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Applying the FINC (Force, Intelligence, Networking and C2) methodology to the Land Environment

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Abstract

In this paper we re-examine the FINC (Force, Intelligence, Networking and C2) methodology for analysing C4ISR architectures, studying its applicability to hierarchical organisational structures in the Land environment. For this study we utilise a search-and-manoeuvre experimental scenario, implemented using an agent-based simulation written in Java. The FINC methodology allows the calculation of three metrics or coefficients for every C4ISR architecture: the information flow coefficient, the coordination coefficient, and the intelligence coefficient. Our experiment shows that the FINC intelligence coefficient alone was able to predict 95% of the variance in performance. Consequently, the intelligence coefficient can be used to compare C4ISR architectures, and predict with moderate accuracy which one will give the best performance. A brief study of some US Civil War battles confirms the usefulness of the intelligence coefficient.

Executive Summary

In responding to the Revolution in Military Affairs and rapid change in the modem strategic environment, it is important to utilise the best possible C4ISR architectures for the Australian Defence Force. Consequently, it is extremely important to evaluate the effectiveness of different C4ISR architectures. This can be done using the regular series of military exercises. However, these are not capable of examining the impact of technologies not yet in service. Wargaming is capable of examining such technologies, but both wargaming and real exercises have a substantial cost, and therefore there is considerable benefit in a low-cost methodology for evaluating C4ISR architectures, and selecting for further experimentation those that the methodology identifies as the best candidates. The FINC (Force, Intelligence, Networking and C2) methodology satisfies this goal. The FINC methodology allows the calculation of three metrics for every C4ISR architecture: the information flow coefficient measuring tempo superiority, the coordination coefficient measuring coordination superiority, and the intelligence coefficient measuring information superiority. Like all methodologies, the FINC methodology requires validation, and this report describes the second step in validating it, with specific emphasis on the Land military environment. For this study we utilised a search-and-manoeuvre experimental scenario, implemented using an agent-based simulation written in Java. Our simulation approach is complementary to agent-based "distillations" such as Project Albert, concentrating more heavily on C4ISR architectures and organisational structures. In our previous study (DSTO-GD-O313), we demonstrated the usefulness of the FINC methodology, and in this paper we build on that work by specifically addressing some issues relating to land operations: • Land forces traditionally employ a hierarchical organisation. This is because land operations usually involve problems which are too complex for centralised optimisation, and so benefit from being hierarchically subdivided. • More complex problems require a hierarchical decomposition, just as larger organisations require a hierarchical structure; centralised architectures are less appropriate. • Land operations are often of longer duration, and involve conditions changing over time and thus require adjustments to centralised planning. • Network capability often varies as conditions change and units move, with consequent variation in performance. In this second simulation study, we have addressed these issues within a scenario involving a larger number of units and a hierarchical command structure. We also specifically addressed variation in performance with varying network capability. Figure (i): Example Micro-World with Five Targets and Three Randomly Placed Walls (See Fig. (1) in Executive Summary in report. The scenario involved manoeuvring a brigade consisting of 16 companies through a micro-world such as that shown in Figure (i), in order to reach an end-state where own forces are positioned at five randomly-positioned targets. The experiment tested three plaIU1ing strategies, seven levels of communication delay, and four C4ISR architectures. The architectures tested were Command Hierarchy (a simple hierarchy); Situation Awareness Hierarchy (situation awareness information is passed up and down the hierarchy); Situation Awareness Networking (situation awareness information is passed sideways via a simulated radio network); and Command Networking (some units can bypass the command hierarchy, issuing orders from below). Best performance was obtained with the Situation Awareness Networking architecture, where situation awareness information was passed sideways, but C2 was handled hierarchically. Statistical analysis showed that one of the FINC metrics, the intelligence coefficient, was capable of integrating information about architectural differences, quality of intelligence, and communication delays. Consequently, the intelligence coefficient was able to predict 95% of the variance in performance, where performance was calculated based on the time taken to locate targets and manoeuvre own forces towards them. The close fit of the data points to the red line in Figure (ii) illustrates the quality of the prediction: Statistical analysis showed that one of the FINC metrics, the intelligence coefficient, was capable of integrating information about architectural differences, quality of intelligence, and communication delays. Consequently, the intelligence coefficient was able to predict 95% of the variance in performance, where performance was calculated based on the time taken to locate targets and manoeuvre own forces towards them. The close fit of the data points to the red line in Figure (ii) illustrates the quality of the prediction: Figure (ii): Prediction of Performance by Intelligence Coefficient for Micro- World Experiment (See Fig. (ii) in Executive Summary in report) A brief survey of twelve real-world battles from the first two and a half years of the US Civil War provided tentative confirmation that the intelligence coefficient is also useful in predicting real-world performance. These battles demonstrated wide variation in the ability to collect good intelligence, process it quickly, and transform it into unambiguous orders rapidly disseminated to subordinates. This is the quality which the intelligence coefficient is intended to measure. In the US Civil War, both sides shared similar culture, technology, and tactics, which reduces (but does not eliminate) the impact of other variables on the outcome of the battles. The US Civil War was also very well-documented. These factors make it a useful test of the applicability of the intelligence coefficient in the real world. The moderate fit of the data points to the red line in Figure (iii) demonstrates the ability of the intelligence coefficient to approximately predict relative casualty rates. Moving from left to right, Union casualties generally decrease (relative to Confederate casualties) as the Union intelligence coefficient increases (relative to the Confederate coefficient). The first outlying point is the Battle of Fredericksburg, where the incompetence of General Ambrose Burnside (which went well beyond his failure to process information) led to Union casualties being more than double those of the Confederacy. The second outlying point is the Battle ,of Chickamauga, where the Confederacy had higher casualties in spite of handling information approximately as well as Union forces. Figure (iii): Prediction of Casualty Rates by Intelligence Coefficient for 12 Civil War Battles (See Fig. (iii) in Executive Summary in report) The combination of in-silica experiment and historical study presented in this paper provides preliminary confirmation of the usefulness of the FINC intelligent coefficient in comparing C4ISR architectures, particularly in the Land environment. This preliminary confirmation justifies more detailed future studies to refine and improve the FINC methodology. Future work will also use the FINC methodology to assess joint and coalition architectures.

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