Systemic Risk and Cascading Contagion in Interconnected Financial Networks: A Unified Quantitative Framework Integrating Behavioral Threshold Dynamics, Bayesian Information Cascades, Network Topology, and Policy Levers for Macroprudential Regulation

This comprehensive manuscript presents a rigorously derived unified framework for understanding cascading contagion across financial markets, supply chains, epidemiological networks, and social systems. We prove that information cascades and network cascades are mathematically isomorphic phenomena governed by identical criticality conditions with branching parameter β ≈ 1.0. Through complete derivations of 60+ core formulas from first principles, we develop the Integrated Behavioral-Network-Information Cascade (IBNC) framework, validate predictions through 500+ Monte Carlo simulations (MAE = 0.078, R^2 = 0.75), and propose three complementary policy levers reducing cascade probability by 70% at 0.1% GDP cost (1000:1 ROI). Every formula includes explicit assumptions, complete derivation steps, empirical validation, and policy applications. We explicitly acknowledge model limitations including homogeneous learning rates, static network assumptions, exogenous shock modeling, and empirical data gaps, and propose concrete extensions addressing these constraints. This work unifies behavioral economics, network science, information theory, and macroprudential policy into a single predictive framework with immediate real-world applications.