UNCERTAINTY AND THE DECISION MAKER: ASSESSING AND MANAGING THE RISK OF UNDESIRABLE OUTCOMES
Bibliographic record
Abstract
We present an approach to rank order new programs in ways that accommodate uncertainty of different outcomes occurring, on the basis of the size and nature ('bad' or 'good') of those outcomes. This represents an improvement on the way uncertainty has been accommodated in existing approaches (e.g., threshold approach to cost-effectiveness analysis). We illustrate the approach using the decision making plane, which explicitly incorporates opportunity costs and relaxes the assumptions of perfect divisibility and constant returns to scale of the cost-effectiveness plane. The nature of the bad (or good) outcome is determined by the quadrant that it falls into (i.e., a 'quadrant effect') and its magnitude by its location within the quadrant (i.e., 'within quadrant effect'). By explicitly defining the loss function, the process of accepting (or rejecting) a new program becomes transparent. We illustrate the approach using a loss function and a net gain function. We show that by recognizing that, not all bad (or good) outcomes are equal and the choice of a loss or a net gain function can result in different ranking of resource allocation options. Further implications of the proposed approach are discussed.
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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 | 0.049 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".