Multi-Agent Resource Allocation (MARA) for modeling construction processes
Why this work is in the frame
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Bibliographic record
Abstract
Multi-Agent Resource Allocation (MARA) is a field developing solutions to the problem of distributing a number of resources amongst multiple agents. This field has inter-disciplinary characteristics and relates to a wide range of applications, such as industrial procurement, scheduling and network routing. Many construction operations involve entities sharing and competing for limited resources. The decision to allocate these resources to entities usually has a significant impact on the schedule and cost of these operations. The dynamic and continuously changing nature of construction operations justifies the need for decision support tools with high adaptability and handling of uncertainty which is featured by MARA. This paper presents the main elements and techniques in MARA and discusses a sample case applying these techniques for the modeling of industrial construction assembly processes, also presents the conceptual model of the sample case and a prototype implementation of that model using Repast multi-agent simulation package.
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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 | 0.000 | 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 it