Asynchronous Consensus Evolution Mechanism for Large Group Emergency Decision Making: Risk Mitigation Strategy Selection Under Uncertainty
Bibliographic record
Abstract
Supply chain disruptions pose substantial risks to the system-on-chip supply chain (SoCSC) within the electric vehicle (EV) industry, potentially resulting in production delays and financial losses. This study proposes a novel asynchronous consensus evolution mechanism (ACEM) designed to enhance large group emergency decision-making (LGEDM) under uncertainty, with specific application to the EV SoCSC. Unlike traditional synchronous approaches, ACEM enables decision makers (DMs) to contribute asynchronously, reducing wait times and accelerating consensus formation. The mechanism integrates uncertain scenario analysis with an optimization framework that dynamically allocates decision steps with relative weights, ensuring adaptability to complex and dynamic environments. We further develop a time-aware adaptive clustering (TAAC) algorithm to segment DMs based on decision quality and response speed, enhancing both the speed and the accuracy of consensus building. Simulation results indicate that ACEM significantly reduces decision latency and improves consensus efficiency under uncertain disruption scenarios. This work provides a robust framework for agile decision-making, enabling manufacturers to enhance SoCSC resilience in uncertain disruptions.
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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 | 0.000 |
| 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 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".