For instance, in score voting, you can count average instead of total score.
I’m not sure I understand how that would do it. I’ve thought of a way that it could, but I have had to independently generate so much that I’m going to have to ask and make sure this is what you were thinking of. So, the best scheme I can think of for averaged score voting still punishes unknowns by averaging their scores towards zero (or whatever you’ve stipulated as the default score for unmentioned candidates) every time a ballot doesn’t mention them, but if the lowest assignable score was, say, negative ten, a person would not be punished as severely for being unknown, as they would be punished if they were known and hated. Have I got that right? And that’s why it’s better, not because it’s actually fair to unknowns but because it’s less unfair?
[Hmm, would it be a good idea to make the range of available scores uneven, for instance [5, −10], so that ignorers and approvers have less of an effect than oppponents (or the other way around, if opponents seem to be less judicious on average than approvers, whichever).]
The best way to deal with this problem is a “soft quota” in the form of N pseudo-votes against every candidate. More-or-less equivalently, you can have every blank ballot count as X% of a vote against.
But that’s just artificially amplifying the bias against unknowns, isn’t it? Have you had so much trouble with dark horses that you’ve come to treat obscurity as a heuristic for inferiority? You know what else is obscure? Actual qualifications. Most voters don’t have them and don’t know how to recognise them. I worry that we will find the best domain experts in economics, civics, and (most importantly for a head of state) social preference aggregation, buried so deeply in the Dark Horse pile that under present systems they are not even bothering to put their names into the hat.
[Hmm, back to recommender systems, because I’ve noticed a concise way to say this; It’s reasonable to be less concerned about Dark Horses in recommender systems, because we will have the opportunity to measure and control how the set {people who know about the candidate} came to be. We know a lot about how people came to know the candidate, because if they are using our platform for its intended purpose, it was probably us who introduced them to it. We will know a lot more- if not everything there is to know- about the forces that bias the voting group, so we can partially correct for them.]
I’m going to keep talking about recommender systems, because it’s not obvious to me that election systems and general purpose demography analysis, discovery, and discussion systems should be distinct.
I’m not sure I understand how that would do it. I’ve thought of a way that it could, but I have had to independently generate so much that I’m going to have to ask and make sure this is what you were thinking of. So, the best scheme I can think of for averaged score voting still punishes unknowns by averaging their scores towards zero (or whatever you’ve stipulated as the default score for unmentioned candidates) every time a ballot doesn’t mention them, but if the lowest assignable score was, say, negative ten, a person would not be punished as severely for being unknown, as they would be punished if they were known and hated. Have I got that right? And that’s why it’s better, not because it’s actually fair to unknowns but because it’s less unfair?
[Hmm, would it be a good idea to make the range of available scores uneven, for instance [5, −10], so that ignorers and approvers have less of an effect than oppponents (or the other way around, if opponents seem to be less judicious on average than approvers, whichever).]
But that’s just artificially amplifying the bias against unknowns, isn’t it? Have you had so much trouble with dark horses that you’ve come to treat obscurity as a heuristic for inferiority? You know what else is obscure? Actual qualifications. Most voters don’t have them and don’t know how to recognise them. I worry that we will find the best domain experts in economics, civics, and (most importantly for a head of state) social preference aggregation, buried so deeply in the Dark Horse pile that under present systems they are not even bothering to put their names into the hat.
[Hmm, back to recommender systems, because I’ve noticed a concise way to say this; It’s reasonable to be less concerned about Dark Horses in recommender systems, because we will have the opportunity to measure and control how the set {people who know about the candidate} came to be. We know a lot about how people came to know the candidate, because if they are using our platform for its intended purpose, it was probably us who introduced them to it. We will know a lot more- if not everything there is to know- about the forces that bias the voting group, so we can partially correct for them.]
I’m going to keep talking about recommender systems, because it’s not obvious to me that election systems and general purpose demography analysis, discovery, and discussion systems should be distinct.