Uncertainty and complexity are opposite sides of the nescience coin. One who suffers from uncertainty is frustrated by a lack of knowledge; one who suffers from complexity is frustrated by a lack of know-how. Knowing what to do is one thing, but knowing how to do it is quite another thing. Even if one knows of a way, in principle, to solve a problem, the computational burden of the solution may be impossible with existing technology. In such cases the solution must await the development of improved methods of computation.

Apparent complexity can often be reduced by eliminating irrelevant attributes of the decision problem, a procedure that Rasmusen terms “no-fat modeling.” This approach is as follows: “First, a broad and important problem is introduced. Second, it is reduced to a very special but tractable model that hopes to capture its essence. Finally, in the most perilous part of the process, the results are expanded to apply to the original problem” (Rasmusen, 1989). Once the problem has been reduced to its essence, game theory provides a way to compute solutions to be expanded to the original problem.