We develop closed-form Gaussian estimators for multi-source PID, providing two-source redundancy, multi-source unique information, and K-th order synergistic effects. The estimators are derived from conditional-independence-based information measures and reduce to log-determinant expressions in covariance blocks.

This work establishes fundamental structural impossibility results for the antichain-lattice framework of Partial Information Decomposition.

We provide explicit closed-form formulas for all information atoms in the two-source case and prove that for three or more sources, PID suffers from fundamental inconsistencies. We propose alternative measures to address these issues.

This work identifies and analyzes the subsystem inconsistency problem in Partial Information Decomposition, showing that the whole can be less than the sum of its parts.

A comprehensive survey reviewing quantitative approaches to understanding causal emergence in complex systems, covering information-theoretic and computational methods.

We derive an explicit closed-form formula for the bivariate Partial Information Decomposition, providing analytical solutions for redundancy, unique information, and synergy.

Master’s thesis exploring the use of information decomposition frameworks to capture and quantify higher-order relationships in complex systems.

We introduce System Information Decomposition (SID), a holistic framework that decomposes the information entropy of all variables in a system into information atoms, going beyond the source-target paradigm of traditional PID.

This paper studies the dynamic issuance mechanism of Central Bank Digital Currency (CBDC) and its implications for monetary policy.

This paper proposes a blockchain-based credit mechanism for electronic honor certificate systems.