Argumentative Agent-based Models

Communication plays a pivotal role in social phenomena such as belief polar- ization, 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 sup- port 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 ar- gumentative exchanges. By synthesizing contributions from computer science, social science, and philosophy, this paper serves as both an entry point for new- comers and a comprehensive resource for researchers advancing the study of AABMs. We are grateful to two anonymous reviewers for valuable feedback on an earlier draft of this entry. Many thanks also to Felix Kopecky and to Carlo Proietti for helpful comments.

Authors

• Louise Dupuis de Tarlé
• Matteo Michelini
• Dunja Šešelja
• Christian Straßer

Keywords

No keywords are indexed for this paper.