This pattern operates within contexts where there is a separation between those who create measurements and targets, and those who are evaluated by them. The pattern assumes that agents have sufficient understanding of the measurement system to engage in strategic behavior, and that there are meaningful consequences (positive or negative) tied to target achievement. The boundary encompasses the feedback loop between measurement, targeting, and behavioral adaptation.
The pattern excludes scenarios where measurements are purely observational without behavioral consequences, or where the underlying system is so simple that gaming is impossible or meaningless. It also assumes that agents have some discretion in how they pursue targets - in completely constrained systems where only one path to target achievement exists, distortion cannot occur.
The dynamics within this boundary are fundamentally about the tension between simplification (necessary for measurement) and complexity (inherent in real systems), and how optimization pressure exploits this gap. The pattern is bounded by the assumption that measurement distortion is generally undesirable from a system design perspective, though in some contexts, the behavioral changes induced might be acceptable trade-offs.