Natural-Language Multi-Agent Simulations of Argumentative Opinion Dynamics

TL;DR

This paper introduces a natural-language agent-based model of argumentation that simulates collective deliberation, revealing how active contribution generation influences opinion dynamics and polarization.

Contribution

It presents a novel natural-language ABMA using neural language models to simulate argumentation and tests the impact of active contribution on deliberation dynamics.

Findings

Passive agents exhibit polarization due to confirmation bias and homophily.

Active contribution generation shifts the conversation dynamics based on agent properties.

The model highlights the importance of argument creation in collective deliberation.

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 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.