Methods of Risks Estimation and Analysis of Business Processes
Bibliographic record
Abstract
Abstract In the paper the model of business processes of a data‐flow type and operations above them are entered into reviewing. The methods of the analysis of business processes on effectiveness are offered. The methods of an risk estimation and analysis of business processes are considered also. The task of risk estimation and using it on practice in managing economic systems with using processing approach is investigated. Conditional‐internal and conditional‐external risks are considered and proved their properties. Metrics of risks estimation are given as highest comparative and absolute losses, average losses etc. Inefficiency of using risk estimation procedures by dispersion and quantiles is shown. Approaches to the business‐processes indicators optimization proposed and investigated in the area “risk‐profit” (“risk‐indicator”). This optimization is realized by using utility function as multicriterion problem. Functional, stochastic dependences between risks are shown, that do not allow to optimize risks only (separate from indicators). The task of risk managing in economic systems is work out in complex with the tasks of it's analysis, modeling and optimization. The problem of factor analysis in deterministic form is investigated too.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".