We learn through feedback loops. Human beings are very bad at understanding cause and effect when they’re separated by time. To learn, we need to close the feedback loops so we understand the effects of our choices, and have less of a chance to blame it on externalities.
A good, iterative IA process teaches by tightening the feedback loop. Ideas get a chance to break when they’re put into a system model, then when their relationships are identified, then when their attributes are teased out, all before you move out of sticky notes. An information architect (or a feature team) that only sees what works once they ship it won’t get the kind of quick feedback they need to learn to make better decisions.
Senge, Peter M.. Random House, 2006.
We learn best from experience but we never directly experience the consequences of many of our most important decisions.
Compensating feedback usually involves a “delay,” a time lag between the short-term benefit and the long-term cost.