This research provides a comprehensive analysis of the dynamic interplay between agent-based modeling (ABM) and artificial intelligence (AI) through a meticulous bibliometric study. This study reveals a substantial increase in scholarly interest, particularly post-2006, peaking in 2021 and 2022, indicating a contemporary surge in research on the synergy between AI and ABM. Temporal trends and fluctuations prompt questions about influencing factors, potentially linked to technological advancements or shifts in research focus. The sustained increase in citations per document per year underscores the field’s impact, with the 2021 peak suggesting cumulative influence. Reference Publication Year Spectroscopy (RPYS) reveals historical patterns, and the recent decline prompts exploration into shifts in research focus. Lotka’s law is reflected in the author’s contributions, supported by Pareto analysis. Journal diversity signals extensive exploration of AI applications in ABM. Identifying impactful journals and clustering them per Bradford’s Law provides insights for researchers. Global scientific production dominance and regional collaboration maps emphasize the worldwide landscape. Despite acknowledging limitations, such as citation lag and interdisciplinary challenges, our study offers a global perspective with implications for future research and as a resource in the evolving AI and ABM landscape.

In the context of an emergency, evacuating people from a location in the shortest possible time is essential, as is the high degree of safety that people should expect when evacuating. Lately, in Romania there have been more and more fire events generated by different causes. This article will use agent-based modeling to simulate an emergency evacuation model in NetLogo. The model has been used to perform and analyze various scenarios. With the help of NetLogo, we managed to perform 400 simulations with the evacuation of 180 people (students, teachers, and non-teaching staff) based on which we developed several recommendations to streamline the evacuation process in order to reduce the possibility of death. The present research will help to identify the evacuation times from a school, but it will also highlight certain aspects that may occur during the evacuation. The model that was used in this research took into account the individual particularities of the people taking part in the evacuation, emphasizing the effects that form in a crowd of people when evacuating; effects such as the funnel effect, which is caused by the formation of bottlenecks around narrow areas. All these things are part of the analysis of the measurement of entropy of the exhaust system, a problem that has captured all of the specialists’ attention. Finally, solutions have been proposed to improve evacuation time in case of disasters.

The uncertainty of the environment, the complexity of economic systems, both at the national and global economy levels, and the digital age and artificial intelligence draw attention to the existence or appearance of systemic, disruptive phenomena that can appear and propagate in different forms, producing effects that can turn into economic crises. These phenomena can be transmitted like a domino effect, and they are referred to as the contagion effect in the scientific literature. In this research, one of the four forms of financial contagion, known as residual contagion, is studied on the mortgage loan market in Romania using agent-based modeling. By considering the economic crisis of 2007–2009, also supported by the mortgage crisis, in the present paper, we aim to study the Romanian mortgage market in 2022 through the use of machine learning techniques and agent-based modeling. The purpose of this research is to capture the potential systemic risks that can outline a residual financial contagion effect. The simulation results highlight the fact that the degree of connectivity between the commercial banks in Romania and the way in which they are interconnected have a major importance in the emergence and propagation of contagion effects. The proposed approach and the obtained results can offer more insight to policymakers on how the contagion effect takes place within the banking sector.

This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The analysis covers the period between the year 2002 and the year 2023, utilizing data sourced from the Web of Science database. Employing the Bibliometrix package within RStudio and VOSviewer software, the study identifies a significant increase in AI-related publications, with an annual growth rate of 22.67%. Notably, key journals such as Remote Sensing, PLOS ONE, and Sustainability rank among the top contributing sources. From the perspective of prominent contributing affiliations, institutions like Duy Tan University, Ton Duc Thang University, and the Chinese Academy of Sciences emerge as leading contributors, with Vietnam, Portugal, and China being the countries with the highest citation counts. Furthermore, a word cloud analysis is able to highlight the recurring keywords, including “model”, “classification”, “prediction”, “logistic regression”, “innovation”, “performance”, “random forest”, “impact”, “machine learning”, “artificial intelligence”, and “deep learning”. The co-occurrence network analysis reveals five clusters, amongst them being “artificial neural network”, “regional development”, “climate change”, “regional economy”, “management”, “technology”, “risk”, and “fuzzy inference system”. Our findings support the fact that AI is increasingly employed to address complex regional challenges, such as resource management and urban planning. AI applications, including machine learning algorithms and neural networks, have become essential for optimizing processes and decision-making at the local level. The study concludes with the fact that while AI holds vast potential for transforming local and regional research, ongoing international collaboration and the development of adaptable AI models are essential for maximizing the benefits of these technologies. Such efforts will ensure the effective implementation of AI in diverse contexts, thereby supporting sustainable regional development.

The main purpose of this research is to study the predictive power of behavioural life profile models for mortgages using machine learning techniques and emerging languages from the same data sets. Based on the results, banks can determine whether the predictive power of the model can be improved regarding estimates of probability of redemption, and probability of internal transfer beyond traditional techniques. Model training will take place using algorithms based on machine learning such as: random forests, extreme gradient, boosting, light gradient boosting, Adaboost, and ExtraTrees. To perform simulations on fast learning and permit testing of hypotheses, the IBM cloud environment and the Watson proven analytical environment will be used, in order to maximize the value derived from the investment and determine the decision on the implementation and modelling strategy for business disciplines. Therefore, these factors could provide a solid basis for the sustainable development of the mortgage market, and the approach in this research is a starting point for identifying the best decisions taken by banking institutions to contribute to the sustainable development of mortgage lending.

This study describes a comprehensive bibliometric analysis of shadow banking and financial contagion dynamics from 1996 to 2022. Through a holistic approach, our study focuses on quantifying the impact and uncovering significant trends in scientific research related to these interconnected fields. Using advanced bibliometric methods, we explored the global network of publications, identifying key works, influential authors, and the evolution of research over time. The results of the bibliometric analysis have highlighted an annual growth rate of 22.05% in publications related to the topics of shadow banking and financial contagion, illustrating researchers’ interest and the dynamic nature of publications over time. Additionally, significant increases in scientific production have been recorded in recent years, reaching a total of 178 articles published in 2022. The most predominant keywords used in research include “systemic risks”, “risk assessment”, and “measuring systemic risk”. The thematic evolution has revealed that over time, the focus on fundamental concepts used in analyzing these two topics has shifted, considering technological advancements and disruptive events that have impacted the economic and financial system. Our findings provide a detailed insight into the progress, gaps, and future directions in understanding the complex interplay of shadow banking and financial contagion. Our study represents a valuable asset for researchers, practitioners, and policymakers with a keen interest in understanding the dynamics of these critical components within the global financial system.