What question did this study set out to answer?

The aim is to present a framework for modeling project risks as interdependent chains to improve risk management.

May 3, 2026Open Access

Probabilistic Chain Analysis (PCA): A Framework for Running Projects in a Probabilistic World

Key Points

The aim is to present a framework for modeling project risks as interdependent chains to improve risk management.
Introduces the Probabilistic Chain Analysis™ methodology.
Validates PCA on 16,000+ projects across 8 infrastructure sectors.
Implements PCA in RiskPulseV12, featuring an MCMC upgrade.
PCA shows a validated risk quantification method through cascade amplification.
Demonstrated effective risk modeling verified with R̂ < 1.1 in MCMC.
Improvements documented for schedule-cost separation in project management.

Abstract

This whitepaper introduces Probabilistic Chain Analysis™ (PCA) — a risk methodology that models project risks as dependent chains rather than independent events. PCA quantifies cascade amplification through Bayesian lift factors and builds institutional memory through cross-project posterior updating via full Metropolis-Hastings MCMC (R̂ < 1.1 verified). Validated on a UK Highways Survey & Investigation scenario across 16,000+ projects in 8 infrastructure sectors. Implemented in RiskPulseV12 — a Bayesian Monte Carlo risk engine. Version 1.1: Intelligence Log MCMC upgrade, schedule-cost separation-with- conjunction methodology documented.

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