Applying Bargaining Game Theory to Web Services Negotiation
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Bibliographic record
Abstract
Service Level Agreements (SLAs) have obvious value for Service-Oriented Computing and have received attention from both academics and industry. However, SLAs still lack a theoretical basis and effective techniques to facilitate automatic SLA establishment. In this paper, we classify negotiations into four types, and focus on the 1-to-1 Web services negotiation between a single service provider and a single service consumer. We make three contributions. Firstly, we represent the 1-to-1 Web services negotiation as a bargaining game. Here, we are interested in a bargain that takes into account the interests of both a service provider and a service consumer, in other words, a fair solution. Secondly, we determine a Nash equilibrium that can be regarded as the fair solution to a two-player bargaining game. We also determine the fair solution to the 1-to-1 Web services negotiation. Finally, we discuss issues that may arise with the 1-to-1 Web services negotiation under credible threats, incomplete information, time constraints, and multiple attributes.
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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 | 0.002 | 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) | 0.001 | 0.004 |
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