Better together? A performance theory of human-machine skill policies under generalization difficulty
Organizations pairing a human and a machine on a task decide, often by default, which executes each instance and what happens when both do. We account for what this attains under the squared rule, with performance distributed over instances to capture generalization difficulty. A gross stake lies between the skills' mean and best per instance. It splits at the best single skill into a systematic part, known from separate assessments and secured by commitment, and a residual, the expected value of perfect information on which skill each instance favors, known only from paired observation and split further by classes. Skills set the stake; workflows decide the take, via two channels. Routin...
| Authors | Riccardo Zanardelli |
|---|---|
| Primary category | cs.HC |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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