Empirical Viability of Nested Markov Blanket Identification Procedures Across Scales

Determine whether existing procedures for identifying nested Markov blankets from empirical timeseries can reliably decompose distributed systems across scales in real-world empirical studies.

Background

The authors outline an empirical program to test whether a system fits their metacognitive particle framework by inferring dynamics from data and checking for the required structural properties. They note that practical procedures exist for identifying nested Markov blankets from timeseries, with a worked example using brain imaging data.

However, it remains uncertain whether these procedures can effectively decompose large, distributed systems across multiple scales in broader empirical contexts.

References

On a practical level, there are procedures for identifying nested Markov blankets from empirical timeseries, allowing the identification of multiply nested particles. Whether these procedures find purchase in decomposing distributed systems across scales, in further empirical studies, remains to be seen.

— Metacognitive particles, mental action and the sense of agency  (2405.12941 - Sandved-Smith et al., 2024) in Discussion, Subsection 'Empirical testing'

Pinpointing and characterizing the spatiotemporal extent of experiencing systems is an open question.

— Gradland: On Phenomenal Experience, Differentiated Across Many Dimensions  (2609.09306 - Balduzzi, 8 Sep 2026) in Section 3.2, subsection “Specifying systems” (following Definition 2)