Multiagent Decision Making in Collaborative Decision Networks by Utility Cluster Based Partial Evaluation
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
We consider optimal multiagent cooperative decision making in stochastic environments. The focus is on simultaneous decision making, during which agents cooperate by limited communication. We model the multiagent system as a collaborative decision network (CDN). Several techniques are developed to improve efficiency for decision making with CDNs. We present an equivalent transformation of CDN subnets to facilitate model manipulation. We propose partial evaluation to allow action profiles evaluated with reduced computation. We decompose a CDN subnet, based on clustering of utility variables. A general simultaneous decision making algorithm suite is developed that embeds these techniques. We show that the new algorithm suite improves efficiency by a combination of a linear factor and an exponential factor.
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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 | 0.005 | 0.001 |
| 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 | 0.001 | 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