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Record W4414223197 · doi:10.70995/eliy9562

EXPERIENCES OF THE APPLICATION OF WAR GAMES IN THE MILITARY DECISION-MAKING PROCESS

2025· article· en· W4414223197 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicAdvanced Research in Science and Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsMilitary scienceProcess (computing)Military theoryRevolution in Military AffairsSerbianMilitary justiceSet (abstract data type)Operational level of war

Abstract

fetched live from OpenAlex

The military decision-making process is a set of mental and visual activities of an individual and/or a team that leads to optimal solutions to problems, that is, optimal decisions on the engagement of military forces in a specific situation. The military decision-making process itself is carried out using several strictly determined algorithms depending on the level of military organization at which the decision is made. War games are one of the tools within standard operating procedures, as an algorithm for the military decision-making process. The paper presents the results of research related to the experience of applying war games in the military decision-making process in foreign armed forces (The United States of America, The United Kingdom and Canada) and the Serbian Armed Forces. For the purposes of the research, the content analysis method was primarily used. The results obtained during the implementation of the research represent the basis for further research into this issue.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.092

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.401
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it