With larger battlespaces, command and control (C2) systems are more distributed. In particular, they have important multivariate, spatial, and temporal components that makes dealing with uncertainty in these settings particularly challenging. Moreover, (statistical) inference is required at diverse resolutions (or apertures) that are often different from those of the data. Statistical multi-resolution analysis and geostatistics (both empirical Bayesian approaches) are used to address the problem of decision making in C2. Dynamic statistical updating of the battlespace is an important aspect of the research, resulting in rapid-response maps of the battlespace that are accurate and easily interpretable for a battle commander.

Understanding and quantifying the behavioral psychology of enemy nations under conflict situations (or perceived conflict situations) can potentially lead to improved tactical counter-measures. A variety of important strategic questions can be answered that can provide extremely useful intelligence. For example, when threatened, does the enemy tend to retreat and defend, or aggressively counter-attack? Does the enemy attack in isolated pockets, or in a more uniform style? An example of interest is the situation where intelligence data is used to examine the positions and readiness-states of hostile mobile launcher systems. The location and potential threat of mobile launcher systems can vary significantly under different readiness states. By smoothing the spatial point pattern of mobile launchers (at a given snapshot in time), we obtain intensity maps that quantify the potential threat the launchers imply. Changes in the estimated intensity maps can be inferred using computer-intensive Monte Carlo testing methodology.

• Spatial Statistics for Command and Control at OSU.
• ONR Computational Decision Making Program.