Modeling interactions within groups poses an increasingly significant challenge in the study of social systems, particularly when behaviors and decisions emerge from complex, dynamic, and decentralized interactions. Agent-based modeling (ABM) and simulation offers a robust methodological framework for analyzing these dynamics, as it enables the representation of individual heterogeneity, social influence, adaptive mechanisms, and network structures within simulated environments. This article presents a comprehensive and systematic review of 24 studies that employ agent-based models to simulate group behavior across a range of contexts. The findings underscore the flexibility of this approach in addressing domains such as cooperation, social learning, collective decision-making, and human–computer interaction. Particular attention is given to the design of collaborative software systems, where simulation provides valuable insights into coordination patterns, shared rules, and the emergent performance of social groups. Finally, the review identifies current limitations such as excessive model simplifications and insufficient empirical validation and outlines future research directions, including the integration of sociocultural variables, greater agent heterogeneity, and more rigorous validation procedures. This systematic review contributes to a deeper understanding of how groups can be modeled and analyzed in terms of their structure, behavior, and interactions.
Sandria et al. (Mon,) studied this question.