User information fusion decision making analysis with the C-OODA model
Bibliographic record
Abstract
For pragmatic information fusion system design and analysis, the user (commander or operator/analyst) needs information in a timely manner to conduct actionable intelligence. With the development of complex information fusion systems, the user still provides valuable inputs to the information fusion system in contextual reasoning and situation understanding. In this paper, we describe the Cognitive Observe-Orient-Decide-Act (C-OODA) model as a method of user and team analysis in the context of the Data Fusion Information Group (DFIG) Information Fusion Model. From the DFIG model [as an update to the Joint Directors of the Lab (JDL) model], we look at Level 5 Fusion of “user refinement” in the context of timely decision making. Using control theory, we present an example of user timeliness assessment in an information fusion decision making model analysis. We model the information input delays in reaching a decision and the action output delays in executing the decision. The C-OODA comparisons to the DFIG model support systems evaluation and analysis as well as coordinating the time interval of interaction between the machine processing (e.g. information fusion) and user processing (e.g. perception and reasoning).
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".