|Think about Loose Coupling|
$NORM measures the average reputation of recent nodes. It answers the question, "Are nodes rated high or low?"
Entropy measures the information content of node reputation. It answers the questions, "How much does the node's reputation tell us? How meaningful is the assignment of reputation?"
Say $NORM is 11. Well, this can happen if all recent nodes have reputation 11. If this is the case, the entropy is 0 because knowing that a node has reputation 11 tells us nothing about the node.
On the other hand, maybe among all recent nodes, an equal number of them have reputation 1, 2, 3, .. up to 22. This situation also gives us $NORM = 11. But here, knowing the reputation of a node gives us much more information. Reputation in itself is more meaningful in this scenario because it can tell us something. The something it is telling us is information in the theoretical sense.
$NORM tells us whether nodes are given high or low reputations on average (although the variance might be useful to know as well). It is an analysis of the values of a random variable. Entropy is completely orthogonal; independent of how high or low the nodes are ranked, it tells us how informative node reputation really is. It is an analysis of the uncertainty of a random variable. You can have any combination of low or high average with low or high entropy.
In reply to Re^3: (contest) Help analyze PM reputation statistics