Earlier this week, I claimed that STAR voters are almost always better off rating people at the extremes, 5 or 0. Sass disagreed, arguing that a voter with no insight into how everyone else will vote will do better scoring their second choice a 4, and their second-to-last choice a 1. I believe his logic is that the score gives them a boost in the runoff round.

To resolve this difference in perspective, I asked AI to write a STAR simulation that compares the expected value of rating a second choice 4 versus 5 across many random elections. I then ran a bunch of trials and thought everyone might be interested in the results.

With 3 or 4 candidates, a voter with no information does better rating their second choice a 4. With 5 candidates it’s roughly a wash, and with 6 or more, a 5 is better.

There are two reasons the strategy changes as the roster grows. First, your first and second choices become less likely to meet in the runoff, so maintaining a distinction between them matters less. Second, the competition to get into the runoff is increased, so the extra point becomes more valuable.

Simulation Notes

• Each run simulates 250,000 elections.
• In each election, two scenarios are run where a test voter rates their second choice with 5 stars and the other with 4. The results are compared.
• Results are the test voter’s expected satisfaction, measured by their own ratings: their favorite winning is worth 5, their second choice 4, anyone else 0–3. Losing your favorite to your second choice costs 1 point; losing it to a candidate you rated 0 costs 5.
• Every other voter rates every candidate independently and uniformly from 0 to 5, and votes honestly.