Optimal Control of False Information Clarification System under Major Emergencies Based on Differential Game Theory

Area of Science:

• Social Media Analysis
• Information Systems
• Game Theory

Background:

• Major emergencies often trigger the spread of false information on social platforms.
• Effective classification and clarification of misinformation are crucial for public trust and safety.

Purpose of the Study:

• To analyze false information classification systems involving opinion leaders and Internet users.
• To compare decentralized, centralized, and subsidized decision-making models for misinformation management.

Main Methods:

• Construction of three differential game models based on optimal control and differential game theory.
• Comparative analysis and numerical simulations of equilibrium strategies and system benefits.
• Evaluation of optimal trajectories and steady-state values for real information volume.

Main Results:

• Centralized decision-making achieves Pareto optimality for user benefits, system benefits, and real information volume.
• Subsidized decision-making, with opinion leader cost subsidies, offers relative Pareto improvement over decentralized models.
• Optimal equilibrium strategies and benefits differ significantly across the three decision-making models.

Conclusions:

• Centralized decision-making is the most effective strategy for managing false information post-emergencies.
• Cost subsidies can improve misinformation management but do not reach the optimal level of centralization.
• Understanding game dynamics between users and opinion leaders is key to developing robust information systems.