The Knowledge Complexity of the European Metropolitan Areas: Selecting and Clustering Their Hidden Features

The role of urbanization in economic development attracted increasing popularity in recent years (OECD, 2015; UN‑Habitat, 2016). Urbanization, indeed, increased from 30% to 50% worldwide in the last 50 years and is expected to keep growing (UN‑DESA, 2019). Moreover, while metropolitan areas cover only a tiny fraction of the planet, they are highly productive places, fundamental for national competitiveness in global markets. For instance, between 2000 and 2012, metropolitan areas accounted for about 45% of the EU15 Gross Domestic Product (GDP), although covering only 10% of its land (own elaboration from OECD, 2013b). Understanding cities’ economic performance is crucial to support countries’ economic growth. However, it is not straightforward to capture cities’ dynamics, and standard (neoclassical) economic policy tools seem not well suited to deal with their complexity. This chapter contributes to show that a complexity economics perspective is more suitable for the analysis of cities’ economy. Neoclassical and complexity economics correspond to distinct ontological claims about the world (Arthur, 1999; 2021) and, like oil and water, cannot mix with each other (Fontana, 2010). Indeed, neoclassical economics describes an economic system as composed of some (soundlessly rational) representative agents who, in facing well‑defined problems, behave consistently with the aggregate outcome of their actions (Arthur, 2021). Without the intervention of some extra‑economic factor, the outcome of such a “well‑functioning machine” will be a timeless equilibrium, where there cannot be growth, if not in quantitative terms (Schumpeter, 1911). On the contrary, complexity economics looks at economies as an evolving system in which novelty emerges from within because of the creative reactions of its agents to macro‑level out‑of‑equilibrium conditions (Antonelli, 2015; Schumpeter, 1947). Agents need to collectively contribute to develop a knowledge base constituted of a coherent scaffolding of technologies, institutions, firms, routines, etc. In this way, the emergent environment that they co‑create via decentralized efforts will guide them toward mutually satisfactory ends, in a continuous feedback loop process. Metropolitan areas hardly fit with the neoclassical paradigm, being perfect examples of complex evolving systems with many interacting physical and social components (Batty, 2013; Jacobs, 1961).