## 2 Materials and Methods  
Bibliometric analysis relies on statistical methodologies and specialized software tools to extract, process, and interpret data pertaining to scientific output. The primary aim of this approach is to present an unbiased overview of the progression within a research field, delineate the contributions made by authors or institutions, and pinpoint potential directions for future research. In our bibliometric analysis, we utilized the R Studio software, incorporating the bibliometrix package and the \"bibliostiny(0)\" function [34]. The \"bibliostiny(0)\" function, as outlined in [34; 36; 37], furnishes a diverse set of functionalities for our intended bibliometric analysis. It enables the extraction of bibliometric data from various sources like Scopus and WoS, generates key bibliometric indicators (e.g., publication count, H-index, citation count), facilitates comparative analyses, and supports the visualization of results through interactive graphics such as network maps, diagrams, and geographic maps. Additionally, it provides the capability to export these results for further use and dissemination. To guarantee transparency and structure in implementing the research [38], we delineated the stages of the methodological process following the guidelines put forth by [PERSON] and [PERSON] [39]. The authors have outlined a procedural workflow for executing scientific mapping studies within the realms of management and organization. From this point of view, the process we will follow in conducting the bibliometric analysis will be as presented below, adapted from [PERSON] et al. [38] and [PERSON] and [PERSON] [39]. Step 1. Study design: The data selection process is a crucial step in refining the dataset for further exploration. Each stage aims to obtain a set of articles relevant to our study on ABM applications and AI. The keywords used in Table 1 were chosen to cover key aspects of this study domain, ensuring precise and specific coverage. Our study explores the interaction between two distinct research fields: AI and agent-based models, and the chosen keywords reflect the interdisciplinary complexity of our topic. Additionally, the selected keywords stem from our main research question: How can AI be utilized in ABM applications? Step 2. Synthesis of Bibliometric Data: Regarding this step in the process, the bibliometric database WoS was chosen, and various queries were conducted based on the following keywords: \"agent-based modeling\", \"agent-based modelling\", \"agent-based model\", \"agent-based models\", \"artificial intelligence\", \"machine learning\", and \"deep learning\". These key concepts were searched in the title, abstract, and keywords of articles, covering the period from 2000 to 2022. The 22-year period chosen for our study acknowledges the swift and substantial evolution witnessed in the field of AI during the past two decades. Our selected time frame encapsulates the era when AI emerged as a prominent domain for research and development [40]. Additionally, significant technological progress, changes in research methodologies, and the advent of new paradigms have prominently characterized recent decades [41]. These factors, which have influenced our decision regarding the time frame, are also underscored in the 2020 technical report issued by the European Commission [42]. Subsequently, only article-type queries written in English as an internationally recognized language were retained. This stage will be extensively described in the interpretation of Table 1.  
Regarding the dataset extraction, it shall be stated that the WoS platform offers, based on subscription, personalized access to data. As a result, as [PERSON] [43] and [PERSON] [44] observed, the results of the bibliometric analysis are highly dependent on the user's access to the ten indexes offered by the WoS. In this context, the authors recommend that the bibliometric papers clearly state the access the users had to the index offered by WoS [43; 44].