Reinforcement Learning-Based Optimization and Simulation Modeling of International Relations Interaction Strategies
Bibliographic record
Abstract
Analyzing the interaction strategies of international relations helps to understand and predict changes in the international landscape, so as to develop and optimize international interaction strategies.Firstly, a single-layer multi-temporal network is modeled for political events, scientific cooperation and international trade in international relations, and added to a multi-layer aggregation network.On this basis, a simulation and analysis method for simulating international relations interaction strategies based on deep learning and multi-intelligence body reinforcement learning methods is proposed.Applying the method of this paper to the arithmetic simulation analysis, it is found that the international relations in the last 10 years have shown the small-world characteristics, and cooperation and conflict coexist.Economic dependence is an influencing factor of conflict between two countries, when the economic and trade links are close, the two countries are less prone to conflict, so the optimization of the international relations interaction strategy should focus on the economic and trade relations.
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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 | 0.001 |
| 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 | 0.001 |
| 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".