Decision‐first modeling should guide decision making for emerging risks
Bibliographic record
Abstract
An emerging risk is characterized by scant published data, rapidly changing information, and an absence of existing models that can be directly used for prediction. Analysis may be further complicated by quickly evolving decision-maker priorities and the potential need to make decisions quickly as new information comes available. To provide a forum to discuss these challenges, a virtual conference, "Decision Making for Emerging Risks," was held on June 22-23, 2021, sponsored jointly by the Decision Analysis Society of the Institute for Operations Research and the Management Sciences and the Decision Analysis and Risk specialty group in the Society for Risk Analysis. Speakers reflected on the work to support decision-makers related to the COVID-19 pandemic as well as experiences in emerging risks across domains from cybersecurity, infrastructure, transportation, energy, food safety, national security, and climate change. Here, we distill the key findings to propose a set of best practice principles for a "decision-first" approach for emerging risks. These discussions underscore the importance of scoping the decision context and the shared responsibility for the development and implementation of the analysis between the analyst and the decision-maker when the context can evolve rapidly. Emerging risks may also favor simpler analytical approaches that increase transparency, ease of explanation, and ability to conduct new analyses quickly. Continued dialogue by the decision and risk analysis communities on the use and development of models for emerging risks will enhance the credibility and usefulness of these approaches.
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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 | 0.064 | 0.108 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".