Decision-Making in Wargames: An E-CARGO Perspective
Bibliographic record
Abstract
In the field of management research, complex decision-making scenarios are frequently encountered. Intelligent decision-making games serve as an essential tool for simulating such scenarios, enabling decision-makers to evaluate strategies and allocate resources more effectively. However, traditional intelligent decision-making games relying on deep reinforcement learning (DRL) often suffer from prolonged training times, convergence difficulties, and challenges in multiagent coordination. To address these limitations, this study proposes an enhanced game framework that integrates role-based collaboration (RBC) with the environment, class, agent, role, group, object (E-CARGO model). In this framework, agents are first assigned different roles, after which reinforcement learning (RL) techniques are applied for policy training. Simulation experiments conducted on the winning-first platform demonstrate that the proposed method achieves superior convergence performance and agent intelligence compared with conventional RL approaches, effectively mitigating the identified challenges and enhancing overall decision-making 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 | 0.000 | 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 | 0.001 |
| 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".