1. Distributed Causality Beyond Linear Chains
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Traditional causality models rely on direct, localised cause-effect sequences.
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In relational terms, causality emerges from networked interactions where effects arise from patterns of constraint modulation across systems.
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Causes are not isolated triggers but systemic conditions enabling or constraining transitions.
2. Agency as Relational Capacity
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Agency shifts from being a property of isolated actors to a capacity of relational networks to enact change.
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Organisms, machines, and even ecosystems manifest agency by modulating relational constraints within their environments.
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This perspective highlights emergent, context-dependent, and multi-scalar agency.
3. Feedback and Circularity
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Complex systems exhibit feedback loops, where cause and effect become intertwined and recursive.
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Relational ontology embraces circular causality as fundamental, allowing systems to self-organise and maintain coherence.
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This contrasts with reductive views that struggle to accommodate such dynamics.
4. Implications for Science and Philosophy
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Understanding causality and agency relationally encourages interdisciplinary approaches integrating physics, biology, cognitive science, and social theory.
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It invites rethinking responsibility, decision-making, and intentionality in light of distributed agency.
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The relational view offers conceptual tools to model emergence and adaptation more effectively.
Closing
Relational ontology dissolves rigid boundaries between cause and effect, self and environment, actor and system—offering a dynamic framework to understand the complex interplay at the heart of life and matter.
In the next post, we will consider how this perspective impacts our understanding of time and temporality in physical theory and experience.
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