Re: computing with continuous distributions: I recall there was a "show HN" a while ago with a graph-style interface to do computations on continuous distributions, so you could very much budget by modelling a plausible distribution of gas prices within the month, of rental prices in your town, etc, and then see plausible distributions of monthly spending.
I'm still trying to find it: anyone remember this, or did I just Mandela Effect myself? I'm not sure whether it computed outputs analytically or through simulation.
There is nothing free and good in this space, last I checked. The alternatives I know of:
- GetGuesstimate[1] is probably the most polished, but development on it is slow and it doesn't lean into the same interaction patterns that I think make actual spreadsheets popular.
- There are various plugins for the proprietary Microsoft Excel that can do this. I don't remember their names off the top of my head, but sometimes "Monte Carlo" is the phrase that unlocks many searches around this. (Crystal Ball is a name of a plugin that pops into my head.)
- One can hypothetically do this in vanilla spreadsheets, by generating arrays of random values and serialising/deserialising to space-separated strings in a cell. This is very, very slow, though.
- I have started working on something I call Precel[2] which is not very polished but I think the basic idea (if not the current implementation) can be a solid foundation for a proper spreadsheet-for-full-distributions.
I miss GetGuesstimate. It was well-polished and surprisingly powerful for what looked I remember being a prototype. For the scenarios like that example in the article, it would arguably fit better than spreadsheet interface.
Last time I used it was about 3 years ago - I used it to estimate of how much we'll end up spending on renovating the apartment we were planning to move it, and then updated it as the work progressed to make decisions like whether we can afford some optional elements of the plan, if we'll need to get a loan to finish everything, and how much.
Structurally, I basically broken it down by rooms, categories of work and stuff to buy - building materials, furniture. Initially, I just guesstimated (!) the costs based on gut feel, or web search. I'd start with things like: "my mom's apartment had the same proportions and painting it costed $X last year, ours is about Y% larger" -> one node "Painting walls & ceilings except kids room", value = PERT distribution between $X and $X * (1 + Y%) * Fudge factor. After we picked the paint, I'd just split that node into a) labor and b) material, the latter split into surface area (known), bucket cost (known), buckets per sqm (distribution based on values from the back of the bucket) - getting a much narrower probability distribution on the material (and total) cost. Stuff like this for every aspect - I'd just model the things I know.
It was a bit of extra work, but it was instrumental for keeping the costs in check and gave me great peace of mind. It also made it obvious which aspects were driving the costs, which were most risky - wide distributions going into large amounts, which I prioritized pinning down the costs of - and where it's worth to look for savings or alternatives.
Also, it demonstrated the usual case of webapps being optimized for demo examples instead of real use cases. My renovation planner quickly accumulated about a hundred nodes, most of which were computing probability distributions (via ~1k samples Monte Carlo). That slowed the UI down to a crawl, and many times updating a node would create a cascade of errors down the dependency graph, as the diminished performance started surfacing race conditions in the evaluator.
(You may ask, wouldn't it be better to do that stuff in code? Not really - half of the value was in having every part of the math as a node in visual, interactive DAG, that displayed histograms of the probability distributions at every node, so you could just see everything all the time.)
Still, I loved it, and I really wish someone made an actively supported product like this.
@Risk, or specifically their competition, is my prototypical go-to example of "your SaaS product would be much more useful and ergonomic for the user if it were implemented as an Excel spreadsheet".
I remember that as well, so no Mandela effect, and I've been trying to find it too.
It had a graphical interface in which you could specify the probability distribution of an event, and the graph would resolve and show the calculated distributions of all the steps.
I'm still trying to find it: anyone remember this, or did I just Mandela Effect myself? I'm not sure whether it computed outputs analytically or through simulation.
(Hell, I might just recreate it on my own...)