What would be a revolution in excel is if we could have probability distributions in cells. Right now all spreadsheets are fairly deterministic and we think that tells us something about the real world. Having an ability to make cells distributions (and then all resultant cells would be distributions too) would represent the uncertainty in the world better (and expose lots of interesting things like distributions of tails)
Guesstimate (<a href="https://getguesstimate.com" rel="nofollow">https://getguesstimate.com) has been around for years. I assumed Excel and Google would incorporate its features quickly, but it never seems to have taken off.
Features:
- All cells are named (auto named if you don't set them)
- Cells can have single values, many types of probability distribution, or sample data
- Formulas also produce distributions as first class outputs, because calculation is a Monte Carlo sim
- Each cell also has a pop-up box that invites you to explain your reasoning -- built in documentation
It got so much right. Sample data particularly useful because you can build a probabilistic bottom-up forecast and then update it with real-world data.
First thing that came to mind is: can this model correlation between each cell, or does it assume that all cells are independent?
For example, in this <a href="https://www.getguesstimate.com/models/2205" rel="nofollow">https://www.getguesstimate.com/models/2205 hours of sleep per night is perhaps correlated with hours per weekday commuting. You might not have the data, but if you do, it could improve the probability distribution in the "weekday hours occupied" cell
m101 · · focus · HN ↗
lorenzowood · · focus · HN ↗
Features:
- All cells are named (auto named if you don't set them)
- Cells can have single values, many types of probability distribution, or sample data
- Formulas also produce distributions as first class outputs, because calculation is a Monte Carlo sim
- Each cell also has a pop-up box that invites you to explain your reasoning -- built in documentation
It got so much right. Sample data particularly useful because you can build a probabilistic bottom-up forecast and then update it with real-world data.
nextaccountic · · focus · HN ↗
For example, in this <a href="https://www.getguesstimate.com/models/2205" rel="nofollow">https://www.getguesstimate.com/models/2205 hours of sleep per night is perhaps correlated with hours per weekday commuting. You might not have the data, but if you do, it could improve the probability distribution in the "weekday hours occupied" cell