Bibliographic record
Abstract
In order to succeed, it is imperative that all e-commerce systems include an effective and reliable trust and reputation modeling system. This is particularly true of decentralized e-commerce systems in which autonomous software engage in commercial transactions. Many researchers have sought to overcome the complexities of modeling a subjective, human concept like trust, resulting in several trust and reputation models. While these models each present a unique offering and solution to the problem, several issues persist. Most of the models require direct experience in the e-commerce system in order to make effective trust decisions. This leaves new agents and agents who only casually use the e-commerce system vulnerable. Additionally, the reputation ratings of agents who are relatively new to the system are often indistinguishable from scores for poorly performing agents. Finally, more tactics to defend against agents who exploit the characteristics of the open, distributed system for their own malicious needs are required. To address these issues, a new metric is devised and presented: seniority. Based on agent age and activity level within the e-commerce system, seniority provides a means of judging the credibility of other agents with little or no prior experience in the system. As the results of experimental analysis reveals, employing a reputation model that uses seniority provides considerable value to agents who are new agents, casual buyer agents and all other purchasing agents in the e-commerce system. This new metric therefore offers a significant contribution toward the development of enhanced and new trust and reputation models for deployment in real-world distributed e-commerce environments.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".