zTrust: Adaptive Decentralized Trust Model for Quality of Service Selection in Electronic Marketplaces
Bibliographic record
Abstract
We present an adaptive decentralized trust formalization well suited for electronic commerce. Our model, calledzTrust, constitutes two essential elements. The first is the adviser modeling mechanism that enables consumer agents to merge the cognitive and the probabilistic views of trust and adaptively calculate the trustworthiness of advisers according to environmental conditions, information availability, and participants' behavioral dispositions. Using this mechanism, consumers are able to form their social network consisting of the most reliable advisers. The second element is a trust‐oriented service selection framework that models the qualification and trustworthiness of providers in delivering the multiattribute products and adopts a procurement auction model to choose the most pertinent provider that meets a consumer's quality of service requirements. We give a formal description of our approach and validate it with simulations demonstrating that our solution yields high‐quality results under various realistic conditions. Experimental results indicate that the zTrust model can be effectively employed in dynamic agent‐oriented e‐commerce applications.
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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 | 0.003 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".