Socially Optimal Correlated Equilibrium in Class-Anonymous Offloading Game with Computing Access Points
Bibliographic record
Abstract
We consider a multi-user mobile offloading network with multiple computing access points (CAPs). Each user has one task to be processed, and may choose to reduce the cost of processing the task by offloading it to a CAP or to a remote cloud server. Each user belongs to one of a fixed number of classes, which determines the distribution of their task parameters. We aim to produce an offloading decision that minimizes the expected social cost of the system, while giving selfish users an incentive to follow that decision. Towards that goal, we show that our system can be formulated as a class-anonymous game, and we derive the reduced form of this game to prove that a socially optimal correlated equilibrium (CE) can be computed in polynomial time and space with respect to the number of users. Like the Nash Equilibrium, the CE maintains the necessary conditions for stability in a system with rational and selfish users, while being much easier to compute for non-potential finite games. Simulation results demonstrate the superior results of our solution when compared with random mapping and an alternate means of computing a CE.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".