JIOX points us to a recently released RAND study titled, “Analytic Support to Intelligence in Counterinsurgencies.” The monograph provides a thorough treatment of the current role of intelligence analysis in COIN; but more importantly, it also moves the discussion forward by proposing new ways for intelligence analysis to support the mission. Given the length of the report, I comment only on the key aspects of the report that stood out to me.
I was thoroughly impressed by the sections on the importance of data collection, and the authors’ notion of ‘precision versus accuracy’. They highlight the importance of good data collection as the foundation for good analysis, and in doing so, stress the importance of improving the significant activity (SIGACT) databases in both Iraq and Afghanistan through improved quality control, standardization and interoperability. They state:
Good analysis depends, in large part, on good data. In a counterinsurgency, analysis is designed to provide the commander with intelligence concerning the likely future behavior of the enemy. To do this, good data are critical. That said, the situation is not as bleak as one might expect based on the preceding discussion. Units in Iraq and Afghanistan have steadily improved their data collection—to include an increasingly rich set of friendly-force data made available through saved BFT [Blue Force Tracker] reports and records kept at the unit level.
I commend the authors for the completeness of their discussion on data quality related to the SIGACTs. For those who wish to understand the dynamics of the conflicts in Iraq and Afghanistan, the SIGACT databases will be the cornerstone of any analysis. What the authors have done is create a road map to improving their quality—an essential first step to creating relevant and timely analysis.
As a whole the report is extremely well written, and the authors display exceptional understanding of the tactical and strategic needs of COIN commanders and operators. As they begin to make suggestions; however, for new tools and technologies to improve operations the authors display weaker understanding of the scope and intent of these tool.
First, the assertion is made that ‘predictive tools’ are needed to inform commanders of the insurgents order of battle (OOB), and the authors construct a hypothetical tool dubbed the “Counterinsurgency Common Operational Picture” (COINCOP, pictured above), which combines a crowd sourced information database (i.e., a wiki), with network analysis tools layered on geospatial information. This system has two critical flaws that must be addressed. First, network analysis in not a predictive tool. Even the most advanced dynamic network analysts will be the first to tell you that at best SNA can inform you about the current structure of a network, but very little about where it is going. Add to that the immense uncertainty in adversarial network data sets, and the danger of making predictive tactical decision based on SNA becomes extremely problematic. Next, network data layered with geospatial data can be very difficult to analyze and interpret, and in that, can often mislead analysts. For example, current analytical techniques do not account for the meaning of distance between nodes, and simply displaying connections on a map may hide a networks true structure by fixing a node at some geolocation, rather than allowing the network structure itself to define how nodes are arranged.
Finally, the authors suggest that game theory may be a useful in helping coalition forces decide on their best actions given the status of the enemy. The authors note:
In a counterinsurgency, friendly forces (Blue) make many decisions when planning and executing missions. They choose routes, times, and speeds to travel, the spacing between vehicles in multi-vehicle convoys, and the configuration of various types of equipment (weapon systems) to be employed; the set of Blue strategies is in correspondence with the set of possible realizations of these choices. Insurgent elements (Red) make their own decisions about attacking Blue, choosing when and where to attack, which tactic to employ, and how to execute the attack;
the set of Red strategies is in correspondence with the set of possible answers to these questions. Although this discussion suggests that a given strategy is associated with a single mission, we note that a single strategy could correspond to multiple missions.
While game theory may in fact provide incredible insight into COIN operation, it will not as suggested here simply because of the level of complexity noted by the authors. As a technique, game theory is meant to reduce complex decisions into simply models, and by solving these “games” we better understand the underlying dynamics of these interactions. From what the authors suggest here, an analyst would have to simultaneously solve a near infinite number of equations for both blue and red forces—a task even the most brilliant mathematicians would not undertake. I suggest the authors reconsider the use of game theory, and present a simply solvable game that can be directly applied to COIN, rather than a grand general game with no real application.
Photo: RAND Corporation
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