2024/11/27 by Kieran A. Murphy, Yujing Zhang, Murphy, Kieran A. +3 · 1 voice
Engineering · #Manufacturing Process and Optimization
paper · doi:10.1103/gxrh-2xsv
openalex created_date 2024/12/05 · openalex publication_date 2025/05/29 · openalex updated_date 2026/07/28
Multivariate information theory provides a general and principled framework for understanding how the components of a system are connected. Existing analyses are coarse in nature-built up from characterizations of discrete subsystems-and can be computationally prohibitive. In this work, we propose to study the continuous space of possible descriptions of a composite system as a window into its organizational structure. A description consists of specific information conveyed about each of the components, and the space of possible descriptions is equivalent to the space of lossy compression schemes of the components. We introduce a machine-learning framework to optimize descriptions that extremize key information theoretic quantities used to characterize organization, such as total correlation and O-information. Through case studies on spin systems, sudoku boards, and letter sequences from natural language, we identify extremal descriptions that reveal how system-wide variation emerges from individual components. By integrating machine learning into a fine-grained information theoretic analysis of composite random variables, our framework opens a new avenues for probing the structure of real-world complex systems.