Definition
A pattern of change in which a system's current state repeatedly feeds back into its own rules of evolution, so that output at one step becomes (part of) the input for the next. In nonlinear systems this generates self-reinforcing loops that can stabilize (converge), oscillate, or cascade into chaos depending on feedback strength and delay.
Key Features
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Self-reference: Equations, agents, or decision rules call themselves again and again, producing nested or fractal trajectories.en.wikipedia.org
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History-dependence: Each iteration “remembers” the entire path so far; small early perturbations can snowball (path-dependency).ntrs.nasa.gov
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Emergent order: Recursion can lock a system into a limit-cycle, strange attractor, or runaway divergence—“what the loop reinforces, the loop increases.”
Why It Matters
Dominator Systems exploit negative recursive dynamics: extraction → scarcity → obedience → more extraction.
Relational-Integrity attractors seed positive recursion: reciprocity → surplus → trust → deeper reciprocity.
Map the loop, tune the gain, and you either amplify integrity or spiral into depletion.
Design Heuristics
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Trace the loop: Identify variables that feed themselves.
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Adjust the gain: Lower delays or diversify feedback paths to prevent runaway.
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Embed damping (care loops, Meissner Boundaries) so growth translates into mutual capacity, not hierarchy.
Pitfall to Avoid
Treating recursion as a straight line—without phase-lag analysis even well-meant feedback can overshoot into oscillation or chaos.
Compass Question
“Does each turn of this loop widen relational capacity, or tighten extraction?”
If the answer is “widens”, you're coding regenerative recursive dynamics; if “tightens”, you're scripting the Master's feedback trap.