Monte Carlo Simulation vs. SimDec

Monte Carlo Simulation vs. SimDec: A Practical Comparison for Better Decision Analysis

Monte Carlo simulation (MC) has been the workhorse of quantitative risk and uncertainty analysis for decades. It answers the core question: “Given uncertain inputs, what is the distribution of possible outputs?”
SimDec (Simulation Decomposition), in contrast, extends this capability by revealing why the outputs look the way they do — and what you can do about it.

This post compares the two methods from a practical, decision-making perspective and explains how SimDec can be added on top of any Monte Carlo model with almost no extra effort.

What Monte Carlo Simulation Gives You

A standard MC run outputs a histogram of simulated results. From this, you can extract:

• Shape of the distribution (skewness, tails)
• Expected mean
• Minimum and maximum
• Standard deviation
• Percentiles
• Value at Risk (VaR) and other risk metrics if in financial context

This is useful: you understand how spread out the results are and how likely certain outcomes appear.

But MC answers only one structural question:

“What is the value/output, and how uncertain is it?”

You gain distributional insight — but not insight into which inputs matter, how they matter, or what to change in order to reach better outcomes.

That is where SimDec comes in.

What SimDec Adds on Top

SimDec starts from the same simulated dataset but gives you a deeper, actionable perspective.

You still get:

• The full distribution
• All classical statistics
• VaR, percentiles, moments, range
• Any metric from the histogram

But you also see:

1. Which inputs actually matter

SimDec decomposes the variability of the output into contributions from each input — simultaneously and interactively.

2. How each input affects the output

You can visually compare how different input levels shift the output distribution.
No more guessing, no more interpreting correlation stats.

3. Totally new questions become answerable

Once you see how each uncertain input influences the result, you can ask:

• “What needs to be done for the output to be in a desired range?”
• “What conditions should we avoid to reduce downside risk?”
• “What combination of drivers leads to the best outcomes?”

This directly connects uncertainty analysis with strategy, planning, and policy design.

Implementation: Nothing to Change in Your Model

To use SimDec, you don’t need to rebuild your model or change your workflow.
Just:

1. Record the input values alongside the output during your Monte Carlo simulation.
   (Most tools already do this or allow one-line modifications to enable it.)
2. Upload the .csv dataset to SimDec dashboard at https://simdec.io‍
3. Automatic, free, open-source decomposition will appear itself! Plus many options to play around

No matter where your original model is, in spreadsheets, Python, R, specific simulation software, as long as you can get an input–output simulation dataset, you can analyze it with the dashboard. If your model is done in Python, R, Matlab, or Julia, you can directly use SimDec functions in a corresponding language.

Learn SimDec in 5 Minutes

(A short, practical walk-through showing how to go from MC data to full decomposition.)

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