During the last decades, we have witnessed a strong development of intangible digital technologies. Software, artificial intelligence and algorithms are increasingly affecting both production systems and our lives; economists have started to figure out the long-run complex economic implications of this new technological wave. In this paper, we address this question through the agent-based modelling approach. In particular, we enrich the macroeconomic model Eurace with the concept of intangible digital technology and investigate its effects both at the micro and macro level. Results show the emergence of the relevant stylized facts observed in the business domain, such as increasing returns, winner-take-most phenomena and market lock-in. At the macro level, our main finding is an increasing unemployment level, since the sizeable decrease of the employment rate in the mass-production system, provided by the higher productivity of digital assets, is usually not counterbalanced by the new jobs created in the digital sector.

For the last 30 years, the economy has been undergoing a massive digital transformation. Intangible digital assets, like software solutions, Web services, and more recently deep learning algorithms, artificial intelligence, and digital platforms, have been increasingly adopted thanks to the diffusion and advancements of information and communication technologies. Various observers argue that we could rapidly approach a technological singularity leading to explosive economic growth. The contribution of this paper is on the empirical and the modelling sides. On the empirical side, we present a cross-country empirical analysis assessing the correlation between the growth rate of both tangible and intangible investments and different measures of productivity growth. Results show a significant correlation between intangible investments and both labor and total factor productivity in the period after the 2008 financial crisis. Similarly, both measures of productivity growth are correlated with a combination of both tangible and intangible investments which include information and communication technologies and software and database. These results are used to inform the enrichment of the agent-based macro-model Eurace that we employ to assess the long-term impact on unemployment of digital investments. Computational experiments show the emergence of technological unemployment in the long run with a high pace of intangible digital investments.

Sustainable industrial systems are complex, since they exhibit both detail and dynamic complexity. Only an integrated approach is able to provide a realistic view of such complex systems providing a useful insight into their behavior. In this paper, a hybrid approach based on Agent Based Modeling (ABM) and System Dynamics (SD) is presented in order to improve modeling insight of an industrial symbiosis (IS) context. Hybrid approaches have gained prominence overpassing limitations of traditional methodologies and tools, as well as computational advances, that permit better modeling and analysis of complex systems with a particular focus on sustainability topics exploiting the strengths of both ABM and SD models, while minimizing the drawbacks. Therefore, to provide a methodological proof, an application of the proposed hybrid approach to an industrial symbiosis relevant case is presented and discussed. The methodological approach adopted in this research can be used to investigate a variety of industrial symbiosis cases providing insights usually not achievable with standard techniques and tools.

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