I occasionally see a question like "what would FDT recommend in ....?" and I am puzzled that there is no formal algorithm to answer it. Instead humans ask other humans, and the answers are often different and subject to interpretation. This is rather disconcerting. For comparison, you don't ask a human what, say, a chessbot would do in a certain situation, you just run the bot. Similarly, it would be nice to have an "FDTbot" one can feed a decision theory problem to. Does something like that exist? If not, what are the obstacles?
Just to follow up on that third point a little more: FDT depends upon counterfactual responses, how you would have responded to inputs that you didn't in fact observe.
If you go into a scenario where you can observe even as little as 6 bits of information, then there are 2^6 = 64 possible inputs to your decision function. FDT requires that you adopt the function with the greatest expected value over the weighted probabilities of every input, not just the one you actually observed. In the simplest possible case, each output is just one of two deterministic actions and there are only 2^(2^6) = 18446744073709551616 such functions to evaluate. If your space of possible actions is any larger than a deterministic binary action, then there will be vastly more such functions to evaluate.
Unlike both CDT and EDT, FDT is massively super-exponential in computational complexity. Pruning the space down to something that is feasibly solvable by actual computers is not something that we know how to do for any but the most ridiculously simple scenarios.