The complexity of the intangible digital economy: an agent-based model

Since 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. This research work as a whole is aimed at investigating potential consequences on our economy deriving from digital technological progress. In particular, the contribution of the thesis is both empirical, theoretical and related to model design. On the empirical side, I 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. The analysis results are used to inform the first of the two frameworks of the agent-based macro-model Eurace that I employ to assess the long-term impact of digital investments on economy. In particular, in the first framework, a total factor augmenting approach has been used in order to model the digital technological progress because of the significant and positive correlation between total factor productivity and ICT capital investments, composed by a combination of both tangible and intangible investments which includes ICT technologies, software and database. In the second framework, I propose a different and innovative approach in which digital technological progress influences the elasticity of substitution between capital and labour. In this way, an increase of the elasticity of substitution can be seen as an increase in the tasks that machines can perform replacing human beings. In order to develop this approach, I substitute the Cobb-Douglas production function used in the first framework with a Leontief technology in which input factors are represented by organizational units. In turn, the contribution of each unit is given by a combination of capital and labour. The second framework results to be more realistic because it allows to distinguish between the various activities performed in the companies and the different education levels characterizing the workforce employed. Computational experiments show the emergence of technological unemployment in the long-run with a high pace of intangible digital investments. However, in the elasticity augmenting framework compensation mechanisms work more effectively leading to lower unemployment levels compared to the total factor augmenting one. Both frameworks are able to capture interesting features and empirical evidences characterizing our economic system

The Productivity and Unemployment Effects of the Digital Transformation: an Empirical and Modelling Assessment

Since 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 side. First, we present a cross-country empirical analysis assessing the correlation between intangible digital assets and different measures of productivity. Then we figure out their long-term impact on unemployment under different scenarios by means of an agent-based macro-model

Sustainability transition and digital trasformation: an agent-based perspective

Digital transformation and sustainability transition are complex phenomena characterized by fun-damental uncertainty. The potential consequences deriving from these processes are the subject of open de-bates among economists and technologists. In this respect, adopting a modelling and simulation approachrepresents one of the best solution in order to forecast potential effects linked to these complex phenom-ena. Agent-based modelling represents an appropriate paradigm to address complexity. This research aimsat showing the potential of the large-scale macroeconomic agent-based model Eurace in order to investigatechallenges like sustainability transition and digital transformation. This paper discusses and compares resultsof previous works where the Eurace model was used to study the digital transformation, while it presents newresults concerning the framework on the sustainability transition, where a climate agent is introduced to ac-count the climate economy interaction. As regards the digital transformation, the Eurace model is able to cap-ture interesting business dynamics characterizing the so-called increasing returns world and, in case of highrates of digital technological progress, it shows a significant technological unemployment. As regard the sus-tainability transition, it displays a rebound effect on energy savings that compromises efforts to reduce greenhouse gases emissions via electricity efficiency improvements. Furthermore, it shows that a carbon tax couldbe not sufficient to decouple economy from carbon consumption, and that a feed-in tariff policy fosteringrenewable energy production growth may be more effective

Digital Innovation and its Potential Consequences: the Elasticity Augmenting Approach

Digital technologies have been experiencing in the last thirty years a considerable development which has radically changed our economy and lives. In particular, the advent of new intangible technologies, represented by software, artificial intelligence and deep learning algorithms, has deeply affected our production systems from manufacturing to services, thanks also to further improvement of tangible computational assets. Investments in digital technologies have been increasing in most of developed countries, posing the issue of forecasting potential scenarios and consequences deriving form this new technological wave. The contribution of this paper is both theoretical and related to model design. First of all we present a new production function based on the concept of organizational units. Then, we enrich the macroeconomic model Eurace integrating this new function in the production processes in order to investigate the potential effects deriving from digital technologies innovation both at the micro and macro level

An economy under the digital transformation

During the last twenty years, we have witnessed the deep development of digital technologies. Artificial intelligence, software and algorithms have started to impact more and more frequently in our daily lives and most people didn't notice it. Recently, economists seem to have perceived that this new technological wave could have some consequences, but which one are they? Will they be positive or negative? In this paper we try to give a possible answer to these questions through an agent based computational approach; more specifically we enriched the large-scale macroeconomics model EURACE with the concept of digital technologies in order to investigate the effect that their business dynamics have at a macroeconomic level. Our preliminary results show that this productivity increase could be a double-edged sword: notwithstanding the development of the digital technologies sector can create new job opportunities, at the same time, these products could jeopardize the employment inside the traditional mass-production system

An investigation into modelling approaches for industrial symbiosis: a literature review

The aim of this paper is to understand how to model industrial symbiosis networks in order to favour its implementation and provide a framework to guide companies and policy makers towards it. Industrial symbiosis is a clear example of complex adaptive systems and traditional approaches (i.e., Input/Output analysis, Material flow analysis) are not capable to capture these dynamics behaviours. Therefore, the aim of this literature review is to investigate: i) the most used modelling and simulation approaches to analyse industrial symbiosis and ii) their characteristics in terms of simulation methods, interaction mechanisms and simulations software. Findings from our research suggest that a hybrid modelling and simulation approach, based on agent-based and system dynamics, could be an appropriate method for industrial symbiosis analysis and design

An economy under the digital transformation

During the last twenty years, we have witnessed the deep development of digital technologies. Artificial intelligence, software and algorithms have started to impact more and more frequently in our daily lives and most people didn't notice it. Recently, economists seem to have perceived that this new technological wave could have some consequences, but which one are they? Will they be positive or negative? In this paper we try to give a possible answer to these questions through an agent based computational approach; more specifically we enriched the large-scale macroeconomics model EURACE with the concept of digital technologies in order to investigate the effect that their business dynamics have at a macroeconomic level. Our preliminary results show that this productivity increase could be a double-edged sword: notwithstanding the development of the digital technologies sector can create new job opportunities, at the same time, these products could jeopardize the employment inside the traditional mass-production system

Epidemiology of gastroenteropancreatic neuroendocrine neoplasms: a review and protocol presentation for bridging tumor registry data with the Italian association for neuroendocrine tumors (Itanet) national database

: Neuroendocrine neoplasms (NENs) are rare tumors with diverse clinical behaviors. Large databases like the Surveillance, Epidemiology, and End Results (SEER) program and national NEN registries have provided significant epidemiological knowledge, but they have limitations given the recent advancements in NEN diagnostics and treatments. For instance, newer imaging techniques and therapies have revolutionized NEN management, rendering older data less representative. Additionally, crucial parameters, like the Ki67 index, are missing from many databases. Acknowledging these gaps, the Italian Association for Neuroendocrine Tumors (Itanet) initiated a national multicenter prospective database in 2019, aiming to gather data on newly-diagnosed gastroenteropancreatic neuroendocrine (GEP) NENs. This observational study, coordinated by Itanet, includes patients from 37 Italian centers. The database, which is rigorously maintained and updated, focuses on diverse parameters including age, diagnostic techniques, tumor stage, treatments, and survival metrics. As of October 2023, data from 1,600 patients have been recorded, with an anticipation of reaching 3600 by the end of 2025. This study aims at understanding the epidemiology, clinical attributes, and treatment strategies for GEP-NENs in Italy, and to introduce the Itanet database project. Once comprehensive follow-up data will be acquired, the goal will be to discern predictors of treatment outcomes and disease prognosis. The Itanet database will offer an unparalleled, updated perspective on GEP-NENs, addressing the limitations of older databases and aiding in optimizing patient care. STUDY REGISTRATION: This protocol was registered in clinicaltriasl.gov (NCT04282083)

The sustainability transition and the digital transformation: two challenges for agent-based macroeconomic models

Digital transformation and sustainability transition are complex phenomena characterized by fundamental uncertainty. The potential consequences deriving from these processes are the subject of open debates among economists and policy-makers. In this respect, adopting a modeling and simulation approach represents one of the best solutions in order to study potential effects linked to these complex phenomena. Agent-based modeling represents an appropriate paradigm to address complexity. This research aims at showing the potential of the large-scale macroeconomic agentbased model Eurace in order to investigate challenges like sustainability transition and digital transformation. In particular, two different simulation studies, i.e., the digital transformation and the sustainability transition are presented, in order to show the potential of the Eurace model in addressing such kinds of complex phenomena. As regards the digital transformation, the Eurace model is able to capture interesting business dynamics characterizing the so-called increasing returns world and, in case of high rates of digital technological progress, it shows significant technological unemployment. As regards the sustainability transition, it displays a rebound effect on energy savings that compromises efforts to reduce greenhouse gas emissions via electricity efficiency improvements. Furthermore, it shows that a carbon tax could be not sufficient to decouple the economy from carbon consumption and that a feed-in tariff policy fostering renewable energy production growth may be more effective