Large networks of human and machine systems have staggeringly complex properties which make them difficult to analyze. This resistance to characterization is due to the fact that the number of possible interactions between the system nodes is exponential in the number of nodes. This combinatorial complexity makes such systems resistant to both formal analysis and empirical exploration. The goal of this work is to analyze a particular complex system, a system of agents that fuse information to develop shared beliefs. Primarily we seek to understand the detrimental emergent effects that might result in convergence to an incorrect belief, due to the large scale interactions of individuals. We achieve this through a two stage approach that combines the formal analysis of an abstracted version of the system with direct simulation of the system itself. The analysis of the abstraction of the system gives us a qualitative description of the system state space which can be used to guide and limit the parameter ranges over which the empirical evaluation is conducted. The particular abstraction that we use is to develop a mean field description of the system. Specifically, we assume that the influence of the remainder of the system on an individual can be replaced, for analysis purposes, with a system wide average influence. In our information propagation and fusion model, the team is connected via a network with some team members having access to sensors and others relying solely on neighbors in the network to inform their beliefs. Each agent uses Bayesian reasoning to maintain a belief about a single fact which can be true, false or unknown. Through our analysis we found that for certain parameter values, the system can converge to an incorrect belief despite primarily accurate information due to emergent effects.
['Robin Glinton', 'Paul Scerri', 'Katia P. Sycara']
Towards the understanding of information dynamics in large scale networked systems