Building on mediated resonance, we now focus on algorithmic and technical feedback loops as extensions of metaharmonic principles. Algorithms, AI systems, and networked infrastructures are not inert; they phase, modulate, and propagate patterns, influencing relational fields at social, ecological, and symbolic scales.
Key dynamics of algorithmic harmonics:
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Self-adjusting loops: Algorithms respond to inputs from users, environments, and other systems, continuously tuning outcomes without fixed endpoints.
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Nested feedback: Local adjustments can propagate through networks, shaping global systemic behavior.
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Amplification and damping: Technical systems selectively reinforce or attenuate patterns of interaction, influencing emergent resonance.
Examples:
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Recommendation systems shaping attention and cultural trends through iterative feedback.
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Distributed AI coordinating traffic, energy grids, or ecological monitoring with dynamic tuning.
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Automated creative tools co-evolving with human participants, generating new symbolic patterns.
Algorithmic harmonics highlight the relational and ethical stakes of technical design: the way systems tune themselves and propagate resonance can expand or constrict collective possibility. Just as in biological or social systems, coherence emerges not from rigid control but from iterative, sensitive, and distributed tuning.
Key move: from computation as calculation to computation as relational modulation; from input-output thinking to systemic resonance; from static control to anticipatory feedback.
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