Bibliographic record
Abstract
Although much of the research into massively multiplayer online games (MMOGs) focuses on scalability concerns, other issues such as the existence of cheating have an equally large practical impact on game success. Cheat prevention itself is usually addressed through the use of proprietary, ad-hoc or manual methods, combined with a strong centralized au-thority as found in a straightforward client/server network model. To improve scalability, however, the use of more extensible, yet less secure, peer-to-peer (P2P) models has become an attractive game design option. Here we present the IRS hybrid game model that efficiently incorporates a centralized authority into a P2P setting for purposes of con-trolling and eliminating game cheaters. Analysis of our de-sign shows that with any reasonable parametrization mali-cious clients are purged extremely quickly and with minimal impact on non-cheating clients, while still ensuring contin-ued benefit and scalability from distributed computations. Cheating has a serious impact on the viability of multiplayer games, and our results illustrate the possibility of a system in which scalability and security coexist.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".