Author manuscript, published in "International Conference on Game Theory for Networks (Gamenets), Vancouver: Canada (2012)" Achievability of Efficient Satisfaction Equilibria in Self-Configuring Networks
Bibliographic record
Abstract
Abstract. In this paper, a behavioral rule that allows radio devices to achieve an efficient satisfaction equilibrium (ESE) in fully decentralized self-configuring networks (DSCNs) is presented. The relevance of ESE in the context of DSCNs is that at such state, radio devices adopt a transmission/receive configuration such that they are able to simultaneously satisfy their individual quality-of-service (QoS) constraints. An ESE is also an efficient network configuration, i.e., individual QoS satisfaction is achieved by investing the lowest possible effort. Here, the notion of effort refers to a preference each radio device independently establishes among its own set of actions. In particular, the proposed behavioral rule requires less information than existing rules, as in the case of the classical best response dynamics and its variants. Sufficient conditions for convergence are presented in a general framework. Numerical results are provided in the context of a particular uplink power control scenario, and convergence from any initial action profile to an ESE is formally proved in this scenario. This property ensures the proposed rule to be robust to the dynamic arrival or departure of radio devices in the network. 1
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.214 | 0.026 |
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".