Natural-Language Multi-Agent Simulations of Argumentative Opinion Dynamics
Gregor Betz
Abstract
This paper develops a natural-language agent-based model of argumentation (ABMA). Its artificial deliberative agents (ADAs) are constructed with the help of so-called neural language models recently developed in AI and computational linguistics. ADAs are equipped with a minimalist belief system and may generate and submit novel contributions to a conversation. The natural-language ABMA allows us to simulate collective deliberation in English, i.e. with arguments, reasons, and claims themselves-rather than with their mathematical representations (as in symbolic models). This paper uses the natural-language ABMA to test the robustness of symbolic reason-balancing models of argumentation (Ms & Flache ; Singer et al.
Topics & Concepts
ArgumentativeComputer scienceDeliberationConversationArgumentation theoryNatural languageNatural (archaeology)Artificial intelligenceComputational linguisticsNatural language processingLinguisticsPolitical scienceLawPoliticsArchaeologyHistoryPhilosophyOpinion Dynamics and Social InfluenceTopic ModelingMulti-Agent Systems and Negotiation