Time series modeling of Army Mission Command communication networks: an event-driven analysis

Laura R. Marusich () and Norbou Buchler
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Laura R. Marusich: U.S. Army Research Laboratory
Norbou Buchler: U.S. Army Research Laboratory

Computational and Mathematical Organization Theory, 2016, vol. 22, issue 4, No 4, 467-486

Abstract: Abstract We examine the communication time series of a fully-networked Army coalition command and control organization. The network comprised two echelons of command, at the Division and Brigade levels, over a 2-week military scenario exercise involving a Mission Command staff communicating over email and phone. We used time series analysis to predict the communications record based on an external work variable of the number of important scenario events occurring across time. After taking into account structural features of the time series—decreasing communications over time, a network crash, and the transition between weeks—we examined the remaining variability in email and phone communication. We found that the exercise scenario events were not a significant predictor of the Divisional communications, which were best fit by an auto-regressive model of order 1, meaning that the best predictor of the volume of communications at a given time point was the volume of communications on the immediately preceding time point. The occurrence of scenario events, however, did predict the Brigade communication time series, which were well fit by a lag dependent variable model. These results demonstrate that Brigade communications responded to and could be predicted by battlefield events, whereas the Division communications were only predicted by their own past values. These results highlight the importance of modeling environmental work events to predict organizational communication time series and suggest that network communications are perhaps increasingly dependent upon battlefield events for lower echelons of command closer to the tactical edge.

Keywords: Time series analysis; Communication network; Event-driven analysis; Mission Command; Dynamic networks; Social network analysis (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10588-016-9211-7

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