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Experimental analysis of networks of cooperative learning agents (to verify certain properties such as the system's stability) has been commonly used due to the complexity of theoretical analysis in such cases. Due to the large number of parameters to analyze, researchers used metrics that summarize the system in few parameters. Since in cooperative system the ultimate goal is to optimize some global metric, researchers typically analyzed the evolution of the global performance metric over time to verify system properties. For example, if the global metric improves and eventually stabilizes, it is considered a reasonable verification of the system's stability.  The global performance metric, however, overlooks an important aspect of the system: the network structure. We show an experimental case study where the convergence of the global performance metric is deceiving, hiding an underlying instability in the system that later leads to a significant drop in performance. To expose such instability, we propose the use of the graph analysis methodology, where the network structure is summarized using some network measures. We develop a new network measure that summarizes an agent's interaction with its neighbors and takes the disparity of these interactions into account. The new measure is applied to our case study, clearly exposing the instability that was previously hidden by the global performance metric.

Using graph analysis to study networks of adaptive agent