To maintain or not to maintain? What should a risk‐averse decision maker do?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose In real‐life applications maintenance managers often face complicated decision problems under uncertainty. This difficulty increases when they have to take conflicting objectives into account. A recent review of the literature shows that previous works consider repairable systems subject to random failures and analyse trade‐offs between the costs and the benefits of maintenance activities. The risk aversion of the maintenance decision maker may be not underlined enough. This paper aims to deal with a single component system that has to accomplish a series of missions of a given length. Design/methodology/approach The development of a maintenance strategy for this system is analysed from a risk aversion point of view. An attempt is made to highlight the attitude of a neutral decision maker versus a risk‐averse manager. Findings Presents a very simple framework to analyse the risk aversion effect on managers' decisions. The model confirms the observation that risk aversion implies no‐monotone relation between optimality frequencies of maintenance operations and the deformation rate of the breakdown probability. Originality/value Since the deformation rate is monotonic with time, the proposed model can be extended to derive optimal frequencies, which allow the implementation of the optimal deformation rates according to the probability law of the deformation rate δ.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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