  In decentralised autonomous systems it is the interactions between individual
agents which govern the collective behaviours of the system. These local-level
interactions are themselves often governed by an underlying network structure.
These networks are particularly important for collective learning and
decision-making whereby agents must gather evidence from their environment and
propagate this information to other agents in the system. Models for collective
behaviours may often rely upon the assumption of total connectivity between
agents to provide effective information sharing within the system, but this
assumption may be ill-advised. In this paper we investigate the impact that the
underlying network has on performance in the context of collective learning.
Through simulations we study small-world networks with varying levels of
connectivity and randomness and conclude that totally-connected networks result
in higher average error when compared to networks with less connectivity.
Furthermore, we show that networks of high regularity outperform networks with
increasing levels of random connectivity.

Multiagent Systems;Artificial Intelligence