Social Network-Based Trust for Agent-Based Services
Why this work is in the frame
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Bibliographic record
Abstract
In service-oriented environments, reputation-based service selection is gaining increasing prominence. We propose in this paper a social network-based approach to model and analyze trust when a given service, called customer service or customer, should select another service, called provider service or provider, in a composition scenario. Trust is modeled as a game between customer and provider services and represented in the network through two types of nodes and labelled edges linking customer nodes to each other and customer nodes to provider nodes. To analyze the different situations using a game-theoretic and mechanism design representation, each service is associated to a rational agent where decisions are based on the gaining utilities. This allows us to capture, assess and analyze the possible strategies in such a game. An overall trust assessment is provided and some interesting properties are discussed. Some simulation results are also presented.
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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 | 0.000 | 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 | 0.001 | 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