Network-Enabled Missile Deflection: Games and Correlation Equilibrium
Bibliographic record
Abstract
The problem of deploying countermeasures (CM) against antiship missiles is investigated from a network centric perspective in which multiple ships coordinate to defend against a known missile threat. Using the paradigm of network enabled operations (NEOPS), the problem is formulated as a transient stochastic game with communication where the appropriate strategy takes the form of an optimal stationary correlated equilibrium. Under this strategy, ships cooperate through real-time communication to satisfy both local and collective interests. The use of communication results in a performance improvement over the noncommunicating, Nash equilibrium scenario. This framework allows us to develop a theoretical foundation for NEOPS and captures the trade-off between information exchange and performance, while generalizing the standard Nash equilibrium solution for the missile deflection game given in [1], The NEOPS equilibrium strategy is characterized as the solution to an optimization problem with linear objective and bilinear constraints, which can be solved calculating successive improvements starting from an initial noncooperative (Nash) solution. The communication overhead required to implement this strategy is associated with the mutual information between individual action probability distributions at equilibrium. Numerical results illustrate the trade-off between communication and performance.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".