Training systems thinking and adaptability for complex decision making in defence and security
Bibliographic record
Abstract
Interactive learning environments are increasingly used to help people better deal with complex situations. One way to improve decision making effectiveness is to train systems thinking skills using interactive simulations in order to reduce the occurrence of “unintended side-effects” of interventions and catastrophic failures. We present a prototype training procedure intended for military officers and civilian personnel engaged in “full spectrum” operations. The Complex Decision Making simulation environment (CODEM) is the core of the proposed training procedure. CODEM aims to improve systems thinking skills, adaptability and other abilities associated with the integrative concept of cognitive readiness. Four training scenarios are designed to reproduce key properties of complex decision making situations. An intelligent tutor providing corrective feedback on decision making behaviors is integrated into each scenario. The tutor interventions help avoid tunnel vision (i.e., the opposite of systems thinking) by discouraging the use of overly simple heuristics. Nine behavioral metrics are monitored by the intelligent tutor - five are related to information seeking behaviors, and four are related to specific decision patterns. The effectiveness of this prototype training procedure is currently being assessed experimentally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".