Let me see if I understand you correctly.
You have a matrix of (number of individuals) x (number of time-slices). Each matrix cell has value ("happiness") that's constrained to lie in the [-1..1] interval. You call the cell value "local utility", right?
And then you, basically, sum up the cell values, re-scale the sum to fit into a pre-defined range and, in the process, add a transformation that makes sure the bounds are not sharp cut-offs, but rather limits which you approach asymptotically.
As to the second part, I have trouble visualising the language in which the description-length would work as you want. It seems to me it will have to involve a lot scaffolding which might collapse under its own weight.
"You have a matrix ...": correct. "And then ...": whether that's correct depends on what you mean by "in the process", but it's certainly not entirely unlike what I meant :-).
Your last paragraph is too metaphorical for me to work out whether I share your concerns. (My description was extremely handwavy so I'm in no position to complain.) I think the scaffolding required is basically just the agent's knowledge. (To clarify a couple of points: not necessarily minimum description length, which of course is uncomputable, but somet...
Summary: the problem with Pascal's Mugging arguments is that, intuitively, some probabilities are just too small to care about. There might be a principled reason for ignoring some probabilities, namely that they violate an implicit assumption behind expected utility theory. This suggests a possible approach for formally defining a "probability small enough to ignore", though there's still a bit of arbitrariness in it.