Spatial evolution of industries modelled by cellular automata

Traditional economic theories often neglect evolutionary aspects and thus offer sterile answers to essential questions grounded in economic reality, such as the dependence of industrial structure on technological progress, evolution of the cooperation/competition within or among industries, or evolutionary stability of cooperation networks. We present a conceptual model of industrial evolution based on agent-based modelling and cellular automata. In evolutionary simulation, the least fitted firms are repeatedly forced to adapt to the changing environment by partial mutations of their profiles. Following self-organised criticality, even a small change in an industrial profile can cause massive waves of firm restructuring causing new spatial patterns. In the long term, new industrial profiles emerge, and firms become self-organised in spatial clusters evolving towards Zipf's rank-size distribution. The proposed model is able to appropriately explain the long-term evolution of industrial economic structures in both time and space.

Automated summary

The study introduces a conceptual model of industrial evolution based on agent-based modeling and cellular automata, where poorly fitted firms are repeatedly forced to adapt to a changing environment through partial mutations of their profiles. This model appropriately explains the long-term evolution of industrial economic structures in both time and space by demonstrating how even small changes in industrial profiles can lead to massive waves of firm restructuring and the emergence of new spatial patterns.