Complex decision making experimental platform (CODEM): A counter-insurgency scenario
Bibliographic record
Abstract
The complex decision making experimental platform (CODEM) is intended as a shareable research tool to stimulate multidisciplinary research on complex dynamic situation management and as an environment for training and testing cognitive readiness. The experimenter can set general parameters, configure the interface, specify the model, insert events and define the resources and capabilities of each player using the scenario development tool. No programming skills are required. Task complexity can be varied by introducing feedback loops, delayed effects, time pressure, situational uncertainty, adjusting model transparency and changing the relationships between system elements. CODEM creates detailed logs of events and actions essential for cognitive process tracing and the evaluation of decision making effectiveness. The first task designed with CODEM is a counter-insurgency scenario in which a coalition force seeks to stabilize a failing state. A genetic algorithm is used to estimate the best strategy in that scenario for comparison with human results. An adversarial version also allows insurgents to be controlled by a human opponent (or a red team) rather than an artificial agent. CODEM can be used as a cognitive engineering testbed and as a training environment for improving decision making and adaptation skills in complex situations.
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 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".