I found the discussion around Hofstadter’s law in forecasting to be really useful as I’ve definitely found myself and others adding fudge factors to timelines to reflect unknown unknowns which may or may not be relevant when extrapolating capabilities from compute.
In my experience many people are of the feeling that current tools are primarily limited by their ability to plan and execute over longer time horizons. Once we have publicly available tools that are capable of carrying out even simple multi-step plans (book me a great weekend away with my parents with a budget of $x and send me the itinerary), I can see timelines amongst the general public being dramatically reduced.
I think unknown unknowns are a different phenomenon than Hofstadter’s Law / Planning Fallacy. My thinking on unknown unknowns is that they should make people spread out their timelines distribution, so that it has more mass later than they naively expect, but also more mass earlier than they naively expect. (Just as there are unknown potential blockers, there are unknown potential accelerants.) Unfortunately I think many people just do the former and not the latter, and this is a huge mistake.
Interesting. I fully admit most of my experience with unknown unknowns comes from either civil engineering projects or bringing consumer products to market, both situations where the unknown unknowns are disproportionately blockers. But this doesn’t seem to be the case with things like Moore’s Law or continual improvements in solar panel efficiency where the unknowns have been relatively evenly distributed or even weighted towards being accelerants. I’d love to know if you have thoughts on what makes a given field more likely to be dominated by blockers or accelerants!
Civil engineering projects and bringing consumer products to market are both exactly the sort of thing the planning fallacy applies to. I would just say what you’ve experienced is the planning fallacy, then. (It’s not about the world, it’s about our methods of forecasting—when forecasting how long it will take to complete a project, humans seem to be systematically biased towards optimism.)
I found the discussion around Hofstadter’s law in forecasting to be really useful as I’ve definitely found myself and others adding fudge factors to timelines to reflect unknown unknowns which may or may not be relevant when extrapolating capabilities from compute.
In my experience many people are of the feeling that current tools are primarily limited by their ability to plan and execute over longer time horizons. Once we have publicly available tools that are capable of carrying out even simple multi-step plans (book me a great weekend away with my parents with a budget of $x and send me the itinerary), I can see timelines amongst the general public being dramatically reduced.
I think unknown unknowns are a different phenomenon than Hofstadter’s Law / Planning Fallacy. My thinking on unknown unknowns is that they should make people spread out their timelines distribution, so that it has more mass later than they naively expect, but also more mass earlier than they naively expect. (Just as there are unknown potential blockers, there are unknown potential accelerants.) Unfortunately I think many people just do the former and not the latter, and this is a huge mistake.
Interesting. I fully admit most of my experience with unknown unknowns comes from either civil engineering projects or bringing consumer products to market, both situations where the unknown unknowns are disproportionately blockers. But this doesn’t seem to be the case with things like Moore’s Law or continual improvements in solar panel efficiency where the unknowns have been relatively evenly distributed or even weighted towards being accelerants. I’d love to know if you have thoughts on what makes a given field more likely to be dominated by blockers or accelerants!
Civil engineering projects and bringing consumer products to market are both exactly the sort of thing the planning fallacy applies to. I would just say what you’ve experienced is the planning fallacy, then. (It’s not about the world, it’s about our methods of forecasting—when forecasting how long it will take to complete a project, humans seem to be systematically biased towards optimism.)