Under Uncertainty Trust Estimation through Unknown Agents, in a Multi-valued Trust Environment
Bibliographic record
Abstract
In a world where an increasing number of transactions are made on the web, there is a need for a trust evaluation tool dealing with uncertainty, e.g., for customers interested in evaluating the trustworthiness of an unknown service provider throughout queries to other customers of unknown reliability. In this paper, we propose to estimate the trust of an unknown agent, say aD, through the information given by a group of agents who have interacted with agent aD. This group of agents is assumed to have an unknown reliability. In order to tackle the uncertainty associated with the trust of unknown agents, we suggest to use possibility distributions. We introduce a new certainty metric to measure the degree of agreement of the information reported by the group of agents about agent aD. Fusion rules are then used to estimate the possibility distribution of agent aD's trust. To the best of our knowledge, this is the first paper that estimates trust, out of empirical data, subject to some uncertainty, in a discrete multi-valued trust environment. Numerical experiments are presented to validate the proposed tools.
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".