I'm certain that I'm forgetting something basic, but here goes. The exploding dice is a (house) rule for some games that says when you roll the maximum result on a given die (e.g. 6 on a six-sided die) you roll that die again and add the result. If you roll the maximum value again, you roll again and add that. This will carry on until you stop rolling the maximum value.
From here, a natural question arises: "what's the average of an exploding die?". With the example of a six-sided die, the following answer comes naturally:
$3.5*+3.5*\frac{1}{6}+3.5*\frac{1}{6^2}\dots=4.2$
This does indeed seem to be correct, and holds to any empirical test that I can think of, but why does this work? I want to use some excuse to the effect of "expected value is linear and we've got identical distributions", but I find that unsatisfactory. In particular, I don't understand why we can use the average values of 3.5 when every term to the right of that 3.5 assumes that we've beat the average. I have no doubt that this is why we need the $6^{-n}$ terms, but my intuition insists that this is insufficient.
Note: What I really want here is to see the rigor. An ideal answer will attack this from the ground up, possibly even axiomatically. I'd hope that we don't have to go as deep as using probability measures on sets, but at the very least I want some answer that focuses on what property of averages allows us to factor the dice like this.