Flexible procurement of services with uncertain durations using redundancy
Bibliographic record
Abstract
Emerging service-oriented technologies allow software agents to automatically procure distributed services to complete complex tasks. However, in many application scenarios, service providers demand financial remuneration, execution times are uncertain and consumers have deadlines for their tasks. In this paper, we address these issues by developing a novel approach that dynamically procures multiple, redundant services over time, in order to ensure success by the deadline. Specifically, we first present an algorithm for finding optimal procurement solutions, as well as a heuristic algorithm that achieves over 99% of the optimal and is capable of handling thousands of providers. Using experiments, we show that these algorithms achieve an improvement of up to 130% over current strategies that procure only single services. Finally, we consider settings where service costs are not known to the consumer, and introduce several mechanisms that incentivise providers to reveal their costs truthfully and that still achieve up to 95% efficiency.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".