Communication plays a pivotal role in social phenomena such as belief polarization, scientific inquiry, and collective problem-solving. Agent-Based Models (ABMs) are computational tools that simulate the emergence of macro-level phenomena from micro-level interactions among agents. This paper focuses on Argumentative Agent-Based Models (AABMs), a specialized subset of ABMs that study argumentative communication, where agents provide reasons to support or counter opinions. We present a systematic overview of AABMs, detailing their design, methodologies, and applications across disciplines. Key research questions include understanding the dynamics of consensus versus polarization, the conditions for epistemic reliability in collective decision-making, and the mechanisms that foster efficient collaboration within diverse groups through argumentative exchanges. By synthesizing contributions from computer science, social science, and philosophy, this paper serves as both an entry point for newcomers and a comprehensive resource for researchers advancing the study of AABMs.
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