Connection-based framework for assessing natural complexity in nonlinear adaptive systems
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Bibliographic record
Abstract
This study introduces a quantitative framework for assessing natural complexity in adaptive systems, based on connection measures weighted by sensitivity indices. The methodology integrates system modeling, sensitivity analysis, and complexity assessment, enabling continuous monitoring and decision support in dynamic environments. Natural complexity is defined as an optimal level at which the system behaves in accordance with its nature, sustaining coherence between structure and function. By employing sensitivity-weighted connections, the framework captures both internal organization and adaptive dynamics, overcoming limitations of traditional metrics such as Shannon entropy and fractal dimension, which often neglect interaction intensity and temporal variability. The framework is validated through two case studies: a computational model of an Intensive Care Unit and a real-world startup acceleration ecosystem. In the Intensive Care Unit, periods of overload were identified through peaks in complexity, associated with an increased number of highly sensitive parameter connections. In contrast, in the startup ecosystem, systemic idleness was reflected by lower complexity levels, driven by weakly influential interactions among actors. These findings highlight the responsiveness and interpretability of the proposed metric compared to conventional approaches, particularly in tracking adaptive states over time. This connection-based framework supports the management of adaptive information systems, offering a dynamic and scalable complexity assessment tool. Its applicability spans medical informatics, business management, and distributed systems optimization, providing real-time insights that improve resilience and efficiency. In addition, the approach aligns with industry 4.0 paradigms, facilitating preventive analyses and adaptive decision-making in advanced technological environments. By offering a unified methodology for complexity evaluation, this research advances understanding and control of complex adaptive systems. • Natural complexity quantifies adaptive system behavior via connection modeling. • Sensitivity-weighted connections enable dynamic system complexity assessment. • The metric ψ ( c , γ c ) identifies idleness, regularity, and overload states. • Validation performed in ICU and startup acceleration real-world case studies. • Framework improves decision-making, resource allocation, and resilience planning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it