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An international relations quantitative evaluation model (IRQEM) based on an integrated method.

Yaping Ma1,2, Mengjiao Yao1,2, Feng Yu3

  • 1School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, China.

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|July 11, 2024
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Summary
This summary is machine-generated.

Predicting international relations (IR) is crucial for stability. A novel quantitative evaluation model (IRQEM) integrates multiple methods to forecast bilateral relations, aiding national security decision-making.

Keywords:
Bayesian networkIRQEMcase datainternational relationsnational security decision‐making

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Area of Science:

  • Political Science
  • Quantitative Analysis
  • International Relations

Background:

  • International relations (IR) are inherently uncertain and unstable, leading to significant economic and social consequences.
  • Accurate prediction of bilateral relations is vital for informed decision-making and maintaining development.
  • Existing models often lack the comprehensive approach needed to address the complexity of IR.

Purpose of the Study:

  • To propose a novel quantitative evaluation model for international relations (IRQEM).
  • To enhance the prediction accuracy of bilateral relations, especially during emergencies.
  • To identify critical factors influencing international relations.

Main Methods:

  • Integration of interpretative structural modeling (ISM), Bayesian network (BN) model, Bayesian search (BS), and expectation-maximization (EM) algorithm.
  • Utilizing BN nodes with assigned state values, structured hierarchically by ISM.
  • Construction of the BN model using GeNIe 4.0 with data from 192 cases.

Main Results:

  • The IRQEM effectively evaluates the influence of emergencies on international relations.
  • Critical factors affecting IR can be identified using the proposed model.
  • Prediction of bilateral relations under emergencies is achievable by updating indicator sets.

Conclusions:

  • The developed IRQEM offers a novel approach to quantitative IR research.
  • The model enhances the capability to anticipate threats by optimizing forecasting information.
  • Results provide valuable support for national security decision-making in complex international situations.