The colonel blotto game based on probability and statistics
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.
Bibliographic record
Abstract
The Colonel Blotto Game is famous as zero-sum game. The game asked players to get more passes(objects) than their opponents to win the game with limited regiments(resources). The one who put more regiments on the pass would get it, and player who has more passes would win the game. The Colonel Blotto Game could be used in athletics, business and competition in other forms: how the player uses specific amount of resource with strategies to gain more benefits than competitors. In this case, the Colonel Blotto Game could be seen transfer to a linear program problem, with constraints about limited resources to maximize what players get in the game. This article would analyze the strategy for the Colonel Blotto game in probability of winning and build the model by extending the Colonel Blotto Game with more regiments, more passes and weighted some passes to look for how these variables impact each other and find the general solution for this game. Then using linear program to check the final results. This article would focus on the resources, benefits and weighted of the benefits for the Colonel Blotto game to find out the directly relationship among these variables of the model with the strategy to win the game.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it