The World of Agent‐Based Modeling: A Bibliometric and Analytical Exploration

The primary objective of this research paper is to conduct a bibliometric analysis of the prevailing research landscape pertaining to agent‐based modeling (ABM). This analysis encompasses an examination of key contributors, affiliated academic institutions, influential publications, and prominent journals within the domain. To achieve this, a dataset consisting of 11,477 scholarly papers retrieved from the ISI Web of Science database has been curated, using keywords specifically related to ABM, spanning the period from 1996 to 2024. Employing n ‐gram analysis techniques on titles, keywords, abstracts, and keyword‐plus fields has unearthed a multitude of domains wherein ABM has been applied with notable success. Our findings, as delineated in this paper, underscore a sustained and robust growth in scholarly interest in the realm of ABM during the specified temporal span, characterized by an impressive annual growth rate of 25.29%. Furthermore, our study contributes to the identification and analysis of salient keywords and emerging trends, thereby elucidating key research trajectories within this domain. The identification of collaborative networks among authors, their respective academic affiliations, and the geographical distribution across various countries and territories offers valuable insights into the global proliferation of ABM as a research methodology. The findings offer valuable insights into the widespread applications of ABM across various domains, including climate change, social networks, supply chain dynamics, public health studies, financial market analysis, and population dynamics. The results of the study can help in guiding future research and practical applications of ABM in these and other multifaceted areas.

Authors

Publication Details

Journal
Complexity
Published
2025-01-01
DOI
https://doi.org/10.1155/cplx/2636704
Citations
2
Primary Topic
Complex Systems and Time Series Analysis
Type
article
Field-Weighted Citation Impact
6.26
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article

The World of Agent‐Based Modeling: A Bibliometric and Analytical Exploration

Camelia Delcea, Liviu‐Adrian Cotfas, Bianca Cibu, Adrian Domenteanu
2 citations
Complexity
Complex Systems and Time Series Analysis
6.26
article

The World of Agent‐Based Modeling: A Bibliometric and Analytical Exploration

Camelia Delcea, Liviu‐Adrian Cotfas, Bianca Cibu, Adrian Domenteanu
article en
2 citations

Abstract

The primary objective of this research paper is to conduct a bibliometric analysis of the prevailing research landscape pertaining to agent‐based modeling (ABM). This analysis encompasses an examination of key contributors, affiliated academic institutions, influential publications, and prominent journals within the domain. To achieve this, a dataset consisting of 11,477 scholarly papers retrieved from the ISI Web of Science database has been curated, using keywords specifically related to ABM, spanning the period from 1996 to 2024. Employing n ‐gram analysis techniques on titles, keywords, abstracts, and keyword‐plus fields has unearthed a multitude of domains wherein ABM has been applied with notable success. Our findings, as delineated in this paper, underscore a sustained and robust growth in scholarly interest in the realm of ABM during the specified temporal span, characterized by an impressive annual growth rate of 25.29%. Furthermore, our study contributes to the identification and analysis of salient keywords and emerging trends, thereby elucidating key research trajectories within this domain. The identification of collaborative networks among authors, their respective academic affiliations, and the geographical distribution across various countries and territories offers valuable insights into the global proliferation of ABM as a research methodology. The findings offer valuable insights into the widespread applications of ABM across various domains, including climate change, social networks, supply chain dynamics, public health studies, financial market analysis, and population dynamics. The results of the study can help in guiding future research and practical applications of ABM in these and other multifaceted areas.

ComplexityVol. 2025(1)
Openalex Percentile: Top 4%
Complex Systems and Time Series Analysis
6.26
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