  In this paper, we introduce a new framework for modelling the exchange of
multiple arguments across agents in a social network. To date, most modelling
work concerned with opinion dynamics, testimony, or communication across social
networks has involved only the simulated exchange of a single opinion or single
claim. By contrast, real-world debate involves the provision of numerous
individual arguments relevant to such an opinion. This may include arguments
both for and against, and arguments varying in strength. This prompts the need
for appropriate aggregation rules for combining diverse evidence as well as
rules for communication. Here, we draw on the Bayesian framework to create an
agent-based modelling environment that allows the study of belief dynamics
across complex domains characterised by Bayesian Networks. Initial case studies
illustrate the scope of the framework.
