My experience is that more knowledge can help an algorithmic decision process make a better decision, but it can also take longer to make that decision when you're dealing with combinatorial explosion. When you inject delay into a feedback system, you almost always will introduce oscillations. (You can do some things to partially correct for it, but that works best when you have a predictable and consistent amount of delay.) Intuitively, you can think of this as taking too long to respond, and then overcorrecting time after time, since you keep overshooting your target and not realizing that you've overshot for some time.
That said, computer systems are similarly prone to inaccuracy. Perhaps not to the same extent as human organizations, but they have similar propensity to fail spectacularly. If you fail to model certain characteristics of the system (or model them poorly), then you can end up in a situation where your load balancer is causing significant congestion and not thinking anything is wrong. Or your hardware planning pipeline thinks that it has lots of spare capacity even though you're overflowing every day at peak. Or your central planning committee for the government thinks you have lots of excess grain production when that's simply not the case in reality.
(Algorithm implementations can also be outright buggy.)
Another way your model can fail is if you treat resources as infinitely fungible. Suppose you have enough food in Shanghai to feed two billion people, but no food anywhere else in China. Without an adequate transportation / distribution network, everyone outside Shanghai is going to starve, and all the excess food in Shanghai is going to rot. Similarly, your servers in California aren't going to do very much for users in India if you don't have the infrastructure to actually serve that long-haul traffic.
Another point that I didn't bring up, but related to your point about global vs local decision makers, is that centralized systems have much larger blast radius. In the case of China during the Great Leap Forward (one of the examples from the article), bad decisions led to famine across the entire country. In the case of a global load balancer, you can break your service globally.