This is a zero-sum game with the same 16 strategies for each player. If player 1 does damage A1 (for attack), S1 times per (say) hour, and player 2 has armour that cancels D2 points from a successful attack and gives E2 (evade) percent change of dodging, the rate of damage by player 1 on player 2 is S1*(1-E2/100)*(A1-D2). Subtract from this the same expression with 1 and 2 swapped and that is the differential rate. Given the conditions of the problem, the only thing that matters about this rate is its sign. So there’s a 16*16 payoff matrix with entries 1, 0, and −1.
I’ve computed it, and it turns out that no strategy dominates any other: every row contains at least one −1 in a column containing at least one 1. However, I forget how to compute a minimax mixed strategy for such games. I think it’s a linear programming problem, and for 16 variables it can’t be done manually unless the data have some regularity to be exploited. Eyeballing the matrix, I don’t see any.
This is a zero-sum game with the same 16 strategies for each player. If player 1 does damage A1 (for attack), S1 times per (say) hour, and player 2 has armour that cancels D2 points from a successful attack and gives E2 (evade) percent change of dodging, the rate of damage by player 1 on player 2 is S1*(1-E2/100)*(A1-D2). Subtract from this the same expression with 1 and 2 swapped and that is the differential rate. Given the conditions of the problem, the only thing that matters about this rate is its sign. So there’s a 16*16 payoff matrix with entries 1, 0, and −1.
I’ve computed it, and it turns out that no strategy dominates any other: every row contains at least one −1 in a column containing at least one 1. However, I forget how to compute a minimax mixed strategy for such games. I think it’s a linear programming problem, and for 16 variables it can’t be done manually unless the data have some regularity to be exploited. Eyeballing the matrix, I don’t see any.