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This sounds great.

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.

http://smpro.ca/pjs/distshaper/

Guesstimate doesn't yet support an input format for distributions outside of via samples.

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.

Crystall Ball: https://www.oracle.com/applications/crystalball/ @Risk: https://www.palisade.com/risk/default.asp Stan: https://mc-stan.org/ PYMC3: https://docs.pymc.io

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.

We're keeping it running but aren't actively improving it.

Thanks for the feedback.

We have some documentation [here](https://docs.getguesstimate.com/), and some in the sidebar entree.

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.

Cofounder Here: Happy to see this on hnews again.

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.

Is Lobe only for image data? Would it work for inputs that are text files or similar?

Very good to know. Right now you can choose between normal, uniform, and a few very simple discreet distributions, but not others.

When I built this, my first goal was to make any distribution run quickly. At this point I believe adding other distribution types will be quite doable, expect them shortly.

Thanks for letting me know.

It's definitely not mobile compatible yet. I think making it viewable on mobile devices soon is doable. Making them easily editable will be much harder / require a different interface.

Guesstimate doesn't yet support correlations. Right now we assume everything is independent, though of course variables are often correlated with their outputs.

That makes sense. It would be very useful to see estimates of how well Presidential candidates would do if they got elected.

In the future, one idea would be to keep track of people's metric estimates in Guesstimate, and later score and rank them on how well they do. So if Charles always reports a 90% confidence interval that's far too optimistic, we could help adjust it automatically next time. This would also allow us to aggregate different opinions directly, essentially being like a mini prediction challenge. This would be a ways off though, and it really depends on what direction the product goes.

"Is there support for µ±σ instead of [low,high] in the works? " - There used to be. I'll be considering ways of adding it back.

"Or support for numerical distributions?" - By numerical distributions do you mean discreet distributions: like, a 40% of being '8' and a 60% chance of being '6'? If so, the answer is no. However, if you use the ternary operator it is possible to do very simple versions of this now. We do support totally random picks of different numbers though, using the pickRandom([3,5,3]) function. http://mathjs.org/docs/reference/functions/pickRandom.html

Right now the vast majority of it is open source. There is a component that is not: the rails server, but that's pretty tiny. The client can be developed on without that. If there's some interest I'm happy to make what's existing available.

I can't make guarantees about the distant future. There's a ton of work I would love to see happen with Guesstimate, and my guess is that much of it would only be possible if it becomes a company. This can still mean that it can be mostly open source, but I really have little idea what the situation would be at that time.

Good catch!

Right now the main distribution types are normal and uniform. In the video, I showed normal distributions, which have long tails in both directions.

In this case, a normal distribution isn't really correct, because, as you noted, being less than 0 is exceedingly unlikely.

I believe the correct way to deal with this is to use a lognormal distribution or something that has 0 chance of being less than 0. I don't yet have a simple way of doing this, but it's definitely on the agenda.

Sorry to hear about Intercom, will investigate shortly. I heard someone else had a similar issue.

In response to your points:

- Other distribution types are the #1 most requested feature at this time. Binary distributions are possible using functions instead of the built in distributions, though obviously not intuitive. It's built using math.js, which has several random functions of different kinds. (In this case, you could use '=randomInt(0,1)' to produce a coin flip.

http://getguesstimate.com/models/365

- The arbitrary two-letter codes were simply the easiest thing to begin with. I started with something derived from the names, but this presented problems with cells with empty names, especially ones that started empty and later become non-empty. Excel has a pretty sophisticated model for referencing cells. For the sake of getting something shipped, I started with a very simple one. Definitely an area to improve.

- I'd love to talk sometime. I'm also in SF, will send you an email. Thanks for the advice!

That's definitely on the agenda. Curious, what are you interested in a JSON serialization for?

No. It needs to be online at this point.

Definitely. Here's one in that range as an example. http://getguesstimate.com/models/163

Feel free to play with it. You can edit it, just not save it. (I recently realized this was not obvious to most people)

In practice, 5000 was basically the number that wouldn't slow it down; this represented around 20-30% of the rendering time (react components were the main bottleneck, surprisingly, though I could still optimize them more).

I think that this works fine for small models, which is much of what exists now. As there are larger models, I'd eventually like to offload calculations to AWS Lambda or something similar, so we can do far more.

I believe it would be handy, but it depends on the size. Right now I think it's reasonably fast and intuitive for models of around 3 to 40 metrics(variables). If you have more it could get slower, especially if many of them have to be recalculated at once.

I suggest trying it out. If nothing else, you may be able to begin with very simple models of the most important variables.

That seems like a great fit.

A few weeks back I used the tool to help a friend decide which mortgage option to take for his house. One house has a slightly lower APR than the other, but was a had a higher assistance fee.

After fiddling with it, it looked like the one with the lower assistance fee was the better option. But perhaps more important, it didn't seem like it made a big difference; perhaps around $200 after 10 years. This was a good indication that the choice didn't really matter; that it wasn't something to spend over a few hours worrying about.

http://getguesstimate.com/models/100