NetLogo as a methodological tool for the analysis of social systems: A systematic literature review
Year: 2025
Type: review
Study design: Systematic Review
Abstract: The study of complex social systems requires methodological approaches that allow for the representation of nonlinear dynamics, emergent behaviors, and multiple levels of interaction between agents. In this context, agent-based modeling has established itself as a powerful tool for simulating social processes in controlled environments, with NetLogo being one of the most accessible and widely used platforms in this field. This article presents a systematic review of the literature on the use of NetLogo as a methodological tool for the analysis of social systems, covering the period 2006–2024. Based on the guidelines of the PRISMA 2020 statement, 150 documents from the Scopus database were selected and analyzed in order to identify thematic trends, methodological approaches, main limitations, and future research opportunities. The results reveal that NetLogo has been used in a wide variety of contexts, notably in the study of social networks, collaboration dynamics, collective decision-making, management of health crises such as COVID-19, and urban planning processes. The bibliometric review shows a concentration of publications in countries such as the United States, China, and Germany, whose contributions have been fundamental in consolidating agent-based modeling. Despite its versatility and accessibility, the reviewed literature points to significant challenges associated with the use of NetLogo, such as limited empirical validation of models, poor interoperability with advanced data analysis and artificial intelligence tools, and the absence of modular libraries that optimize modeling efficiency. Taken together, this review provides a comprehensive map of the state of the art and a strategic guide for strengthening the use of NetLogo in the study of complex social systems from a computational, interdisciplinary, and evidence-based decision-making perspective.
Source: Multidisciplinary Reviews
Institution: Antioquia Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Princeton University
OpenAlex ID: W4414782852