Search

In this paper, we report results from a large-scale military experiment with Canadian Forces (CF) officers on the impact of collaborative work support systems—in the present experiment, a set of integrated tools such as operations planning systems, joint fire systems and other logistics tools—on individual and shared situation awareness (SA). In order to measure SA and the ability to share SA, we used the Quantitative Assessment of Situation Awareness (QUASA) technique: We first analyzed SA quality (sensitivity, response bias, accuracy) and metacognition (level of confidence, and calibration bias), and then computed the level of response concordance within and across groups (three different Operational Commands of the CF). The addition of the new support system led to a significant improvement in shared SA. However, this beneficial effect comes with a drop in both objective and perceived individual SA. From this pattern of results, we conclude that there might be a tradeoff between SA sharedness and quality. Supporting the process of sharing SA through enhanced means of information integration and exchange, communication and coordination can also lead to a considerable decrease in individual SA and meta-SA.

Problematic command and control situations are often characterized with high levels of uncertainty and severe time constraints. In time compressed situations where only a limited number of information can be considered by the decision-maker, it becomes critical that the information available is the most relevant for taking action in that situation. This paper presents a Framework for the Analysis of Information Relevance (FAIR). The objective with this stepwise framework is to guide filtering and prioritization of information based on its level of relevance to the situation. The rationale is that with adequate filtering and prioritization processes, the decision-maker is left with only the most relevant information to act within a time-constrainted window of opportinity.

In this paper, we use the Cognitive Observe-Orient-Decide-Act (C-OODA) model in a Civil-Military Cooperation (CIMIC) analysis. CIMIC requires intelligent decision making over many activities, variables, and effects. We utilize the Complex Decision Making Experimental Platform (CODEM) from Lafond, DuCharme, and Rioux to provide situation observation, environmental orientation, relational decision making, and action selection, evaluation, and feedback. With the development of complex CIMIC activities, users require effects-based analysis of all civil and military actions for contextual reasoning and situation understanding. For pragmatic information CIMIC system design and analysis, the user (commander or operator/analyst) needs timely and accurate information to conduct proactive actionable intelligence over complex situations. In this paper, we use the C-OODA model in a CIMIC analysis using the CODEM simulation to support the modeling of stability and sustainment operations.