Cluster‐based improvement rates for trust establishment models in single or distributed multi‐agent systems
Bibliographic record
Abstract
Abstract Intelligent agents within open and dynamic multi‐agent systems are becoming increasingly capable in their decision‐making abilities and rely upon the notion of trustworthiness to determine which agents to interact with. To improve the overall performance of trust establishment models which trustees individually select and equip to improve their trustworthiness with trustors, while balancing the resources being spent, a cluster‐based trust establishment model update mechanism is proposed. This cluster‐based approach is applicable to robust trust establishment models which utilize dynamic improvement and disimprovement rate variables to adjust a trustee's behaviors toward trustors to improve or maintain trust with the trustor. By storing a single trust establishment model's dynamic improvement and disimprovement rate variables independently for each trustor and by clustering similar trustors together based on observed experiences, a model can more accurately update a trustee's behaviors toward trustors. Through simulated experiments comparing the performance of the existing integrated trust establishment (ITE) model with and without the cluster‐based approach, with varying trustor to trustee ratios to diversify the agent behaviors, the cluster‐based approach consistently improves a trustee's ability to fully meet a trustor's needs, for less resources than ITE, while minimizing the corresponding impact to the trustee's overall trust.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".