Saturday, June 21, 2025
SOC Models for Insurance: Predicting $10B+ Catastrophic Claims Before They Cascade
Hurricane Katrina: $125 billion. Fukushima: $210 billion. COVID-19 supply chain failures: $4 trillion. What if your actuarial models could predict these catastrophic cascade events 6-18 months before they devastate your loss ratios? SOC models make it possible.
Traditional insurance models treat risks as independent events with normal distributions. But catastrophic losses follow Self-Organized Criticality patterns—where small triggers cause massive, correlated failures across interconnected systems. Discover how SOC models provide 86% improved accuracy for extreme events and enable $127M average annual loss avoidance per major insurer.
Explore Insurance SOC Models
Saturday, June 21, 2025
When Airports Fail Like Sandpiles: SOC Models for Aviation Network Resilience
When Chicago O'Hare shuts down due to severe weather, the cascade of delays doesn't just ripple—it avalanches across the entire U.S. aviation system. What if we could predict these system-wide failures with the same mathematical precision we use to model sandpile avalanches?
This research demonstrates how Self-Organized Criticality models reveal the hidden patterns governing aviation network failures. Watch the 60-second animation showing how airport disruptions cascade through the U.S. aviation network exactly like sandpile avalanches, and discover how SOC mathematics enables predictive modeling of system-wide aviation disruptions.
Explore Aviation SOC Models
Saturday, June 21, 2025
The Sandpile That Explains Everything: Why Critical Infrastructure Needs SOC Models
What if I told you that a simple pile of sand could predict when your power grid will fail, your supply chain will collapse, or your transportation network will cascade into chaos? The mathematics of sandpiles reveals the hidden patterns governing every critical system around us.
This interactive exploration demonstrates how Self-Organized Criticality emerges in complex networks, showing infrastructure engineers why SOC models are essential for understanding cascade failures in power grids, transportation systems, and communication networks. Watch the 35-second sandpile animation that explains the fundamental dynamics of all critical infrastructure.
Watch Interactive Demo
Thursday, June 7, 2025
SOC Infrastructure Risk Assessment Tool - Comprehensive User Manual for Policymakers
A complete policy-maker's guide to using our Self-Organized Criticality Infrastructure Risk Assessment Tool. This comprehensive manual enables government officials, emergency managers, and international organizations like the UN to assess cascading failure risks in critical infrastructure networks and make evidence-based decisions for resilience investments.
The manual covers everything from network building and disaster scenario design to interpreting results for policy decisions, with specific guidance for UN Sustainable Development Goals alignment, National Adaptation Plans, and international standardization frameworks.
Access User Manual
Monday, March 24, 2025
How the Self-Organized Criticality Model Could Have Anticipated Hurricane Sandy's Cascade
What if we could rewind to October 2012 and estimate exactly how Hurricane Sandy would likely trigger a cascade of failures across critical infrastructure? This groundbreaking analysis demonstrates how Self-Organized Criticality models transform theoretical insights into probabilistic forecasting power.
Building on the foundational work of Pescaroli and Alexander's cascading disaster framework, this research shows how SOC models can recreate Hurricane Sandy's cascade patterns with remarkable accuracy—but with the crucial advancement of providing probability estimates before disasters strike.
Read More
Saturday, March 15, 2025
A Self-Organized Criticality Model of Extreme Events and Cascading Disasters in Critical Infrastructure Networks
Critical infrastructure networks face increasing threats from extreme weather events and cascading disasters. When Hurricane Maria struck Puerto Rico in 2017, initial power grid failures triggered a cascading collapse that left the entire island without electricity for months, revealing fundamental limitations in traditional failure analysis.
This research develops a comprehensive SOC model that captures how extreme events trigger cascading failures through network amplification effects, enabling predictive analysis of disaster scenarios and evidence-based resilience planning for climate adaptation.
Read More
Wednesday, March 5, 2025
Self-Organized Criticality (SOC) in Network Cascade Failures
Our latest research examines how self-organized criticality emerges in complex interconnected networks under medium retention scenarios. The cascade failure patterns reveal striking power-law distributions characteristic of SOC systems.
This visualization-driven analysis demonstrates how Delta-Inspired Hub-and-Spoke networks exhibit threshold behaviors where small initial perturbations can lead to system-wide cascade failures, with important implications for infrastructure resilience planning.
Read More
Friday, Feb 28, 2025
Generalizing the Classical Risk Formula: A Spatial Latent Process Approach
The classical risk formula expresses damage as a product of asset value and hazard-dependent vulnerability, has served as the foundation of natural hazard risk assessment for decades. While this multiplicative approach has provided valuable insights, the complexity of damage mechanisms suggests the need for a more comprehensive framework.
This research introduces a generalized risk formula that integrates spatial latent processes to capture complex interactions between assets and hazards that the classical multiplication cannot represent.
Read More
Sunday, Jan 27, 2025
Multi-Hazard Bayesian Hierarchical Model for Damage Prediction
A fundamental theoretical limitation undermines current disaster risk models. Existing approaches suffer from two critical constraints: predominantly deterministic models with fixed parameters, and an assumption of hazard independence that contradicts the reality of cascading and compound disasters.
This work develops the Multi-Hazard Bayesian Hierarchical Model (MH-BHM), which reconceptualizes the classical risk equation as a fully probabilistic model that accommodates hazard interactions.
Read More