“MARE,” a Two-Round Voting Method Design Framework to Reduce Strategic Incentives
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I’ve been considering generalizations of SSVV designed to improve robustness against strategic voting, and landed on the following design outline for a voting system, which can be called “MARE,” standing for “Majoritarian vs. Alternative with Rescue Election.”
I tested this framework using limited simulations of strategic voter behavior, and found significantly improved outcomes against benchmarks. For example, successful burial rates dropped from ~15% to ~7%, with >50% of buried Condorcet winners being rescued. This needs further investigation and more rigorous analysis.
The idea is as follows: in the first round, voters submit rank-score ballots, which determine two sets of candidates: a Majoritarian set M, and an Alternative set A.
For example, M could be the Smith set or Bipartisan set. A could be the Approval winner set or the IRV winner set.
Next, voters are given information about M and A. For example, M may be made public. A may also be made public, or some relevant statistics about A may be made public. For example, maybe instead of revealing the score winner, only the score is shown.
The key is, voters can use the information about M and A to do two things at once: (1) vote for whether to proceed by electing a candidate from A, or a candidate from M. And (2) possibly submit a “Rescue” signal, designed to counter burial tactics that may have manipulated M.
For example, the Rescue signal may be implicit, as the M-outsider with the highest top-rank support among outsiders. The design goal for the Rescue signal is to re-introduce a buried candidate into M with high efficiency, without ruining a sincere election. The cost is strict Smith compliance (although, if the Smith set is already manipulated and insincere, that may not be a bad thing).
If the electorate chooses M, then the pre-specified completion rule is run on M with the Rescue set R. For example, Benham could be run on M, and the winner could go head-to-head against a winner in R.
Otherwise, if they choose A, then the pre-specified completion rule is run on A.
The goal of MARE is to enable information/incentive engineering by selecting the rules for M, A, and R. In some experiments, I had M be Smith, A be IRV, and R be top outsider. There are various other considerations to explore within this framework. I just wanted to share it to see if others wanted to experiment with simulations.