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A geometric-causal knowledge framework for quantifying and simulating counterfactual credit risk cascades under supply chain shocks
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DOI：10.1016/j.jik.2026.101114.png)
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Abstract
En 中文
Conventional credit risk models often fail to capture the non-linear cascades triggered by supply chain shocks, treating systemic risk within a flat linear Euclidean framework. This study introduces a unified geometric-causal knowledge discovery framework that conceptualizes the supply chain ecosystem as a Riemannian manifold (M,g) . We derive a deterministic information-geometric metric tensor based on Shannon entropy, which warps the state-space to reflect multimodal information density. To validate this framework, we develop a rigorous data generating process (DGP) that simulates a systemic disruption in the Strait of Malacca, modeling the shock as a metric perturbation that propagates through the manifold's topology.
Keywords:
Credit risk
Riemannian geometry
Entropy weight theory
Counterfactual simulation
Supply chain disruption
Supply chain risk analysis
Financial risk modeling
Geometric-causal approach
Geodesic migration
Risk sink effect
G32: Financial Risk and Risk Management, G01: Financial Crises, C45: Neural Networks and Related Topics, C53: Forecasting and Prediction Methods
Simulation Methods, D81: Criteria for Decision-Making under Risk and Uncertainty, L14: Transactional Relationships
Contracts and Reputation
Networks
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