All against the machine: the Solo score for rating skill in variable environments
We propose a distribution-free metric to rate individual skill in ``player-versus-environment'' settings, where participants face heterogeneous tasks without direct opponents. Such settings are common in digital platforms, games, education, finance, and the benchmark evaluation of AI agents. They combine high randomness, tasks of widely varying difficulty, and unknown heterogeneity across individuals. Our metric maps each task outcome to a bounded performance score with zero population mean and variance bounded by $1/3$, regardless of the outcome distribution, so that scores are directly comparable across tasks. Aggregating these scores over tasks yields an interpretable skill score for e...
| Authors | David Reguera, Xavier R. Hoffmann, Irene Pérez et al. |
|---|---|
| Primary category | physics.soc-ph |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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