Systems Learning & Causal Feedback
From fixed models to feedback-driven foresight.
What This Service Covers
This service helps you transform expert-driven risk maps into self-correcting, evidence-based systems. We use observed events to test and refine your causality assumptions—comparing forward predictions to real outcomes using Bayesian feedback logic. When discrepancies emerge, we recalibrate probabilities, improve model accuracy, and expose where your risks behave differently than expected.
This is how simulations evolve from theoretical to decision-grade: each outcome tightens your model.
Why It Matters
Static assumptions lose value quickly in volatile environments. Investors, boards, and regulators now expect your risk models to adapt—to learn. Whether you’re simulating operational failure, policy response, or financial exposure, systems that don’t recalibrate will miss material risks. ISSB and modern ERM frameworks favour approaches that evolve with empirical evidence.
If your P(B|A) keeps failing to predict reality, we help you fix it—quantitatively, not subjectively.
Who This Is For
Organizations running risk, scenario, or strategy models
Firms that rely on expert-driven causality maps and forecasts
Companies preparing ISSB-compliant risk disclosures under IFRS S1/S2
Risk, audit, sustainability, and data teams seeking adaptive modelling frameworks
Key Outcomes You Can Expect
A live feedback mechanism to test and correct risk propagation logic
Recalibrated causality probabilities (P(B|A)) based on real-world evidence
Bayesian diagnostics revealing which assumptions hold—and which don’t
A tighter, evidence-aligned causality map over time
Smarter simulations that learn, adjust, and improve decision quality
What We Deliver
Reconstructed Causal Map from Track 1 with traceable event nodes
Expert Probability Register (P(B|A) inputs and rationales)
Bayesian Feedback Engine
P(A|B) Derivation Logs and Recomputed P(B|A) Tables
Causality Drift Report (Where Expert Assumptions Diverge from Reality)
Updated Simulation Model with Calibrated Propagation Parameters
Board Explainer Deck: “What Changed, and Why It Matters”
How We Work
We follow a structured 4-phase engagement model over 6–10 weeks, adapted to your model maturity:
Trace the Causality: Map out event paths and expert probability assumptions
Inject Observations: Identify where B has occurred and extract observed P(B)
Run Feedback Loop: Apply Bayesian recalculation to derive P(A|B), then re-test P(B|A)
Refine the Model: Adjust propagation strength, log evidence, and issue updated simulation outputs
All analysis is documented, traceable, and designed to integrate with your existing simulation, ERM, or scenario-planning infrastructure.
Ready to Engage? Here's What Helps
If you can share your latest causality map, simulation logic, or expert-driven P(B|A) estimates, we’ll help you test which ones are holding up—and which need recalibration. NDA-ready and platform-agnostic. Just bring your model—we’ll help it learn.