A scalable, synergy-first backbone decomposition of higher-order structures in complex systems (2402.08135v1)

Abstract: Since its introduction in 2011, the partial information decomposition (PID) has triggered an explosion of interest in the field of multivariate information theory and the study of emergent, higher-order ("synergistic") interactions in complex systems. Despite its power, however, the PID has a number of limitations that restrict its general applicability: it scales poorly with system size and the standard approach to decomposition hinges on a definition of "redundancy", leaving synergy only vaguely defined as "that information not redundant." Other heuristic measures, such as the O-information, have been introduced, although these measures typically only provided a summary statistic of redundancy/synergy dominance, rather than direct insight into the synergy itself. To address this issue, we present an alternative decomposition that is synergy-first, scales much more gracefully than the PID, and has a straightforward interpretation. Our approach defines synergy as that information in a set that would be lost following the minimally invasive perturbation on any single element. By generalizing this idea to sets of elements, we construct a totally ordered "backbone" of partial synergy atoms that sweeps systems scales. Our approach starts with entropy, but can be generalized to the Kullback-Leibler divergence, and by extension, to the total correlation and the single-target mutual information. Finally, we show that this approach can be used to decompose higher-order interactions beyond just information theory: we demonstrate this by showing how synergistic combinations of pairwise edges in a complex network supports signal communicability and global integration. We conclude by discussing how this perspective on synergistic structure (information-based or otherwise) can deepen our understanding of part-whole relationships in complex systems.

• The paper introduces a scalable synergy-first backbone decomposition that quantifies the joint information lost when any single element is perturbed.
• It defines synergy as the specific loss of information due to individual perturbations, offering a clearer metric than traditional PID methods.
• The method's versatility is demonstrated through applications in network theory and machine learning, revealing higher-order multivariate interactions.

Analysis of Synergistic Information Structures in Complex Systems

The paper introduces a novel approach to understanding multivariate information structures in complex systems, emphasizing synergistic interactions. It addresses limitations in existing methodologies like Partial Information Decomposition (PID) and O-information by proposing a synergy-first backbone decomposition, which effectively scales with system size while offering a clear definition and interpretation of synergy.

The core concept of synergy in this context refers to information present in the joint state of multiple elements that is lost if any single element's state is perturbed. This is articulated through a synergy-first perspective that diminishes the reliance on redundancy as a defining feature. The newly introduced decomposition produces what the author terms a "backbone" of partial synergy atoms that are hierarchically ordered. Each atom captures the information that requires progressively larger subsets of elements to be fully revealed, thereby providing a systematic understanding of how synergy is distributed across various scales.

Key Contributions

1. Scalable Decomposition: The proposed decomposition scales more gracefully than traditional PID, avoiding the explosion in computational complexity associated with large systems. By focusing on direct synergy in subsets of elements, the approach remains interpretable even as system size increases.
2. Formal Definition of Synergy: Synergy is defined as the information lost when any single element is perturbed, offering a clear metric that contrasts with the vague redundancy-first definitions used in PID.
3. Applications Beyond Information Theory: The methodology is extended beyond information theory to assess higher-order interactions in a variety of contexts. This includes applications in network theory where synergistic combinations of network edges can influence signal transmission and integration capabilities.
4. Theoretical and Practical Implications: By providing a clear metric for synergy, this work facilitates the exploration of emergent behaviors in complex systems. It can potentially deepen our understanding of part-whole relationships, emphasizing the importance of interactions that are not merely the sum of their individual components.
5. Case Studies and Generalization: The paper extends its methods to analyze the Kullback-Leibler divergence and explores applications such as single-target mutual information. It offers various formulations of synergy, demonstrating flexibility depending on the system under analysis.

Underlying Assumptions and Challenges

While the methodology offers a novel perspective, it assumes that information structures can be consistently decomposed into synergistic components. The real-world applicability of this assumption, especially in non-theoretical environments, needs further exploration. Moreover, though the approach simplifies mathematical representation, the reliance on sampling and optimization in large systems could affect precision and repeatability.

Future Directions

The proposed framework opens several avenues for future research. It suggests the potential for investigating the dynamics of systems beyond static states, such as exploring cascading failures or the robustness of complex networks. Additionally, integrating this decomposition with machine learning models could enhance the interpretability of high-dimensional feature interactions, further bridging the gap between theoretical insights and practical applications.

In conclusion, this work provides a comprehensive yet manageable framework for iteratively understanding the role of synergy in complex systems. By shifting the focus from redundancy to synergy, it enriches the toolset available for analyzing multivariate systems, providing a foundation to explore both theoretical nuances and practical scenarios where emergence plays a critical role.