mshih's aa1942calc2

I’ve also been working on a calculator based on math.

Here’s a link: https://mshih01.github.io/aacalc2/

It has most of the features of aa1942calc.com… multiwave support, rounds of battle, amphibious assault. (the missing feature is the monte carlo analysis to support extra large battles )

It also has advanced retreat options…
number of rounds.
number of attacking units <=
expected profit continuing < threshold
probability of winning < threshold
probability of losing air > threshold.
probability of killing defenders > threshold (strafe analysis).

The territory can also be marked as deadzone in the profit computation – in which case attacking captured land units count inf(-2), art(-3), arm(-4.5) towards the profit computation.

I think it’s even faster than your implementation. If you like, we can discuss implementation details.

At the time of my post. So last couple days. For example. Try 80 inf/art vs. 80 inf/art (160 vs. 160). My calculator runs in 0.2 seconds. I think yours currently crashes. But if you reduce to 120 vs. 120 (60 each), then yours takes 10+ seconds.
My calculator can handle 400 vs 400 (200 inf/art vs. 200 inf/art) in 10 seconds.

You can check out my repo on github: https://github.com/mshih01/aacalc2

The readme has a discussion on the algorithm… and it should be pretty fast – and memory efficient.

• Mitchell

@djensen If you’re using typescript/javascript, my engine is available on npm.

npm install aacalc2

The webpage is basically something I vibe coded to showcase the engine.

@djensen Sure. Even if you don’t directly use the engine., maybe you can take advantage of some of the techniques?

I think the speed of my engine is due to:

1. Precompute everything up front
   For each node (unique sub-army)
   get any needed probability with an array lookup
   get the next state index for any casualty type with an array lookup.
   I don’t use any hash/maps in the tight inner loop. Only for postprocessing the output, or preprocessing the input.
2. Pruning. I use a pruning threshold 1e-12. If a state’s seed probability is smaller than this threshold – I prune to zero – and don’t update child states.
3. Roundless evaluation. I don’t roll multiple rounds of battle. The 2-D probability matrix is solved only one time.

@djensen Aside from the pure speed. My calculator has some neat advanced features like:

1. Compute the probability of winning for all sub-battles in one pass. This allows a second pass to implement things like retreat if the probability of winning the battle is too low.

2. Compute the Expected profit for all sub-battles in one pass. This allows a second pass to implement retreat if the EV profit is too low (typically retreat if EV is negative).

3. Strafe analysis is_deadzone, combined with the territory value can be used to model a variety of strafe scenarios.

e.g. If strafing from a capital with a large army next door… a large negative value is warranted. (russia IPC’s * 2 + russia territory IPC value. + value of other allied units lost if the strafe turns into take. Using large values like -50 IPC’s is fair.

If strafing from W.Russia… and W Russia is safe even if the strafe fails and the territory is captured, a normal positive territory value is good enough.

4. Army recommendation - which can compute optimal strafes. If you set is_deadzone – and an appropriate territory value (positive or negative based on the situation), the army recommendation can recommend the sub-army that gives maximum expected profit.

#1 and #2 are simple recurrence relations that I can share