Tag: proportional

Incoherence & Mattson

People are often incoherent: their probabilities don't add to 100%. We get an 18% gain in accuracy if we coherentize their estimates. But we get a 30% gain in accuracy if we also assign more weight to coherent estimates.

What implications does this have for making subjective probability maps?

The following figure is from a recent paper I co-authored*:

Figure from Karvetski et al. 2014 showing we get more accuracy by ignoring incoherent estimates than by simple unweighted averages.
Figure from Karvetski et al. 2014 showing we get more accuracy by ignoring incoherent estimates than by simple unweighted averages.  (The unfortunately abbreviated 'BS' means 'Brier Score'. Lower is better, with 0 being perfect.)

What implications does it have for making subjective "consensus" probability maps at the start of a search?

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