Analyzing Communities of Web Services Using Incentives
Bibliographic record
Abstract
This paper proposes an effective mechanism dealing with reputation assessment of communities of web services (CWSs) known as societies composed of a number of functionally identical web services. The objective is to provide a general incentive for CWSs to act truthfully. The considered entities are designed as software autonomous agents equipped with advanced communication and reasoning capabilities. User agents request CWSs for services and accordingly rate their satisfactions about the received quality and community responsiveness. The strategies taken by different parties are private to individual agents, and the logging file that collects feedback is investigated by a controller agent. Furthermore, the accurate reputation assessment is achieved by maintaining a sound logging mechanism. To this end, the incentives for CWSs to act truthfully are investigated and analyzed, while the proposed framework defines the evaluation metrics involved in the reputation assessment of a community. In this paper, the proposed framework is described, a theoretical analysis of its assessment and its implementation along with discussion of empirical results are provided. Finally, the authors show how their model is efficient, particularly in very dynamic environments.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".