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Techniques for Identifying Agency

It seems to me that an objective attempt to locate agency as an emergent property will benefit from the following kinds of investigation. First, assessing the degrees of spatio-mechanical freedom between components in a system (and also between each component and their internal subcomponents) and secondly, the redistribution of energy within the system over time.

If part of a system is rigidly connected to others, then we can immediately relocate our search for causes to a higher level. Consider a system of three equal length struts loosely jointed to form a triangle. Any perturbation of the triangle will be immediately and entirely passed on to the whole. It is rigid by definition. This is not the case with a system of four struts. If we perturb one corner, this necessarily moves three struts, but not the fourth. We end up with a different trapezoid. Unlike the triangle, the trapezoidal system can experience changes/causes within itself because it has an internal degree of freedom. We can predict the state of the triangle into the future because it is stable, but we cannot do so with the trapezoid because we may find it in an infinite number of equally possible states. As we continue into three dimensions, we can conceive of systems with various other kinds of freedom: Some which are perfectly rigid, some with 'universal joints', and some that are perfectly free to move and occupy any point in space.

Clearly this is a crude analogy, but I believe this kind of analysis hints at the following: It's possible to classify systems according to the kinds and quantities of spatio-mechanical freedom exhibited by their components. We can then locate various degrees of emergent causation at the joints that enable this freedom. Rather than saying that systems experience 'bottom-up causation', or 'top-down/whole-part influence', we could produce a graph showing the distribution of causal efficacy as it varies across the components and joints that make up the system. I like the term 'system causation' or 'network causation' to describe such a view because it acknowledges a simultaneous causal role at many scales from the unitary to the aggregate to the complex.

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