zTrust: Adaptive Decentralized Trust Model for Quality of Service Selection in Electronic Marketplaces
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 present an adaptive decentralized trust formalization well suited for electronic commerce. Our model, called zTrust , 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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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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