It is absolutely not true that the problem can "definitely be solved." You have no grounds to make such a ridiculously confident statement about such a complicated problem. AI is not magic which can solve any sort of problem you show if you just sacrifice enough GPUs to the ML god.

The notion of constrained optimization is not exactly new, that isn't the hard part. And while solving a constrained multi objective optimization problem is generally gonna be np-hard, if it even has a well-defined solution, even that isn't actually the hard part.

The problem is figuring out what the inputs and measured outcomes should even be and then getting them into a form that an AI can actually process. I was not asking you to tell me that the objective function would be an optimization problem; that's what they all are. I was asking you what the actual objective and actual constraints are. Because there is no way that you can possibly summarize every important impact of an economic policy in an objective function, much less doing so while differentiating it across different interest groups. Nor could you actually encode all of the input information which might be relevant.

And then what would you even train on if you could accomplish that already impossible task? It's not like we have a large or terrible diverse set of worked examples of fully characterized policies and outcomes. And if you wanted to take a more unsupervised route then it basically amounts to accurately simulating an economy, which in itself would be worth all the nobel prizes.