God dammit. This sort of thing pisses me off. Here I am, on vacation, waiting for my family to wake up. What better way to spend my time but pursue HN. I happen upon something like this. Something so damned useful that I have no choice but investigate.
Guesstimate – A Spreadsheet for things that aren't certain
61–70 of 73 posts
Re: Guesstimate – A Spreadsheet for things that aren't certain
#62Cofounder 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 mo…
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Re: Guesstimate – A Spreadsheet for things that aren't certain
#63I would love to see this idea translated into event planning/calendaring. Probabilistic party planning. I want to see what might be happening tonight in addition to what is definitely happening.
"If 5 people show up at my house tomorrow evening, I'll hold a poker night." 10 people were invited and 4 of them RSVP yes and 2 of them RSVP no. It looks like there's a 95% chance I'm holding a poker night tomorrow.
"The X team has a monthly meeting on the 1st, never fail. They haven't decided on the location yet, just that it's on the North Side." As the team members pick possible locations, the possible locations appear more distinct until one is chosen.
Re: Guesstimate – A Spreadsheet for things that aren't certain
#64Earlier quoted context omitted.
Are you able to apply global correlations to all the variates? One of the triggers for the financial crisis in '08 was that the Monte Carlo pricers assumed the various risks were much less correlated than they actually were. For example, they largely assumed that it was unlikely for many mortgages or underlying MBS securities to simultaneously default (low correlation). This is how many AAA rated CDO securities ended…
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…
Thanks for explaining your thought process, I read your other replies and it's agree that many decisions are being made without any formal probabilistic model at all. There's a lot of value in sitting down and working out how things might be related to each other.
> where there isn't a straightforward causal relationship
One way to interpret a global pairwise correlation is simply that the person building the model is being systematically biased in one direction—either being too pessimistic or optimistic. This is a 'non-causal' relationship but often the biggest contributor to variance between the model and the real world.
Philosophically, this is a bit like the difference between 538's modeling approach and Princeton Election Consortium's for the 2016 election—the former gave Hillary a 2/3 chance of winning, while the latter ascribed a ~99% chance.
The risk of leaving modeling error out is that you'll end up with much more confidence than is called for—it feels very different to come up with a point estimate (I'll save $10k this year) vs. a tight range (I'll save 9k-11k this year), if the true range is much wider.
In the former case you know your point estimate may be very far off, but in the latter you may be tempted to rely on an estimate for variance that too low.
> It could introduce essentially another layer to the currently 2-dimensional grid
You could probably get away with doing almost all of this automatically for the user as long as the decide on what the 'primary' output is:
- For every input, calculate whether it's positively or negatively correlated with the output
- Apply a global rank correlation to all the inputs with all the standard techniques, flipping the signs found above as appropriate
- Report what the output range looks with a significant positive correlation (usually the negative correlation case isn't as interesting)
Re: Guesstimate – A Spreadsheet for things that aren't certain
#65Can this sample from an empirical distribution? (i.e. from a CDF, not from a known distribution family)
I should add that I was thinking about this method -- Inverse Transform Sampling [1].
It's also just a fancy name for generating a pseudo-random number from a Uniform distribution in [0,1], and reading off the x-axis of the CDF.
[1] https://en.wikipedia.org/wiki/Inverse_transform_sampling
Re: Guesstimate – A Spreadsheet for things that aren't certain
#66Earlier quoted context omitted.
Can you give some examples on what you have built with it?
Sure! Here's a particularly big one I built when deciding whether to invest in Walmart: https://www.getguesstimate.com/models/10935 Here's another one I did to ballpark a pension plan: https://www.getguesstimate.com/models/11133 Another thing I like is that you can do simple statistical reckoning for it. For my job, I often have to benchmark something several hundred times with or without a patch applied. It can be b…
> whether to invest in Walmart: https://www.getguesstimate.com/models/10935
That is one heavy model. My computer comes to a standstill trying to open this one.
I am curious though what was the result of the analysis?
Re: Guesstimate – A Spreadsheet for things that aren't certain
#67Can this sample from an empirical distribution? (i.e. from a CDF, not from a known distribution family)
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.
Re: Guesstimate – A Spreadsheet for things that aren't certain
#68This is terrific! The UI is very clever. I may have to steal some ideas from this.
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.
Re: Guesstimate – A Spreadsheet for things that aren't certain
#69Cofounder 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 mo…
Great work; this looks awesome.
I am wondering what products would look like, if hardware engineers applied this to the modeling of future products.
At my startup valispace.com for now we only allow for a simple propagation of worst-case values (gaussian distribution or worst-case stacking), but I think that specially for early design phases it would be of huge help and foresee problems in complex projects early on.
Do you know of anyone using guesstimate for hardware engineering purposes?
Re: Guesstimate – A Spreadsheet for things that aren't certain
#70Cofounder 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 mo…
I’ve used the product to “guesstimate” a few things like quality of life with a higher paying job with longer commute (not worth it!) and starting a business. Love how intuitive and clean the UI is and how it puts probability estimation at my fingertips, in simple, human language. Thank you!
Can you share what you found?