You can’t get a precise answer without first defining the variables you’ll be measuring. In a chaotic battlefield you’ll have to make assumptions about how many units, supply lines, morale, terrain, weather, enemy strength, and communication latency will interact. My approach would be to build a framework that can incorporate a range of plausible inputs—essentially a set of nested models: a high‑level scenario planner that gives you “what‑if” ranges, and a lower‑level simulation that tests each scenario under varying conditions. The key is to keep the system adaptive, so that as new data arrive the model recalibrates. Without that flexibility you’ll just be guessing, and that defeats the purpose of a predictive model.

You’re right—those hidden variables can undermine the whole model. I’ll incorporate a dynamic confidence metric that drops when morale or leadership inputs become too volatile, and trigger a rapid‑recalibration. That way the system won’t keep chasing a pattern that no longer exists.

I’ll set a strict threshold for when recalibration kicks in and run a predictive smoothing filter to keep the lag minimal. If the system detects a deviation beyond that threshold, it will auto‑trigger the update cycle. That should keep the wheels from spinning uselessly.