Bibliographic record
Abstract
Competitive Diffusion is a recently introduced game-theoretic model for the spread of information through social networks. The model is a game on a graph with external players trying to reach the most vertices. In this thesis, we consider the safe game of Competitive Diffusion. This is the game where one player tries to optimize his gain as before, while his opponents' objectives are to minimize the first player's gain. This leads to a safety value for the player, i.e. an optimal minimal expected gain no matter the strategies of the opponents. \n\nWe discuss safe strategies and present some bounds on the safety value in the two-player version of the game on various graphs. The results are almost entirely on the safe game on trees, including the special cases of paths, spiders and complete trees but also consist of some preliminary studies of the safe game on three other simple graphs. Our main result consists of a Centroidal Safe Strategy (CSS) Algorithm which suggests a safe strategy for a player on any centroidal tree, a tree which has one vertex as centroid, and gives its associated guaranteed gain.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".