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Feedback
2 min read · 340 words
Feedback is data returning from the environment about the system’s output.
The organism produces output — behavior, work, communication, decisions. The environment responds. That response is feedback: information about what the output produced. Not judgment, though it often arrives wearing judgment’s clothes. Information. The output landed here. It produced this effect. The model was this accurate.
The Criticism entry covers the identity defense system’s response to negative feedback. This entry covers the mechanism itself: the signal returning from the world that tells the system how its output is being received.
Feedback has two sources, and the system processes them differently.
External feedback — the response from other systems and from environmental conditions. This is the data the organism cannot generate internally. The plan looked good in the simulator. The feedback says it didn’t survive contact with reality. The communication felt clear from the transmitting end. The feedback says it arrived distorted. External feedback is the only source of information about how the output actually landed, which the internal model cannot accurately predict.
Internal feedback — the system’s own assessment of its output. The body reports: this movement was efficient or it wasn’t. The alignment gauge reports: this activity produces the meaning signal or it doesn’t. Internal feedback is real-time data about the organism’s own operating state during and after the output.
The operator benefits from both. The system that only processes internal feedback is operating blind — its self-assessment is running through the same filters and biases that shaped the output. The system that only processes external feedback has outsourced its assessment to other systems whose filters are equally biased, just differently.
The balanced read: receive external feedback as data about the output’s effect. Receive internal feedback as data about the operating state. Compare both against the intended result. Adjust. This is the iterative loop that produces improvement — not the single data point but the accumulated pattern across many cycles of output and response.