2022/07/01 by Tomas Scagliarini, Scagliarini, Tomas, Davide Nuzzi +11 · 3 citations
Economics, Econometrics and Finance · Physics and Astronomy · #Complex Systems and Time Series Analysis #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Information Theory (cs.IT) #Opinion Dynamics and Social Influence #Statistical Mechanics and Entropy #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2207.03581
openalex publication_date 2022/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
O-information is an information-theoretic metric that captures the overall balance between redundant and synergistic information shared by groups of three or more variables. To complement the global assessment provided by this metric, here we propose the gradients of the O-information as low-order descriptors that can characterise how high-order effects are localised across a system of interest. We illustrate the capabilities of the proposed framework by revealing the role of specific spins in Ising models with frustration, and on practical data analysis on US macroeconomic data. Our theoretical and empirical analyses demonstrate the potential of these gradients to highlight the contribution of variables in forming high-order informational circuits