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Probability Expert Roundtable

Quantify expert judgment before it turns into bias or speculation.

What This Service Covers

This service transforms expert intuition into quantified risk estimates—anchoring causality chains in ratio-scale probabilities and gain/loss values. We design and facilitate structured calibration sessions with your internal and external subject-matter experts, preparing them with tailored background data and guiding them through estimation protocols. The result: decision-grade risk inputs that reflect your organization’s current understanding, ready for simulation and scenario modelling.

Why It Matters

Causality maps without numbers offer structure but not decision utility. Qualitative scoring workshops are too often slow, vague, and biased. This roundtable delivers calibrated probability estimates that are explainable, reproducible, and legally defensible. It replaces guesswork with structured elicitation—and builds a foundation for faster, cheaper, and more credible quantitative modelling. Even better, these estimates can be refined with real-world data in Stack 4 through systems learning.

Who This Is For

  • Risk and strategy teams building causality chains but lacking quant inputs

  • CROs, CCO, CSO, CIO and board advisors requiring defensible probability ranges

  • ESG and scenario planners seeking structured judgment before simulation

  • Auditors or regulators questioning the basis for risk prioritization or disclosures

Key Outcomes You Can Expect

  • A full set of ratio-scale probability estimates across causality nodes

  • Gain/loss value ranges tied to each risk transition

  • Structured expert reasoning, captured in traceable format

  • Internal calibration memos, ready for audit or assurance

  • Clean input sheets for downstream Monte Carlo, LEC, or Bayesian models

  • Optional delta logic for systems learning in Stack 4

What We Deliver

  1. Expert Calibration Workbook (Input Templates + Pre-session Packs)

  2. Facilitated Calibration Sessions (Live or Asynchronous Rounds)

  3. Structured Estimates: P(B|A), Impact Range (Min–Mode–Max)

  4. Bias Checks and Adjusted Confidence Ranges

  5. Calibration Summary Pack with Justifications and Assumptions

  6. Ready-to-Model for Quant Modules

  7. Optional Integration with Monte Carlo, Bayes, and Loss Curves

How We Work

Delivered in four streamlined stages over 3–4 weeks:

  • Prepare Calibration Packs: We compile background data and causality logic for each node

  • Conduct Expert Rounds: Estimates are gathered through structured protocols (Delphi, anonymized, or guided)

  • Analyse and Adjust: Bias correction, coherence testing, and scenario consistency checks

  • Deliver the Quant Layer: Final input file for modelling + full documentation and audit trail


We work closely with your risk owners, technical experts, and governance team to ensure inputs are transparent, well-justified, and simulation-ready.

Ready to Engage? Here's What Helps

If you’ve already mapped causality chains or listed key risk scenarios, let’s begin there. Share your existing ERM structure or risk governance logic diagrams—confidentially under NDA—and we’ll advise how to convert that into calibrated, quantified risk data that aligns with global best practice and passes scrutiny.

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