I mean, I think it is, but I need to understand it more fully. I just really like the tricks so far, and haven’t evaluated what they’re optimal for yet. :P
if i gave x a wap of r, then i approves x iff less than r% of all participants do not approve x
A wap is a measure of how much you’d be willing to let your approval of x override others’ underapproval of x? (Where “underapproval” is just assigning a wap<r)
Does the method have a name? Is it similar to anything else? The % conditional approval thing has assurance-contracty vibes, but it does more.
The method is named “Maximum Partial Consensus (MaxParC)” in the paper.
The exact meaning of giving a wap of r to option x is really just the following binding conditional commitment: “I commit to approve x if and only if less than r% of the participants do not approve x”.
All the waps for x together form an interdependent system of conditions, which has a “largest” solution in the sense that there is a unique largest set of participants such that if all of them approve, then everyone’s condition for approval is met.
One can find this solution easily with pen and paper as follows: sort all waps for x in ascending order, draw a diagonal like in the screenshot below, and find its leftmost intersection with the wap distribution. Then all participants with waps to the left do not approve and those with waps to the right do approve.
In the GUI, one can see whether one approves by checking whether one’s wap slider intersects with the light approval bar, as in this screenshot from the EA-related demo poll posted earlier:
This is awesome!
I mean, I think it is, but I need to understand it more fully. I just really like the tricks so far, and haven’t evaluated what they’re optimal for yet. :P
A wap is a measure of how much you’d be willing to let your approval of x override others’ underapproval of x? (Where “underapproval” is just assigning a wap<r)
Does the method have a name? Is it similar to anything else? The % conditional approval thing has assurance-contracty vibes, but it does more.
The method is named “Maximum Partial Consensus (MaxParC)” in the paper.
The exact meaning of giving a wap of r to option x is really just the following binding conditional commitment: “I commit to approve x if and only if less than r% of the participants do not approve x”.
All the waps for x together form an interdependent system of conditions, which has a “largest” solution in the sense that there is a unique largest set of participants such that if all of them approve, then everyone’s condition for approval is met.
One can find this solution easily with pen and paper as follows: sort all waps for x in ascending order, draw a diagonal like in the screenshot below, and find its leftmost intersection with the wap distribution. Then all participants with waps to the left do not approve and those with waps to the right do approve.
In the GUI, one can see whether one approves by checking whether one’s wap slider intersects with the light approval bar, as in this screenshot from the EA-related demo poll posted earlier: