The footprint of evolutionary processes of learning and selection upon the statistical properties of industrial dynamics

Evolutionary theories of economic change identify the processes of idiosyncratic learning by individual firms and of market selection as the two main drivers of the dynamics of industries. Are such processes able to robustly account for the statistical regularities which industrial structures and dynamics display? In this work we address this question by means of a simple agent-based model formalizing the mechanisms of learning and selection. The interplay between these two engines shapes the dynamics of entry-exit and market shares and, collectively, the productivity and the size distributions and their patterns of growth. As such, and despite its simplicity, the model is able to robustly reproduce an ensemble of empirical stylised facts, including ample heterogeneity in productivity distributions, persistent market turbulence and fat-tailed distribution of growth rates.