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HUMAN to SYSTEM relationship models

A working library of recurring human behaviour and how systems could adapt to them. This is a working library, updated as mechanisms are understood and developed.

Decision Compression
People recognise what they want when they see it but cannot describe it in advance. Declared preferences are unreliable proxies for revealed ones.
Constructed Insight
Understanding arrives through guided inquiry. The right questions create the conditions for insight to surface.
Autonomy Gradient
The degree of agency delegated to system could only increase as trust increases.
Preference Accumulation
Preferences are situational. Taste is more stable. Systems should learn how people choose, not just what they choose.
Cognitive Scaffolding
People often need help thinking, not answers. Systems can help with strengthening thought rather than generating output.
Elimination Sequencing
When constraints are objective and self-known, preferences can be declared sequentially.
Tiebreakar Protocol
When users know what they want, but need arbitration in deciding between a shortlist of options.
Escalation Intelligence
Not everything should be delegated. Knowing when a human should re-enter the loop is as important as knowing when they shouldn’t.
Intent Extraction
Users rarely articulate their actual need. They ask for solutions instead of expressing goals.
Judgment Amplification
The most valuable systems amplify expertise rather than replace it. Experts need support, not substitution.
Adaptive Complexity
Beginners and experts require different experiences. Interfaces should reveal sophistication progressively.
Attention Compression
Attention is the scarce resource. Reduce cognitive load without reducing awareness.
Shared Decision Systems
Important decisions require collaboration. The future is collaborative intelligence, not replacement.