Why Systems Reward Better Scores — Even When the Work Gets Worse

Описание: In many complex systems, performance is guided by numbers. Metrics, indicators, and targets are designed to show whether the system is improving. On the surface, this seems rational: if the numbers go up, the system must be getting better.

But systems often behave in a more complicated way.

Over time, organizations begin to adapt their behavior to the signals used to measure performance. Teams learn which numbers matter, processes start to align with those measurements, and decisions increasingly focus on improving the indicators themselves.

The result can be a strange outcome: the system’s metrics improve, but the underlying work those metrics were meant to represent does not.

This video explains why this happens. By examining how measurement signals shape incentives and decision-making inside complex systems, it shows how systems can gradually shift from improving real performance to improving the signals that represent it.

The explanation explores how measurement, feedback loops, and organizational incentives interact — and why systems that rely heavily on indicators can slowly drift away from the reality those indicators were meant to capture.


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