Use of non-monotonic utility in multi-attribute network selection
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
Network convergence across different access technologies holds a promise of enabling ubiquitous service availability but faces several technical challenges. With anticipated proliferation of multimode IP devices, the optimal selection of a service delivery network among multiple IP based wireless access alternatives is one of the important issues that is actively studied and discussed in several standardization forums. Use of multi attribute decision making (MADM) algorithms has been proposed in the past for network selection decisions in a heterogeneous wireless network environment. A direct comparison of these algorithms is difficult as this would require the use of another MADM algorithm. A better approach instead is to ascertain the appropriateness of the algorithm to the problem space. This paper provides the basis for evaluating the appropriateness of MADM algorithms for network selection. It analyzes the use of MADM algorithms such as TOPSIS, ELECTRE and GRA for network selection and argues that GRA provides the best approach in scenarios where the utilities of some of the attributes are non-monotonic. The paper proposes a novel stepwise approach for GRA that uses multiple reference networks and explains its working with network selection scenarios.
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 | 0.001 |
| 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 it