General significance of alterations in higher-order synergy and redundancy

Determine, in the general case across complex systems, what observed alterations in higher-order synergistic and redundant information imply about system-level properties and behavior, beyond condition-specific findings reported in individual domains.

Background

The paper reviews numerous studies reporting changes in higher-order information—particularly redundancy and synergy—across diverse natural and artificial systems (e.g., brains under different states, aging, task performance in neurons, and training in neural networks). Despite these observations, there is no unified understanding of what such alterations mean in general for the systems being studied.

This motivates the authors’ approach of evolving Boolean networks to be highly redundant, synergistic, or complex, and then characterizing their dynamics; however, the broader interpretive significance of changes in redundancy or synergy remains an unresolved question.