Support Requirements for Cognitive Readiness in Complex Operations
Bibliographic record
Abstract
The authors report two experiments studying the requirements for effective decision making in a complex environment. The focus lies on three components of individual cognitive readiness: situation awareness (SA), problem solving, and decision making. Participants performed a simulated society management task in which they could allocate resources to stabilize a national crisis involving multiple interrelated factors (political, economic, environmental, and social). A striking aspect of this simulation is that even though information about the causes and effects within the system is available, most individuals fail to bring the system to the targeted state because of unintended consequences of their decisions. The experiments test the impact of two cognitive support tools designed to improve anticipation of future outcomes. Results show that supporting short-term anticipation (with perfectly accurate projections) was insufficient to improve effectiveness, but supporting long-term anticipation (with approximate projections) successfully improved performance in this complex environment. We conclude with a review of requirements that training and technological support should address to augment individual cognitive readiness for operations in complex environments and propose an extension to SA theory by conceptualizing a Level 4 SA (long-term projection) that may be particularly important to overcome the “wall of complexity.”
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".