Well ok, but if you listen carefully you can hear which elevator is coming/closest and always be standing directly in front of the one that is arriving.
Where to wait for an elevator (2010)
71–80 of 115 posts
Re: Where to wait for an elevator (2010)
#72Might be a bit off-topic… But: The one big question I have with elevator UX is this: why isn’t it possible to deselect a choice? So pressed 10th floor but wanted 11th. There is no way to press button 10 and then button 11 to correct that mistake. Will I ever solve this mystery?
Because in a busy environment (where consistently more than one person uses the elevator) the scenario of making a wrong decision and having the wish to correct it occurs far less than the scenario where someone does not pay attention to which floors have been selected already and deselects your destination. At the worst case this means that you both miss your stop because you both did not notice.
Also, malicious fighting over selecting and deselecting destinations
Re: Where to wait for an elevator (2010)
#73Might be a bit off-topic… But: The one big question I have with elevator UX is this: why isn’t it possible to deselect a choice? So pressed 10th floor but wanted 11th. There is no way to press button 10 and then button 11 to correct that mistake. Will I ever solve this mystery?
> But: The one big question I have with elevator UX is this: why isn’t it possible to deselect a choice? So pressed 10th floor but wanted 11th. There is no way to press button 10 and then button 11 to correct that mistake.
In many elevators, one can double-press a button to deselect it.
Re: Where to wait for an elevator (2010)
#74[deleted]
Re: Where to wait for an elevator (2010)
#75Re: Where to wait for an elevator (2010)
#76Is there any intuitive explanation for why the mean minimizes the squared error? I know it IS true - this property is used for linear regressions etc - but I couldn’t actually explain it. It seems like whatever minimizes a squared error should itself have some squares in it.
Distances are squares, actually square roots of squares, in Euclidian space, so it makes sense to find squares when we deal with distances.
An intuitive way to look at it is that square function is the simplest function that goes down until it reaches a minimum, and then back up again. If you want this property, you can expect to find squares somewhere. Distance have this property: if you move in a straight line, the distance to your target will go down as you are closing in, then you reach it, then if goes back up again after you pass it.
That's the general intuition for where the squares can be. Let's be a bit more formal.
Let's flip the problem and try to minimize the sum of the squared distances (or squared error, same thing). In 1D, the sum of the squared distances to a and b is (a-x)^2 + (b-x)^2 = a^2 - 2ax + x^2 + b^2 - 2bx + x^2 = a^2 + b^2 - 2(a+b)x + 2x^2
It is a parabola, the minimum is where the derivative is zero. The derivative is -2(a+b) + 4x, solve -2(a+b) + 4x = 2 for x and you have x = (a+b)/2, which is your average. Of course, it works with more than 2 distances.
Another intuition: to minimize something, usually, we take a derivative, and when we take the derivative of something squared, the square tends to disappear, so it shouldn't surprising that the minimum of something squared has no squares in it.
Re: Where to wait for an elevator (2010)
#77Is there any intuitive explanation for why the mean minimizes the squared error? I know it IS true - this property is used for linear regressions etc - but I couldn’t actually explain it. It seems like whatever minimizes a squared error should itself have some squares in it.
Distances are squares, actually square roots of squares, in Euclidian space, so it makes sense to find squares when we deal with distances. An intuitive way to look at it is that square function is the simplest function that goes down until it reaches a minimum, and then back up again. If you want this property, you can expect to find squares somewhere. Distance have this property: if you move in a straight line, the…
> An intuitive way to look at it is that square function is the simplest function that goes down until it reaches a minimum, and then back up again
By what measure of simplicity is x^2 simpler than abs(x)?
I mean, if you added the caveat “with a continuous derivative”, sure.
Re: Where to wait for an elevator (2010)
#78The best real-world optimization: stand aside to make room for anyone exiting the elevator before crowding the doorway trying to walk in too quickly!
Great, you just made the problem more complicated.
That's is because if there are people in the elevator and you right in front of it, you have to move away, wait, and go back. Not ideal, it makes more sense to wait on the side of the elevator, possibly at a distance proportional to the expected time it takes for the passengers to leave.
That would make the ideal waiting spot somewhere between the middle elevator and the other one that is the furthest away from the middle elevator, but not necessarily the mean.
Re: Where to wait for an elevator (2010)
#79"Since you can’t move without increasing the average distance, you must have started at the best spot." Technically this isn't a fully sound inference. All this proves is that you're at a local optimum. So you'd also need to know or show that there is only one optimum/the problem is convex/local=global or any variant. Of course that is the case here, but it's always worth noting the specific properties of a problem t…
The author addressed this point in a comment on the article:
> True, it’s not a rigorous proof. You can find a rigorous proof here:
> “A Simple Noncalculus Proof That the Median Minimizes the Sum of the Absolute Deviations.” Neil C. Schwertman, A. J. Gilks, and J. Cameron. The American Statistician, Vol. 44, No. 1 (Feb., 1990), pp. 38-39.
That paper fits on one page, not counting references. Here's a link: https://tommasorigon.github.io/StatI/approfondimenti/Schwert...
Re: Where to wait for an elevator (2010)
#80Reminds me of this: https://en.wikipedia.org/wiki/Rectilinear_Steiner_tree#Singl...
The best "trunk" to minimize total distance to all points is the median of their positions.