Governments usually conduct experiments before coming up with new methods of harnessing military activities. These experiments involve real life scenarios and collection of statistical data. Learning experiments can be conducted using various theories including constructivist and learning theories.
Recently, the Canadian Defense and Research Authority used the results of a statistical study to figure out the best way to conduct tactical operations (Grant, 2012). The study took six days and involved nineteen military officers.
The results of the study were used to indentify skills that are important to military personnel. Moreover, the military was able to learn new ways to use the newly developed command and control software program. This paper will explore how the statistical results of this study were used to impact changes to Canada’s military system.
Military command organizations also referred to as C2 units are in charge of providing directions to teams that have been deployed to the field. The success of a C2 organization is vital to the success of military operations. The operations system of command units keeps changing in line with technology and security requirements.
This is why it is necessary for changes in the functionality of a C2 unit to be communicated fast. For instance, as the war in Iraq progressed, NATO command units kept updating their modes of operations. The purpose of this experiment was to transmit knowledge and to generate new useful information (Grant, 2012).
This study was initially commissioned by the Canadian Department of National Defense. The study was specifically aimed at enhancing the fire support team during military operations.
The experiment was conducted with the aim of refining the then ongoing Afghanistan campaign. Nineteen military officers of different ranks took part in the experiment. The experiment was code-named the Coalition Attack Guidance Experiment (CAGE).
The results of CAGE were employed in several military operations. The first changes in military operations as a result of this experiment involved unscheduled missions. Unscheduled missions refer to events that occur haphazardly such as ambushes or evacuation of casualties. The results of this experiment are now used in unscheduled missions that require fire support.
The results of the experiments revealed that a longer chain of command resulted in more failures during unscheduled missions (Grant, 2012). In addition, unscheduled military missions in future would be recorded in single entries. The experiment had revealed that making several entries regarding a single mission was ineffective. In addition, it increased the chances for errors and the workload in unscheduled missions.
Through the experiment, the Canadian Defense Authority was able to adapt a “Joint Automated Deep Operations Coordination System (JADOCS)”. From this experiment, the military learnt a new way of logging information using a color-coded system. This system is used to complement the pre-existing voice system.
The method is very effective in scenarios requiring rapid response. The results of this experiment are also used during battlefield briefing tasks. The graphs derived from the experiment revealed the best approach to a mission briefing. The mode of conducting a briefing is dependent on factors like priorities and enemy positions.
Psychological research has often been used to come up with better ways of handling everyday scenarios (King, Rosopa & Minium, 2011). In this case, an experiment conducted using the constructivist theory was used to come up with better ways of handling a military command system.
Not all of the results in this experiment were helpful to the improvement of the military command system. However, the experiment offered some results that were of help to Canada’s defense force. The above demonstration is an example of an effective way to employ psychological experiments in formulating public policies.
References
Grant, C. (2012). A constructive approach to organizational learning in a tactical operations centre. PsychNology Journal, 10(1), 7 – 22.
King, M., Rosopa, J. & Minium, W. (2011). Statistical Reasoning in the Behavioral Sciences. Hoboken, NJ: John Wiley & Sons.