Cross-Vertical Intelligent Network Systems (CVNIS) Optimization Model for Multi-Dimensional Distributed Decision Making through Cognitive Reinforcement Learning

Mr. Sandeep Bhattacharjee

doi:https://doi.org/10.17492/computology.v5i2.2502

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Cross-Vertical Intelligent Network Systems (CVNIS) Optimization Model for Multi-Dimensional Distributed Decision Making through Cognitive Reinforcement Learning

Sandeep Bhattacharjee

https://doi.org/10.17492/computology.v5i2.2502

Published Online: December 11, 2025

Author Details ( * ) denotes Corresponding author

1. * Sandeep Bhattacharjee, Assistant Professor, Amity business school, Amity University, Kolkata, West Bengal, India (sandeepbitmba@gmail.com)

Multi-dimensional decision making is not only complex but also difficult to interpret in business decision making, given the nature of the dynamicity of interacting variables in the environment. The proposed cognitive modeling framework integrates cross-vertical intelligent network systems and reinforcement learning to capture complex inter-domain dependencies. Results from analysis of five domains of Energy, Health, Traffic, Infrastructure and Market using heatmaps, correlation matrices, radar charts, and t-SNE indicate signals at domain-specific activations using heatmaps, minimal inter-domain redundancy using correlation matrix (~0.02), elevated activity observed in Traffic and Industrial sectors using radar chart and oscillatory patterns with a positive upward trend with proximal policy optimization. The framework with high-dimensional GNN representations enables a comprehensive, robust, scalable, and adaptive method for decision making with refined policies for reward-stress patterns and managing dependencies among different domains to enable better-informed business decision making.

Keywords

Cognitive graph modeling; Cross-sectoral policy decision-making; Graph Neural Networks (GNNs); Reinforcement learning optimization; Multi-domain adaptive systems; Systemic risk and resilience

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