Some gradients between two colours look good, others suck. It's easy to tell the difference, but what *is* that difference?
I think @JoshWComeau has figured it out! Here's the thought: gradients trace a line between two points in colour space, but there's more than one way to represent colours in a (typically 3D) space.
One way to pick out a colour is by specifying how much red, green, and blue it contains. These are RGB colour spaces.
Second pic is my phone screen under a microscope, which uses this idea of adding together R, G, and B
Another way to pick out a colour is by specifying its 'hue' (where on the colour wheel?), its saturation (how pastel vs colourful?), and its 'value' (how bright?). These are 'HSV' or 'HSB' colour spaces.
Josh's idea is that attractive-looking colour gradients are linear gradients in HSV-type colour spaces, and other gradients are unattractive because they trace a straight line through RGB-type spaces.
This is because bad-looking gradients typically have a greyish bit between the two colours, while attractive gradients are colourful throughout (see above).
If you imagine the RGB cube, there is a channel of greyish colours running between the "all 0%" corner and the "all 100%" corner. Two colours with significantly different hues are going to have to pass through that channel.
In HSV space, two colours with significantly different hues avoid that grey zone by orbiting around it, remaining colourful.
Notice that just varying the hue to get between two colours of similar S and V means circling around vs cutting through the colour wheel.
Here are two slices of the HSV cylinder for colours of the same value (~brightness).
The linear gradient in RGB space is on the left, the linear gradient in HSV space is on the right.
Interesting paper: participants got $50 to ‘trade’ in S&P or bonds as if it were a given day in the last 15 years, and they were shown the WSJ headline from the *next* day (36-hour clairvoyance)
After 15 random days: average return was 3.2%, 45% lost money, 16% went bust (!)
A memorably insane detail from 'The Doomsday Machine' by the late Daniel Ellsberg:
In 1960, the US Air Force would sometimes task the RAND Corporation with assessing new technical proposals. One memo titled "Project Retro" fell to Ellsberg to assess.
The scheme, which had already passed through multiple agencies without being discarded, was prompted by the worry that a surprise Soviet attack with ICBMs could incapacitate US land-based missiles before they could retaliate.
Ellsberg: "[it] proposed in some detail to assemble a huge rectangular array of one thousand first-stage Atlas engines—our largest rocket[s]—to be fastened securely to the earth in a horizontal position, facing in a direction opposite to the rotation of the earth...
You start with a sample space S, and a prior about the likelihood of H. You can think of learning new evidence like placing a smaller frame somewhere inside of S.
The dots ":" are there because if you write in the areas of the left and right sides, you get the fractional odds. In this example we go from about 2:1 to about 1:3 (from the bigger to the smaller rectangle)
Areas stand for probabilities: you thought that H was less likely than not before you placed the smaller frame, and now it looks more likely.
Should settling Mars be a current priority for longtermists? I think no, not by a long shot! (Let’s instead focus on preventing pandemics, decarbonising, making sure AGI doesn’t go terribly...)
Here’s why:
The most compelling argument for travelling to Mars soon is that it’s a hedge against extinction on Earth. Launching some of our eggs into a different basket.
I agree that preventing anything as irreversible as extinction is hugely important!
But on these grounds the Mars strategy looks far less promising than other potential priorities: it’ll likely be hugely expensive for the risk it could reasonably hedge against, and (as far as I can tell) it probably wouldn’t protect against the most concerning risks.