Towards the 4th Industrial Revolution: Production networks, diversification and geography
The world is undergoing a process of rapid and profound transformation. New technologies such as artificial intelligence or biotechnology are revolutionizing industry, medicine and our daily activities . These technologies are producing structural changes in the global economy and the fourth industrial revolution has already begun . However, several studies have cautioned that these transformations do not occur in a uniform way between countries and industries . In addition, experts warn that countries heavily dependent on the export of raw materials will find it more difficult to adapt to these changes, due to their reduced diversification and economic complexity [3, 4, 5].
Studies have shown that the diversification and transformation of the productive base of a country is a path-dependent process, based mainly on the accumulation of knowledge and 'know-how' in the networks of individuals that make up a society (work teams, companies and cities) [6, 7, 8). This accumulation of knowledge and its transfers depend strongly on geographical proximity and cognitive affinity between productive activities [6, 9, 10, 11]. In recent years the understanding of the mechanisms that facilitate the accumulation of productive knowledge in a society has been deepened and optimal strategies have been found to achieve the diversification of regions .
Using Ecuador as a study case, this project analyses individual transactions between all the companies in the country [more than 100 000 firms], their productive linkages and respective geographic structures from 2007 to 2015. This information allows us to reconstruct the productive network of the whole country and generate scenarios to identify the potential areas of diversification, based on the most up-to-date scientific evidence on the processes of economic diversification and technological evolution of regions.
In addition, in this project we are using the state-of-the-art tools for analyzing economic and geographic data such as network analysis to understand the dynamics of productive chains; geographic information systems to link productive dynamics with regional dynamics; artificial intelligence and automatic learning for the development of scenarios and potential prediction of new business activities; and finally, intelligent data visualization tools to help understand and disseminate research outcomes.