Evolutionary Game Analysis of the Effects of Problem Size and the Problem Proposing Mechanism on the Problem Processing Mechanism in a New Main Manufacturer–Supplier Collaborative System
Bibliographic record
Abstract
This paper analyzed the effects of the problem size and the problem proposing mechanism on the decision-making processes, for the manufacturer and the supplier, regarding processing a problem in a new main manufacturer–supplier collaborative system using evolutionary game theory. Unpredicted problems may arise in the process of collaborative research and development (R&D) of complex products, like big passenger aircrafts, without any relative advanced contract, and either player will take risks to announce it. In addition to the factors of traditional cost and income, we take another two factors (i.e., the problem size and the problem proposing mechanism) into account in the examination of the problem processing mechanism. With evolutionary game theory applied, we can obtain the stable decision-making states of both players and how these two factors affect the problem processing mechanism. From the result, we find that the problem size has little effect on the two players’ decisions, while the problem processing mechanism has an impact when the experiences or the capacities of the manufacturer and the supplier are unbalanced. This paper contributes to manufacturer and supplier in a newly-established collaborative system to consider how to behave when unpredicted problems come.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".