Research map / Learning theory and algorithms / Matrix and tensor methods
Properties of Position Matrices and Their Elections
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use position matrices closer to UN rather than ID | Elections realizing the same position matrix · 8x80 dataset | maximum isomorphic swap distance: larger than 20% of the maximum possible isomorphic swap distance | Mixed |
| Use position matrices closer to UN rather than ID | Elections realizing the same position matrix · 4x16 dataset | maximum isomorphic swap distance: around 50% of the maximum achievable distance | Helped |
| Provide an explicit description of the vote domain | Frequency-matrix realizability testing · Explicit, single-peaked, and group-separable domains | algorithmic complexity | Helped |
| Allow an explicitly listed subset of votes rather than the full balanced group-separable domain | Position-matrix realizability testing · Explicit vote subsets | algorithmic complexity | Hurt |
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