Estimating force mix lower bounds using a multi-objective evolutionary algorithm
Bibliographic record
Abstract
Nations will always experience conflicting pressures to reduce both (i) the funding of militaries and (ii) the probability that they will not be able to respond to scenarios that may arise. We develop a multiobjective evolutionary algorithm (MOEA) to generate force mix options that trade-off between lower bounds for objective (i) versus objective (ii). A set of military assets or force mix is evaluated against multiple instances of the future, each composed of a mix of stochastically generated realistic scenarios based on historically derived parameters. Scenario success is evaluated by matching each occurrence with a course of action (CoA) whose force element (FE) demands can be met. The lower bound on (i) comes from the assumption that a nation has complete flexibility to engage in scenarios at times that minimize simultaneous demand on FEs. The results are compared with the results from Tyche, a discrete event Simulator, which provides an more realistic, though pessimistic, point estimate of objective (ii). Results confirm the expected relative behavior of both models.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".