Decision Strategy Types and Situation-Contingent Selection Mechanisms: A Review and Some Field Data
Bibliographic record
Abstract
Decision research has revealed a variety of adaptive strategies that experts use when making decisions; however, there is no widely accepted model of how experienced decision makers choose such a strategy to solve a particular decision problem. Within most decision-making models that include a selection mechanism, decision strategies are selected according to cost-benefit trade-offs. These models assume that the selection is based on an evaluation of the subjectively expected utility of a correct decision and the effort the decision maker is willing to make in the situation at hand. In opposition, there are research findings showing that proficient decision makers mainly seem to select strategies based on recognition of the decision situation and a history of successful applications of a certain strategy. In this context I discuss findings from a field study in production planning and scheduling that are contrary to predictions from cost-benefit models. In accordance with recent scholarly work on routine decisions, I suggest that decision-strategy selection mechanisms based on recognition are a valid theoretical background for the design of future decision support systems. Accordingly, the cognitive engineering focus would shift from accuracy maximization and effort reduction to situational differentiation and strategy learning.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".