Measuring system resilience through a comparison of information- and flow-based network analyses

Area of Science:

• Complex Systems Analysis
• Network Theory
• Sustainability Science

Background:

• Quantifying self-organizing systems is crucial for understanding system development and state.
• Conventional flow-based network analysis is often limited by data availability.
• A novel information-based technique, QtAC, models interactions as information transfers, overcoming data constraints.

Purpose of the Study:

• To compare the novel information-based QtAC method with conventional flow analysis.
• To evaluate the applicability of QtAC in complex systems analysis with limited data.
• To assess resilience indicators derived from both network approaches.

Main Methods:

• Application of both QtAC and conventional flow analysis to a 90-year socio-economic dataset from Samothraki, Greece.
• Derivation of resilience indicators using Ulanowicz's ascendency analysis on both network types.
• Comparative analysis of network dynamics and indicator interpretations.

Main Results:

• Information-based networks modeled by QtAC align more closely with complex system dynamics (adaptive cycle model).
• QtAC provides alternative interpretations of network indicators compared to flow analysis.
• The study demonstrates QtAC's utility when conventional flow analysis is constrained by data.

Conclusions:

• QtAC serves as a viable alternative for complex systems analysis, particularly when data limitations hinder traditional flow-based methods.
• Combining QtAC with conventional flow analysis can generate novel and valuable insights.
• The study highlights the methodological evaluation of sustainability frameworks in complex systems.