How suitable do you think this is for CPU-intensive work? I'm interested in having servers for scientific-computational work, which would be rather CPU-heavy. It would be great to offload some of this to a nearby browser for bits and pieces that desire low-latency.
Strongly thirded. I'm not sure I agree with absolutely everything they say, but overall I think they have a far more pragmatic and honest set of answers than any competing advice I hear. For disclosure, I worked there for a year.
The EA worldview takes some getting used to though.
Please do so. The UI is all open-source react, so you may be able to copy some components directly if you wanted. I'd be happy to help people out with this if you have requests.
You can copy & paste an array of samples and Guesstimate will sample from that cluster. For instance, try pasting the following into the value field of a cell:
[1,1,1,2,2,2,2,3,3,3,3,3,4,4,4,4,4,7,7,7,7]
You can use tools like distshaper6 to generate arbitrary distributions, then copy the samples into Guesstimate.
On the question of "what are other ways of doing MC analysis", there are two approaches.
The first is to use Excel apps like Oracle Crystal Ball or @Risk. These are aimed at business analysists. They're pretty expensive, but also quite powerful.
The other option is to use probabilistic programming languages. Stan and PYMC3 are probably the best now, but hopefully, some others will become much better in the next few years.
That said, this is a pretty small space. The main "business competitor" is probably people just using google sheets or Excel without distributions to make models.
You can choose from a few distributions (normal, lognormal, uniform) in the main editor, or you can type many others using the function editor. The sidebar describes all of them.
In my experience, many consequential business decisions aren't even made with probability distributions, let alone probabilistic models with realistic correlations. I would generally encourage people who are comfortable with more advanced probabilistic systems to use them.
I'd definitely agree that correlations can be a really big deal, especially in very large models like that one.
Guesstimate doesn't currently allow for correlations as you're probably thinking of them. However, if two nodes are both functions of a third base node, then they will both be correlated with each other. You can use this to make somewhat hacky correlations in cases where there isn't a straightforward causal relationship.
Implementing non-causal correlations in an interface like this is definitely a significant challenge. It could introduce essentially another layer to the currently 2-dimensional grid. It's probably the feature I'd most like to add, but the cost was too high so far.
I think Guesstimate is really ideal for smaller models, or for the prototyping of larger models. However, if you are making multi-million dollar decisions with hundreds of variables and correlations, I suggest more heavyweight tools (either enterprise Excel plugins or probabilistic programming).
I've heard of it being used in a few classes. There was one estimation session with one group of what I remember to be 8th-graders. Honestly, I really don't think you need to be great at statistics to understand the fundamental concepts.
Generally, we recommend lognormal distributions for estimated parameters that can't be negative. This works when you span multiple orders of magnitude, though it's possible you may want an even more skewed distribution (which is unsupported).
I may be able to make a much longer video introduction some-time soon.
We initially had a lot of uncertainty on how to price it but wanted to experiment with more users rather than fewer, with the premise that if it were very successful we could scale up.
I think if I were to start again or spend much time restructuring it, I'd probably focus a lot more on enterprise customers. That would be quite a bit of work though, so I don't have intentions of doing that soon.
Update: Matthew (the other cofounder) and I got Guesstimate to a stage we were happy with. After a good amount of work it seemed like several customers were pretty happy with it, but there weren't many obvious ways of making a ton more money on it, and we ran out of many of the most requested/obvious improvements. We're keeping it running, but it's not getting much more active development at this time.
Note that it's all open source, so if you want to host it in-house you're encouraged to do so. I'm also happy to answer questions about it or help with specific modeling concerns.
Right now I'm working at the Future of Humanity Insitute on some other tools I think will compliment Guesstimate well. There was a certain point where it seemed like many of the next main features would make more sense in separate apps. Hopefully, I'll be able to announce one of these soon.
I guess another question here is what are heuristics for how many images are necessary for different levels of functionality. The demos look pretty impressive, but I'm not sure how much went into them.
How suitable do you think this is for CPU-intensive work? I'm interested in having servers for scientific-computational work, which would be rather CPU-heavy. It would be great to offload some of this to a nearby browser for bits and pieces that desire low-latency.