I wonder about this. One of the most common human methods of making decisions with incomplete information and/or short time frames is to fall back on heuristics rather than try to reason through the data. Heuristics have the advantage of being fast and available, but they're also notoriously bad at producing the quality of decision that might come from a more thorough analysis of the data.
Frankly, I think I'd feel more comfortable with computers that made decisions through brute force processing and bayesian analysis.
Brute forcing all possible combinations and finding the best one (for your specific goal, or taking into account all externalities as well) would be great. But it is impossible for two reasons:
1) incomplete, or even purposefully incorrect information
2) time constraints/compexity. For a non-linear system with more than a few variables, it is just not feasible to enumerate everything.
You end up with heuristics. The challenge is that remains is finding good heuristics. If the decision is given more time, you can probably come up with better heuristics and even sort-of brute force through the "best" options (which is what humans do when they reason).