diff --git a/_posts/2024-10-21-building-an-assignment-algorithm-2.markdown b/_posts/2024-10-21-building-an-assignment-algorithm-2.markdown index 3bae805aa..656a5538b 100644 --- a/_posts/2024-10-21-building-an-assignment-algorithm-2.markdown +++ b/_posts/2024-10-21-building-an-assignment-algorithm-2.markdown @@ -154,6 +154,9 @@ We considered normalisation, however, the highest value (no matter whether an ou Finally, we landed on using the Z-score for aggregate compromise. The Z-score is a statistical value which measures how many standard deviations (a measure of spread) a dataset value is from the average. You can find out more on the Z-score here. This means that compromise will play a more significant role in sorting when the aggregate compromise value is an outlier, however it would have a relatively small effect if the value is close to the average of the attendees aggregate compromise, no matter how large the compromise or the surplus is.
Click the 'more' button for to see how we compared compromise and surplus difference exactly, along with the rationale. +
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\(\text{sorting score} \\= standardisedSurplusScore - standardisedCompromiseScore \)

@@ -209,6 +212,7 @@ Finally, we landed on using the Z-score for aggregate compromise. The Z-score is \text{attendee surplus difference} \] +

The rationale behind this was as follows: