Monte Carlo Simulation Challenges: Is Quantitative Risk Analysis objective?

“Quantitative Risk Analysis is Objective!”.

This myth is often promoted by companies selling Monte Carlo Simulation software, training, or consulting services. For example, one vendor’s website states:

‘Qualitative Risk Analysis is Subjective. Quantitative Risk Analysis is Objective.’

🚫 This is a common misconception.
A Monte Carlo Simulation doesn’t make data ‘objective’, it just processes it mathematically.

🧠 Fact: Quantitative Risk Analysis based on subjective inputs is still subjective.

Many people assume:

• Qualitative Risk Analysis = subjective, because it relies on expert judgment (e.g., “high”, “medium”, “low”).
• Quantitative Risk Analysis = objective, because it uses numbers, probability distributions, and Monte Carlo simulations.

💡The fallacy lies in believing that numbers automatically create objectivity.

⚠️ Why This Is Wrong

Quantitative analysis is only as objective as its inputs – the data and assumptions behind it. If probabilities, impacts, correlations, or mitigation effects are based on subjective judgments, then the result, no matter how mathematically elegant,  remains subjective in disguise.

What is even worse, Project Delivery Monte Carlo Simulation Models are often based on speculative, not even subjective, inputs. The lack of well-evaluated three-point estimations forces risk consultants to guestimate uncertainties and probability distributions. 

✅ The Correct View

• Qualitative Risk Analysis: Subjective and openly acknowledged as such. It uses descriptive scales to prioritise risks.
• Quantitative Risk Analysis: Can be objective only if based on reliable empirical or statistically validated data. But if based on subjective estimates (expert opinion, workshop guesses, or analogies), and convenient assumptions (unlimited resources), it remains subjective.

💡 Numbers don’t equal objectivity – evidence does.

💡 Quantitative methods can amplify subjectivity if users assume the results are “scientific” without validating the inputs. The danger is “false precision”: producing charts and probability curves that look objective but are built on personal judgments.

Ask yourself: ‘What is worse, acknowledged subjective analysis or subjective analysis perceived as objective?’ 

💡 It’s not uncertainty that hurts projects. It’s the illusion of certainty.