This post asks: what does scientific explanation look like when we adopt a relational ontology? If the world is not made of particles moving through space, but of fields of potential undergoing transformation under constraint, then explanation itself must shift—away from mechanism and toward coherence.
1. The Classical Ideal: Explanation as Causal Mechanism
In the classical tradition, to explain a phenomenon is to identify:
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A set of entities (objects with intrinsic properties),
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A causal mechanism by which these entities interact,
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A lawful regularity that governs the interactions.
This model assumes an ontological base of things whose interactions unfold in a background of space and time. It is local, deterministic (or probabilistically so), and hierarchical: macro-level events are explained in terms of micro-level causes.
Quantum mechanics and relativity have both undermined this framework:
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Entanglement defies local causality.
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Measurement resists mechanical description.
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Spacetime is not fixed, and may not be fundamental.
As a result, classical explanation becomes either contradictory or incomplete.
2. Relational Explanation: Coherence in Context
A relational ontology reframes explanation. The core question is no longer “What caused this to happen?” but:
How does this configuration arise as a coherent resolution under current constraints?
In this view:
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There are no primitive entities—only configurations of relation.
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Events are not caused by things, but are emergent resolutions of potential under tension.
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Regularities arise not from laws governing entities, but from systemic constraints shaping transformation.
Explanation becomes a matter of showing how a given outcome is the most coherent realisation available to the system, given its present state and structure.
3. Examples of Constraint-Based Explanation
Let’s consider two familiar examples from physics:
In each case, explanation identifies how the system evolves toward coherence, not how an object is pushed by a force.
4. Predictive Power and Systemic Tension
Prediction in a relational model is not about forecasting trajectories of objects. It is about anticipating patterns of coherence, given a configuration of constraints.
This shifts the emphasis from:
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State-space evolution → to tension-space transformation
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Initial conditions + laws → to configurations + constraint profiles
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Causal determinism → to systemic affordance
It also redefines explanation in probabilistic systems. Probability is not ignorance about hidden variables, but a measure of the relative coherence of different potential transitions—how compatible each one is with the total constraint structure.
5. A New Economy of Explanation
Relational explanation promises a new kind of parsimony—not in reducing everything to a few entities and laws, but in minimising ontological projection. That is:
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Don’t posit more than what is needed to explain coherence.
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Avoid metaphors (e.g. particles, forces, waves) that mislead or over-specify.
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Prioritise systemic structure over substantive mechanics.
This offers a powerful methodological guide:
A good explanation is one that makes the coherence of a phenomenon transparent without appealing to non-relational primitives.
Such explanations may be more abstract, but they are also better aligned with the nature of physical reality as revealed by quantum and relativistic theory.
Closing Thought
What we call “understanding” in physics has always been shaped by metaphysical commitments. As our best theories continue to defy the intuitions of classical thought, a new framework is needed—one that shifts the goal of science from describing entities in motion to articulating patterns of transformation within coherent relational fields.
In the next post, we will explore what this approach means for the role of mathematics in physical theory. Can equations still serve as models of reality if reality is no longer composed of measurable entities? What, in a relational ontology, does it mean for a theory to be “true”?
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