## 3 Dataset Analysis
### Mixed Analysis  
In this section, we will conduct a mixed analysis by integrating information from multiple perspectives with the aim of providing a comprehensive view of the scientific landscape in our research field. To achieve this, we employed the Three-fields plot from Bibliometrix. In Figure 22, we generated a Three-fields plot (countries, authors, journals). The first part of the plot illustrates how scientific contributions are distributed based on the countries of origin. This allows us to observe whether certain countries dominate in a specific field or if there is a balanced distribution. The second part of the plot focuses on the authors involved in research, providing insights into collaborations among authors and their influence in a given field. The third section highlights the distribution of results across scientific journals, potentially revealing the top journals where most works in the analyzed field are published. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Word** & **Dim.1** & **Dim.2** & **Cluster** \\ \hline Simulation & \(-0.37\) & \(-0.38\) & \(1\) \\ Model & \(-0.15\) & \(0.09\) & \(1\) \\ Systems & \(0.25\) & \(-1.09\) & \(1\) \\ Behavior & \(0.72\) & \(-0.22\) & \(1\) \\ Framework & \(-0.34\) & \(-0.94\) & \(1\) \\ dynamics & \(-0.26\) & \(0.49\) & \(1\) \\ System & \(-0.33\) & \(-0.56\) & \(1\) \\ Networks & \(1.68\) & \(0.19\) & \(1\) \\ Optimization & \(-0.37\) & \(-0.21\) & \(1\) \\ Evolution & \(-0.57\) & \(0.86\) & \(1\) \\ Algorithm & \(1.34\) & \(0.78\) & \(1\) \\ Risk assessment & \(0.52\) & \(0.29\) & \(1\) \\ Artificial Intelligence & \(0.11\) & \(-0.79\) & \(1\) \\ Machine Learning & \(0.46\) & \(0.72\) & \(1\) \\ Deep Learning & \(-0.28\) & \(1.58\) & \(1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Factorial Analysis of 15 words by Cluster. Figure 22: Three-fields plot countries (**left**), authors (**middle**), journals (**right**). Additionally, in Figure 23, a Three-fields plot (affiliations, authors, keywords) was generated. The first section illustrates how authors' affiliations are distributed in the scientific space, allowing us to observe if certain institutions dominate in specific fields. The second part of the plot focuses on the authors themselves, providing information on how they are connected based on their affiliations. The last section highlights the keywords associated with the research field, offering insights into emerging research trends and directions. VOSviewer is a powerful tool for visualizing networks in bibliometric data [78; 79]. From the analysis conducted in Figure 24, we can observe if there are certain patterns, connections, and relevant trends associated with the specific research field. Each color highlighted in Figure 24 represents a cluster, having their explanation in Table 11. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Cluster** & **Color** & **Label** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Red} & Agent, algorithm, machine, prediction, population, ability, effect, state, parameter, decision, impact, insight, goal, feature, interest, addition, individual, case study, case. \\  & & Artificial intelligence, challenge, modeling, knowledge, development, implementation, article, example, field, information, issue, need, point, problem, research, tool \\ \cline{1-1}  & & \\ \cline{1-1}  & & Complexity, ABM, ABMs, performance, number \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Explanation of the clusters of co-occurrences depicted in Figure 24.  
Figure 23: Three-fields plot affiliations (**left**), authors (**middle**), keywords (**right**). In Table 11, the formed clusters are described. Regarding Cluster 1 (red), among the representative keywords, we find \"agent\", \"algorithm\", \"machine\", \"prediction\", \"decision\", \"parameter\", \"effect\", and \"case study\". Thus, this cluster highlights specific aspects of ABM, such as algorithms, agent abilities, and the effects and impact of predictions on the population or individuals.