Global natural projections.

The paper contributes to the debate on natural interest rates and potential growth rates. We build a model-based projection of the world's most significant economies/areas to improve understanding of their change over the long run and the factors behind their decline. We use a general equilibrium overlapping generation model to understand the simultaneous role of demographics, technology, and globalization. The novelty of the model lies in the way it constructs a human capital index based on UN population projections and an estimated increasing returns production function for major economies worldwide. We find that the decline in interest rates is well explained through labor market dynamics and the increasing obsolescence of capital goods. We also find that a reduced share of labor income has caused movement in the opposite direction, leading to an increase in natural interest rates, which runs counter to the empirical evidence. Moreover, the dynamics of economic integration predict an endogenous adjustment of global imbalances over the long run, with an increasing weight of the Chinese economy and, consequently, a phase of weakness in United States growth between 2030 and 2040. The model is also used to perform shock scenario analysis. We find that demographic decline can adversely affect the growth dynamics for European countries, while a change in the dynamics of globalization can have serious consequences, especially for the United States, with significant benefits for European countries and China.

Economic and labour market impacts of migration in Austria: an agent-based modelling approach.

This study examines the potential economic and labour market impacts of a hypothetical but plausible migration scenario of 250,000 new migrants inspired by Austria's experience in 2015. Using the agent-based macroeconomic model developed by Poledna et al. (Eur Econ Rev, 151:104306, 2023. 10.1016/j.euroecorev.2022.104306, the study explores the detailed labour market outcomes for different groups in Austria's population and the macroeconomic effects of the migration scenario. The analysis suggests that Austria's economy and labour market have the potential to be resilient to the simulated migration influx. The results indicate a positive impact on GDP due to increased aggregate consumption and investment. The labour market experiences an increase in the unemployment rates of natives and previous migrants. In some industries, the increase in the unemployment rates is more significant, potentially indicating competition among different groups of migrants. This research provides insights for policymakers and stakeholders in Austria and other countries that may face the challenge of managing large-scale migration in the near future. Supplementary Information: The online version contains supplementary material available at 10.1186/s40878-024-00374-3.

Bayesian Networks in the Management of Hospital Admissions: A Comparison between Explainable AI and Black Box AI during the Pandemic.

Artificial Intelligence (AI) and Machine Learning (ML) approaches that could learn from large data sources have been identified as useful tools to support clinicians in their decisional process; AI and ML implementations have had a rapid acceleration during the recent COVID-19 pandemic. However, many ML classifiers are "black box" to the final user, since their underlying reasoning process is often obscure. Additionally, the performance of such models suffers from poor generalization ability in the presence of dataset shifts. Here, we present a comparison between an explainable-by-design ("white box") model (Bayesian Network (BN)) versus a black box model (Random Forest), both studied with the aim of supporting clinicians of Policlinico San Matteo University Hospital in Pavia (Italy) during the triage of COVID-19 patients. Our aim is to evaluate whether the BN predictive performances are comparable with those of a widely used but less explainable ML model such as Random Forest and to test the generalization ability of the ML models across different waves of the pandemic.

Performance of an AI algorithm during the different phases of the COVID pandemics: what can we learn from the AI and vice versa.

Background: Artificial intelligence (AI) has proved to be of great value in diagnosing and managing Sars-Cov-2 infection. ALFABETO (ALL-FAster-BEtter-TOgether) is a tool created to support healthcare professionals in the triage, mainly in optimizing hospital admissions. Methods: The AI was trained during the pandemic's "first wave" (February-April 2020). Our aim was to assess the performance during the "third wave" of the pandemics (February-April 2021) and evaluate its evolution. The neural network proposed behavior (hospitalization vs home care) was compared with what was actually done. If there were discrepancies between ALFABETO's predictions and clinicians' decisions, the disease's progression was monitored. Clinical course was defined as "favorable/mild" if patients could be managed at home or in spoke centers and "unfavorable/severe" if patients need to be managed in a hub center. Results: ALFABETO showed accuracy of 76%, AUROC of 83%; specificity was 78% and recall 74%. ALFABETO also showed high precision (88%). 81 hospitalized patients were incorrectly predicted to be in "home care" class. Among those "home-cared" by the AI and "hospitalized" by the clinicians, 3 out of 4 misclassified patients (76.5%) showed a favorable/mild clinical course. ALFABETO's performance matched the reports in literature. Conclusions: The discrepancies mostly occurred when the AI predicted patients could stay at home but clinicians hospitalized them; these cases could be handled in spoke centers rather than hubs, and the discrepancies may aid clinicians in patient selection. The interaction between AI and human experience has the potential to improve both AI performance and our comprehension of pandemic management.