This blog post is the second in a series of posts about STAR voting. If you haven’t read the previous entry, I recommend you do so before starting this one.
In the previous post I made the case that STAR voting is simple enough to be a viable option for voting method reform, but I didn’t explain why STAR voting would be worth adopting. I want to start that explanation by talking about the ballot type that STAR voting uses. Often referred to as “the 5-star ballot”, it is a rated ballot with a scale of 0-5. This means that unlike with RCV ballots, you are free to rate candidates equally and to skip ratings as you like.
An example STAR ballot
I am a big supporter of STAR voting as a practical reform for single-winner elections in the United States. Under STAR voting, voters rate each candidate on a scale from 0 to 5, the two candidates with the highest total scores become finalists, and whichever finalist is rated higher on the most ballots wins. This is where the full name—Score Then Automatic Runoff—comes from; the first round of tallying chooses the finalists based on their score totals, and then the second round of tallying is an automatic runoff with each ballot counting for the finalist that voter preferred (or counting as an abstention if the voter liked both finalists equally).
There are a lot of reasons that I like this method, but one important reason is how simple it is. As you saw above, I can describe the entire method in a single sentence. Many other competing reforms like single-winner ranked choice voting can only be partially explained in a single sentence. STAR isn’t the simplest voting reform out there—that honor goes to approval voting—but I think a lot of people overestimate its complexity. Ranked choice voting, the most popular reform option in the U.S., is much more complex than STAR. If ranked choice voting can make as much progress as it has, then STAR is more than simple enough to be a viable reform, and anyone dismissing it on complexity grounds is doing it a disservice.
Anarcho-primitivism and transhumanism are two ideologies that place a lot of importance on technology, but they do so in completely opposite ways. This makes the task of combining them into a cohesive ideology very difficult, but that hasn’t stopped people from trying. While previous attempts have had many desirable features, there is still plenty of room for improvement. As such, I wish to propose a new combination of the two which I believe makes many of the available improvements.
To build this new ideology, we first need a model of the ideologies we’re building it out of. It’s common to model anarcho-primitivism and transhumanism as being on opposite ends of a technology political axis that lies orthogonal to other axes like the left-right axis. The key to combining these ideologies will be to split this axis in two.
In my previous post, I argued that probabilistic analysis was superior to pass/fail analysis as an approach to social choice theory. As a quick recap, pass/fail analysis tries to identify desirable criteria, then figure out which methods pass them and which methods don’t. Probabilistic analysis instead tries to identify how often failures of these criteria occur and how severe those failures are.
One consequence of pass/fail analysis is that it’s tempting to adopt what I would call a “dealbreaker criterion”, a voting criterion which a voting method must pass for you to even consider recommending it. If a voting method fails that criterion, it doesn’t matter if it is otherwise great; that failure is a dealbreaker. I’m not sure how many people actually do this, but I do know that it’s common to pick a criterion to emphasize as being crucial, which at the very least comes close.
Within social choice theory, there are two major approaches to evaluating voting methods: pass/fail analysis and probabilistic analysis. Pass/fail analysis primarily consists of defining mathematical criteria that seem desirable to have, then proving which methods pass them and which methods fail them. It also has other aspects like proving that certain sets of criteria cannot all be satisfied by the same voting method; this is where the famous Arrow’s impossibility theorem comes from. On the other hand, probabilistic analysis does away with this and instead assesses voting method performance using simulations and, where possible, data from real-world elections. This approach has its downsides, but overall I think it is by far the superior evaluation method.
As a simple example of the limitations of pass/fail analysis, I’d like to consider reversal symmetry. Reversal symmetry is passed if reversing the preferences of every voter will always result in a different winner when there are at least two candidates running. The idea behind this criterion is that voting methods should not consider the best candidate to also be the worst candidate. Single-choice plurality fails reversal symmetry, since it is possible for one candidate to have both a plurality of first-choice support and a plurality of last-choice “support”, which becomes first-choice support if preferences are inverted. On the other hand, approval voting passes reversal symmetry, since the candidate with the most approvals cannot also be the candidate with the least approvals.