  It has been shown that one can accommodate data (Bayes) and constraints
(MaxEnt) in one method, the method of Maximum (relative) Entropy (ME) (Giffin
2007). In this paper we show a complex agent based example of inference with
two different forms of information; moments and data. In this example, several
agents each receive partial information about a system in the form of data. In
addition, each agent agrees or is informed that there are certain global
constraints on the system that are always true. The agents are then asked to
make inferences about the entire system. The system becomes more complex as we
add agents and allow them to share information. This system can have a
geometrical form, such as a crystal structure. The shape may dictate how the
agents are able to share information, such as sharing with nearest neighbors.
This method can be used to model many systems where the agents or cells have
local or partial information but must adhere to some global rules. This could
also illustrate how the agents evolve and could illuminate emergent behavior of
the system.
