Two decades of agent-based modeling in marketing: a bibliometric analysis

Romero, Elena de Luis; Chica, Manuel; Damas, Sergio; Rand, William

doi:10.1007/s13748-023-00303-y

Cited by 1 publication

(1 citation statement)

References 140 publications

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“…AI, as a field of study, offers machine learning algorithms that can be used in managing and analyzing large volumes of data and information, complementing and seamlessly integrating with the methods and frameworks provided by cybernetics. Integrating ABM with AI provides new perspectives for study and applicability in various fields such as sustainable agriculture [21], marketing [22], education [23], biomedical systems [24], agent behavior [25], management [26], etc.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Use of Artificial Intelligence in Agent-Based Modeling Applications: A Bibliometric Study

Ionescu,

Delcea,

Chiriță

et al. 2024

Algorithms

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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.

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