SΩI: Score-based O-INFORMATION Estimation in Multivariate Systems

Core Concepts

O-INFORMATION provides insights into synergy-redundancy balance in multivariate systems, with SΩI offering a scalable and flexible estimation method.

Abstract

The content discusses the development of SΩI for computing O-INFORMATION in multivariate systems, overcoming limitations of existing methods. It introduces the concept of information synergy and redundancy, highlighting the practical applications and experimental validations. The paper explores high-dimensional interaction measures, score-based divergence estimation, and experimental validation in synthetic and real-world scenarios. Introduction to O-INFORMATION and its significance. Limitations of existing methods like PID. Development of SΩI for scalable O-INFORMATION estimation. Experimental validation in synthetic and real systems. Application to neuroscience data analysis.

Stats

Mutual Information (MI) is fundamental for non-linear dependence between random variables (Shannon, 1948; MacKay, 2003). Computational complexity grows fast as the Dedekind number of variables increases (more than 10^31 for 9 variables).

Quotes

"The main limitations of PID persist in all variants." "Recent work focuses on studying individual influence of variables to high-order interactions."

Key Insights Distilled From

S$Ω$I

by Mustapha Bou... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2402.05667.pdf

Deeper Inquiries

How can SΩI impact the analysis of complex systems beyond neuroscience

SΩI can have a significant impact on the analysis of complex systems beyond neuroscience by providing a flexible and scalable method to quantify high-order interactions among multiple random variables. This can be beneficial in various fields such as climate modeling, econometrics, machine learning, and many more. For example, in climate modeling, understanding the intricate relationships between different environmental factors is crucial for accurate predictions. SΩI can help in quantifying how these variables interact with each other, leading to better models and forecasts.

What counterarguments exist against the scalability and flexibility claims of SΩI

Counterarguments against the scalability and flexibility claims of SΩI may include concerns about computational efficiency and resource requirements when dealing with very large datasets or high-dimensional systems. While SΩI has shown promising results in handling multivariate systems efficiently, there might still be challenges when scaling up to extremely complex scenarios with a vast number of variables. Additionally, the flexibility of SΩI may be questioned in terms of its adaptability to diverse data types or distributions beyond what has been tested so far.

How can the concept of information synergy be applied to unrelated fields but still provide valuable insights

The concept of information synergy can be applied to unrelated fields such as social sciences or economics to gain valuable insights into complex interdependencies within those domains. For instance, in economics, analyzing how different economic indicators synergistically affect each other could provide deeper insights into market trends and financial stability. Similarly, in social sciences like sociology or psychology, studying how various societal factors synergize to influence human behavior or decision-making processes could lead to a better understanding of social dynamics and group behaviors.