  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 formal models). This paper uses the natural-language
ABMA to test the robustness of formal reason-balancing models of argumentation
[Maes & Flache 2013, Singer et al. 2019]: First of all, as long as ADAs remain
passive, confirmation bias and homophily updating trigger polarization, which
is consistent with results from formal models. However, once ADAs start to
actively generate new contributions, the evolution of a conservation is
dominated by properties of the agents *as authors*. This suggests that the
creation of new arguments, reasons, and claims critically affects a
conversation and is of pivotal importance for understanding the dynamics of
collective deliberation. The paper closes by pointing out further fruitful
applications of the model and challenges for future research.
