Dynamic Parameter Calibration Framework for Opinion Dynamics Models

Area of Science:

• Computational Social Science
• Mathematical Modeling

Background:

• Opinion dynamics models predict public opinion trends but suffer from deviations due to external factors and accumulated errors.
• Existing models struggle with real-time adaptation and error correction.

Purpose of the Study:

• To develop a dynamic framework for calibrating opinion dynamics models.
• To improve the accuracy of public opinion prediction by addressing model limitations.

Main Methods:

• A hybrid approach combining a genetic algorithm and a particle filter algorithm was developed.
• A fitness function was designed to match model parameters with initial public opinion observations.
• Particle distribution was used to track the opinion dynamic system's state with successive observations.

Main Results:

• The proposed framework dynamically calibrates opinion dynamics model parameters.
• Testing on typical models demonstrated improved prediction accuracy.
• The method effectively reduces deviations caused by external factors and random errors.

Conclusions:

• The dynamic calibration framework significantly enhances the predictive power of opinion dynamics models.
• This approach offers a more robust method for forecasting public opinion trends.
• The integration of genetic algorithms and particle filters provides a powerful tool for adaptive modeling.