Providing tailored heuristic advice to Systems Engineers
Bibliographic record
Abstract
Abstract An INCOSE‐wide initiative has exposed at least 600 heuristics. Previous work indicates that rationalizing and simplifying this set to make it useful and memorable is difficult, if not intractable. Difficulty Assessment Tools (DATs) have been used for years to characterize the difficulty of a problem and provide tailored advice. This paper explores using a DAT to characterize the problem, and using the outputs to provide heuristic and other forms of advice. To test this approach, 50 heuristics and 10 principles were scored and embedded into an online DAT. An experiment was conducted to determine whether the DAT discussion, recommended approach, and heuristic/principles advice were useful. All teams considered the discussion very useful. As might be expected, the results indicated that the heuristic usefulness was a function of the teams' experience and familiarity with the task. The tool prioritization of suitable heuristics met developers' expectations, but was undetected by the users. This maybe because the heuristics were a hand‐picked set of 50 Heuristics from a set of 600+, meaning all were highly useful. Further work is proposed to check this assessment. The DAT usefulness results indicate that Systems Engineers should use the DAT to inform their approach throughout the lifecycle.
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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 | 0.014 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".