The uniform-distribution assumption (20) means that cuts close to the root of the hierarchy result in a greater smoothing of the probability estimates than cuts near the leaves.
there is a trade-off between choosing a model that has a cut near the leaves, which is likely to overfit the data, and a more general (simple) model near the root, which is likely to underfit the data.