Optimal Pricing for Selfish Users and Prefetching in Heterogeneous Wireless Networks
Why this work is in the frame
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Bibliographic record
Abstract
Prefetching has been shown to be an effective technique for reducing resource cost and delay in heterogeneous wireless networks. However, in modern wireless local area networks, there is little centralized management, with no control of upper-level functions such as prefetching, and so users are free to behave selfishly. This work focuses on how pricing can be used to control the suboptimality that results from prefetching and selfish users in heterogeneous wireless networks, and how the perceived cost for the user can be optimized. We derive an analytic model to characterize the optimal network and Nash equilibrium prefetching strategies. We present a pricing scheme that optimizes the best achievable perceived cost when the network is in a Nash equilibrium.
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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 it