Apparent downward causation does not demand that estimates of aggregate properties be correct or even good predictors of the system’s future state or successful strategies (although that would be useful). Furthermore, components do not need to agree in their estimates of the variables.
Apparent downward causation becomes effective downward causation—the strong form—when:
- — aggregate properties are predictive of the future state of the system (slow variables);
- — aggregate properties are robust to small perturbations;
- — estimates of these variables are used nearly universally by all components to tune decision-making;
- — components largely agree in their estimates of these variables; and
- — as estimates converge there should be an increase in mutual information between the microscopic behaviour and the macroscopic properties.
As an interaction or environmental history builds up at the microscopic level, the coarse-grained representations of the microscopic behaviour consolidate, becoming for the components increasingly robust predictors of the system’s future state. We speak of a new organizational level with effective downward causation when:
- — the estimates are (functionally) good approximations of the idealized aggregate properties, summarizing regularities in the system well enough to be useful for prediction;
- — the aggregate properties are sufficient to predict system dynamics at the macroscopic scale;
- — the system’s components rely to a greater extent on the coarse-grained descriptions of the system’s dynamics for adaptive decision-making than on microscopic behaviour;
- — the course-grained estimates made by components are largely in agreement; and
- — there is mutual information between the consolidating higher level/layer and the microscopic behaviour one level down.
Jessica C. Flack 2017 Coarse-graining as a downward causation mechanism Phil. Trans. R. Soc. A.3752016033820160338 http://doi.org/10.1098/rsta.2016.0338