I don't think you've really understood, because your contradictory response supports mine: Even when fictitious play is a good approximation for a real system (

I don't think you've really understood, because your contradictory response supports mine: Even when fictitious play is a good approximation for a real system (and they aren't because firms do not adopt static strategies), strategies do not always converge, which means iterative play does not necessarily lead you to terminal equilibrium states. But maybe we can salvage equilibria as a functional tool. Can these systems arrive at 'almost equilibrium states?" Knowing this would still give us some predictive oomph. This article states that even approximations might be out of reach, but there are ways to speed the development of the metagame along. On your other point, you've missed half of the article's content. It isn't saying "if you don't know the utility functions of the players it's hard to make computational inferences". It is saying that the amount of information required to obtain the utility function of the players for even trivial games is well beyond the scope of most systems we have, and accordingly the assumption that even approximate equilibrium will be reached requires a good independent rationale. This is a MUCH larger issue than the base computability of the problem and jumps into the epistemology of economics and policy. This is expanded upon because those assumptions regarding reaching equilibrium are often used in justifying financial, policy and other decisions where billions of dollars are at stake. See the Cournot and Bertrand competition models and their successors for a view into how that faulty assumption can lead you to terrible policy decisions.