One piece of the imagined "great scenario analysis tool" I thought was missing was functionality to *easily combine events that have probability distributions*.
I think that the statistical wherewithal to do this is surprisingly rare in our collective analyst's toolkit.
One piece of the imagined "great scenario analysis tool" I thought was missing was functionality to *easily combine events that have probability distributions*.
I think that the statistical wherewithal to do this is surprisingly rare in our collective analyst's toolkit.
For example - let's say two teams each have "new ARR" forecasts that come with Bear/Base/Bull scenarios, and they both tell you their Bull scenarios have just a 10% chance of being met or exceeded.
What's the probability of an "Overall Bull" scenario? (I.e. that new ARR >= the sum of both teams' Bull scenarios.)
Naively we might expect it's 10%^2, if the plans are independent. But this is not the case, since there are many ways we could get to the same overall new ARR. (E.g. a big overhit in one team, and a miss in the other). Plus in reality, the plans are not independent.
I had this question and put it to an actuary friend, for whom the answer was obvious - this type of question is complicated to solve analytically, but easy to solve with a (stochastic) simulation model.
I.e. we input the Bear/Base/Bull probabilities & ARR impacts for each team's plan, define the "correlation factor" between plans if needed, run N simulations, and receive an output in the form of a probability distribution of overall new ARR exceeding certain values.
So would love to throw "easy simulation modelling in an analytics context" into the mix here :)
I love this--I've only done a bit of this type of modeling before and was curious about how to draw this into the mix but intentionally left it out so as not to get too far out over my skis. If you or others have links that folks can learn more about how to do this that would be A++!
Really enjoyed this post!
One piece of the imagined "great scenario analysis tool" I thought was missing was functionality to *easily combine events that have probability distributions*.
I think that the statistical wherewithal to do this is surprisingly rare in our collective analyst's toolkit.
For example - let's say two teams each have "new ARR" forecasts that come with Bear/Base/Bull scenarios, and they both tell you their Bull scenarios have just a 10% chance of being met or exceeded.
What's the probability of an "Overall Bull" scenario? (I.e. that new ARR >= the sum of both teams' Bull scenarios.)
Naively we might expect it's 10%^2, if the plans are independent. But this is not the case, since there are many ways we could get to the same overall new ARR. (E.g. a big overhit in one team, and a miss in the other). Plus in reality, the plans are not independent.
I had this question and put it to an actuary friend, for whom the answer was obvious - this type of question is complicated to solve analytically, but easy to solve with a (stochastic) simulation model.
I.e. we input the Bear/Base/Bull probabilities & ARR impacts for each team's plan, define the "correlation factor" between plans if needed, run N simulations, and receive an output in the form of a probability distribution of overall new ARR exceeding certain values.
So would love to throw "easy simulation modelling in an analytics context" into the mix here :)
I love this--I've only done a bit of this type of modeling before and was curious about how to draw this into the mix but intentionally left it out so as not to get too far out over my skis. If you or others have links that folks can learn more about how to do this that would be A++!
This seems like a good overview: https://www.vosesoftware.com/Monte-Carlo-simulation.php. For Excel there's also https://www.palisade.com/risk/