I don't know what the problem was where I was getting different results when I ran the code myself, but I suspect there was some code before it that I didn't run that was relevant, because when I went back and tried it again, I am getting the same results.

Is it fair to say that the solutions found in field theory of games are numerical solutions to a closed-form expression for iterative play of classical game theory? At least, as shown through lesson 8?

Yes, my code was meant to be an example of my reverse polish intuition from HP-45 days. Is this the general concept with v being the more valuable (3/4 for Blue-Major in your case)?

TableForm[{{1, 1 - v}, {v, 1}}, 
 TableHeadings -> {{Subscript[m, B], Subscript[M, B]}, {Subscript[m, 
    R], Subscript[M, R]}}]

Given a quadratic determinant, that suggests a quadrant of the unit circle as trade-off. Clearly, I am looking for an idiot’s guide to payoff matrices and an explanation of why the WL MatrixGamePayoff tableau looks so different.

Last century, before you provided such nifty code, I would have looked at attackDefense this way,

Block[{b, r, p, s}, b = {{4, 1}, {3, 4}}; r = -Transpose@b;
 p[x_] := {x[[1, 2]]/Det[x], x[[2, 1]]/Det[x]};
 {s = p[#] & /@ {b, r}, Norm /@ Normalize /@ s} // Column
 ]

But I admit that I never understood the input. For example, what does the 3 mean? Is that blue’s value for thing 1 and Red’s value for thing 2? I got your books but I remain confused about the payoff versus the view, etc.

I am not familiar with it, but thank you for the link. It appears related to the work people do in Systems Dynamics, though from the web site the underlying mathematics is not clear. That it draws from science is definitely a connection.

How does this relate to Operation Science: https://opscience.org/