Symmetric probabilistic values for identifying informative sensors
Bibliographic record
Abstract
In this paper, we show how the notion of symmetric probabilistic values from cooperative game theory can be used in a sensor network to identify the sensors that are relatively more informative than others. We note that parameter estimation in a sensor network can be modeled as a cooperative game, where a metric of estimation accuracy assigns a value to each subset of sensors. Symmetric probabilistic values are then known to be indicators of the relative power of players in cooperative games. Motivated by this, we define a power index for sensors based symmetric probabilistic values. While generally any metric of estimation accuracy can be used for computing power indices, it is noted that by choosing the determinant of the Fisher information matrix, the computational complexity associated with power indices gracefully increases with the number of sensors. The formulas are explicitly provided for computing the Banzhaf value and the Shapley value, two well-known symmetric probabilistic values. A target whose parameter is being estimated by the sensor network can use power indices to identify and act against the informative sensors. As an important application in this regard, the power indices of sensors are computed in bearings-only and range-only target localization.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".