## 3 Dataset Analysis
### Analysis of Literature  
Regarding the state of knowledge in the field, the following analyses provide an overview of the scientific landscape and previous contributions in the analyzed domain. In the context of our research, the current state of knowledge indicates a dynamic intersection between two evolving domains: AI and ABM. This exploration reflects a contemporary interest in leveraging advanced AI techniques within ABM, opening up potential new directions for modeling and simulation across various fields. This convergence marks a frontier where traditional ABM methodologies meet advanced AI technologies, promising a more realistic, adaptable model with enhanced predictive capabilities. The synergy between AI and ABM holds the potential to address complex systems with greater fidelity, enabling researchers to simulate and understand intricate real-world scenarios with increased precision. As researchers delve into this interdisciplinary realm, challenges and opportunities emerge. Original contributions in this direction may involve refining existing AI algorithms for ABM contexts, adapting ABM paradigms to harness the learning capabilities of AI, and identifying novel applications that can benefit from this amalgamation. For instance, [PERSON] et al. [PERSON] et al. (2018) address the challenges associated with explaining the structure and performance of agent-based models (ABM) in the quantitative social sciences. The authors propose an innovative approach that utilizes AI for both constructing models from data and enhancing the way we communicate these models to stakeholders. While machine learning is actively employed for data preprocessing, this study introduces, for the first time, its use to facilitate the direct development of a simulation model from data. The proposed framework, ML-ABM, is designed to capture causality and feedback loops in complex and nonlinear systems while maintaining transparency for stakeholders ([PERSON] et al., 2018). The authors argue that their approach not only leads to the creation of a behavioral ABM but also unveils the internal workings of empirical models, traditionally considered \"black boxes\". They suggest that integrating AI into simulation practices can bring a new dimension to modeling and provide valuable insights for future applications.  
[PERSON] et al. (2018) explore the integration of Machine Learning (ML) techniques into ABM to enhance the design and analysis of models. The authors propose an extension of the Overview, Design Concepts, and Details (ODD) protocol to standardize the description of ML applications within ABM. The extension categorizes the use of ML based on various factors, facilitating transparent communication of ML workflows in ABM. The proposed approach is exemplified through a tax evasion model, highlighting improved precision with statistical significance.  
Figure 18: Top 50 authors’ collaboration networks.  
On the other hand, the role of ABM can be an innovative approach in various fields. For instance, in transportation studies, it can provide an alternative to traditional equation-based models. [PERSON] and [PERSON] (2018) highlight that specific applications of ABM in transportation, including the aviation, maritime, road, and rail sectors, involve the study of airport operations, maritime efficiency, traffic congestion, evacuation scenarios, and public transportation systems.