Justifying multiply sectioned Bayesian networks

We consider multiple agents whose task is to determine the true state of an uncertain domain so they can act properly. If each agent only has partial knowledge about the domain and local observation, how can agents accomplish the task with the least amount of communication? Multiply sectioned Bayesian networks (MSBNs) provide an effective and exact framework for such a task but also impose a set of constraints. The most notable is the hypertree agent organization which prevents an agent from communicating directly with arbitrarily another agent. Are there simpler frameworks with the same performance but with less restrictions? We identify a small set of high level choices which logically imply the key representational choices made in MSBNs. The result addresses concerns regarding the necessity of restrictions of the framework. It facilitates comparison with related frameworks and provides guidance to extension of the framework as to what can or cannot be traded off.