  We build a theory of binary trees on finite multisets that categorifies, or
operationalizes, the entropy of a finite probability distribution. Multisets
operationalize probabilities as the event outcomes of an experiment. Huffman
trees operationalize the entropy of the distribution of these events. We show
how the derivation property of the entropy of a joint distribution lifts to
Huffman trees.
