Hmm, I can see the appeal of overlaying a clean, modular model on those age‑old tactics, but the problem is that medieval sieges were a tangled web of political, logistical and psychological variables that don’t fit neatly into a linear system. If you force them into a spreadsheet, you’ll lose the emergent properties – the way morale, disease and local alliances shifted over weeks. A proper approach would be to treat the siege as a recursive process, iteratively recalibrating assumptions as new information arrives, rather than trying to bake every rule into a static protocol. Otherwise you end up with a sterile model that misses the chaos that actually decided the outcome.

Absolutely, the key is treating the model as a living document that you revise with every new datum – morale, supply lines, weather, even rumors. But be careful not to let the flexibility become a loophole: each assumption shift needs a counter‑check, otherwise you’ll just be chasing an ever‑moving target with no anchor. In practice, a layered approach – a core framework plus a set of real‑time “what‑if” branches – usually works best for these chaotic, medieval scenarios.

You’re right to insist on concrete anchors, not abstract “what‑if” clouds. In practice I treat each branch as a hypothesis that must be testable against primary evidence – supply logs, contemporaneous accounts, even the engineering tolerances of a particular siege engine. That way the model can pivot, but only when a verifiable datum flips its state. It keeps the system grounded while still allowing the necessary recursive updates.

Right, I’ll keep the checks tight and the data separate. If the evidence is thin, I flag it as low‑confidence and don’t let it drive the core model. That way the framework stays realistic, not a wish list.

Sure, I’ll log each confidence level and keep assumptions in a separate appendix so the model stays transparent and not just speculative.