Multi-Level State Evaluation in Complex Systems: Information Granules and Evidence Theory Approach
Bibliographic record
Abstract
Abstract Real-world systems often exhibit intricate complexity. Navigating and examining the conditions under which these systems operate presents various challenges. These systems are characterized by a web of interconnected inputs and subsystems organized in hierarchical way. The status of each subsystem depends on multiple inputs and the conditions of other interconnected subsystems. Additionally , articulating precise definitions for the states of these subsystems is a complex task, usually fraught with uncertainties. Experts frequently employ information granules to encapsulate imprecise quantities, basing these granules on specialized domain knowledge. These granules may manifest as linguistic terms or intervals, resulting in approximate state definitions. To the best of our knowledge , no methodology currently accommodates multiple uncertainties – including those tied to state definitions – while also offering an evaluation of the varying states across different subsystems. In this study, we present a novel technique for identifying local and global states in hierarchical, multi-component systems under conditions of uncertainty. Utilizing principles of Evidence Theory, we incorporate a recently devised method to evaluate how well uncertain objectives are met. These uncertain objectives correspond to state definitions formulated using information granules of a specific context. By measuring the extent to which the inputs to a given subsystem align with these imprecise state definitions, we can identify the most probable state the subsystem will likely be in. Our proposed method addresses various types of uncertainty when ascertaining system states. The specific areas of imprecision tackled by our approach include: 1) the vagueness and ambiguity inherent in the measurements serving as subsystem inputs, 2) the levels of uncertainty involved in defining subsystem states based on the conditions of other interconnected subsystems, and 3) the indistinct and incomplete knowledge incorporated into the definitions describing individual subsystems’ states. The paper elaborates on the intricacies of our method and includes a case study to demonstrate how system states can be identified when faced with ambiguous definitions of subsystem states.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".