1. Computation as Systemic Transformation
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Computation is not merely symbol manipulation by an isolated processor.
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Instead, it is a continuous process of relational actualisation, where the system evolves by reconfiguring constraints and potentials.
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Information processing becomes a form of dynamic adaptation within a network of relations.
2. Moving Beyond Static Architectures
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Classical computing relies on fixed architectures and binary states.
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Quantum computing introduces superposition and entanglement but remains largely within a state-vector formalism.
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A relational approach embraces non-linear, context-sensitive, and multi-scale dynamics where computation is emergent from systemic coherence.
3. Examples and Models
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Neural networks and biological systems illustrate relational computation: their “processing” emerges from interaction patterns, not discrete stepwise algorithms.
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Recent models in quantum cognition and reservoir computing hint at how relational dynamics can harness complex constraint modulations.
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Novel computing paradigms (e.g., topological quantum computing, morphogenetic computing) resonate with relational concepts.
4. Implications for Artificial Intelligence and Complex Systems
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Relational computation reframes AI not as symbolic manipulation but as participation in relational fields, with learning as adaptation of coherence patterns.
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Complex systems—from ecosystems to social networks—compute relationally by evolving constraints and affordances.
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This perspective encourages new algorithmic designs emphasising fluidity, context, and emergence.
Closing
Reimagining computation relationally challenges the limits of current paradigms and invites innovation grounded in the dynamic fabric of reality. It opens pathways toward more robust, adaptive, and integrated technologies.
The next post will explore how relational ontology informs our understanding of causality and agency in complex physical and biological systems.
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