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This tool does a slightly different analysis than @RISK's — we use a different method under-the-hood that can account for interaction effects between variables, whereas I think @RISK performs one-way sensitivities. But it's essentially the same use-case, yes :)

@RISK is pretty cool and does a lot of important things that spreadsheets alone don't do, like working with distributions + monte carlo instead of single values, and sensitivity analysis. It has a pretty steep learning curve, though, and inherits all the issues of the spreadsheet paradigm.




Thanks for the reply. Does your tool assume that the variables are normally distributed for the Monte Carlo analysis you run?

It looks like your tool is closer to TopRank from Palisade in it's functionality?


We actually assume uniform distributions for the inputs of the sensitivity analysis.

This tool is a standalone thing, and you're right — it's similar to TopRank. Our actual product, Causal (https://causal.app) has aspects of @RISK, but packaged in our own (non-spreadsheet) modelling paradigm.


Nice demo! It's perhaps a little confusing if you're using uniform probability distributions for the sensitivity analysis inputs, but normal distributions for the uncertainty analysis (shown at https://causal.app/buy-to-rent).

A previous comment mentions you are using SALib in Python, which can (even if it's not documented) use normal probability distributions for the inputs: here's a notebook with an example: https://risk-engineering.org/notebook/sensitivity-analysis.h...




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