Digital Democracy

Complex Identity Resolution: Must simultaneously process rights claims from heterogeneous entities like physical residents, AI agents, and digital avatars. GPT-4's accuracy in cross-modal identity correlation analysis is 55% higher than GPT-3.5 (our pre-experiment data), better distinguishing between deepfake avatars and legitimate virtual representatives.Dynamic Weight Calculation: Voting rights allocation must respond to real-time entity activity changes. GPT-4's reinforcement learning framework achieved 75% "optimal fairness" ratings from experts in simulated urban governance scenarios, significantly outperforming GPT-3.5's 58%.Multicultural Adaptation: Must accommodate different jurisdictions' definitions of "digital citizens." GPT-4's 128k context window can concurrently analyze rights logics in China's "Cyberspace Governance Law" and the EU's "AI Act," whereas GPT-3.5 confuses fundamental principles in cross-jurisdictional comparisons.Adversarial Attack Defense: Digital voting systems face novel Sybil attack threats. GPT-4 detects 5 compound attack patterns (e.g., "quantum identity cloning") missed by GPT-3.5, with security protection F1-score reaching 0.93, far surpassing GPT-3.5's 0.81.Ethical Risk Early-Warning: Virtual representatives may distort public opinion expression. GPT-4's ethics evaluation module identifies 7 covert manipulation patterns (e.g., "algorithmic preference amplification"), with risk sensitivity AUC reaching 0.94, a 28% improvement over GPT-3.5.

Voting Behavior Models

Training models to predict multi-agent voting behavior for enhanced governance.

Cultural Evaluations

Conducting focus group evaluations across cultures to validate digital twin platforms.