the empirical global network of corporate control is marked by a central, tightly connected core made of a small number of lar
In order to model volatile real-world network behavior, we analyze phase-flipping dynamical scale-free network in which nodes and links fail and recover. We investigate how stochasticity in a parameter governing the recovery process affects phase-fli
Understanding cities is central to addressing major global challenges from climate and health to economic resilience. Although increasingly perceived as fundamental socio-economic units, the detailed fabric of urban economic activities is only now ac
We have recently introduced the ``thermal optimal path (TOP) method to investigate the real-time lead-lag structure between two time series. The TOP method consists in searching for a robust noise-averaged optimal path of the distance matrix along wh