On 2020-09-04 12:19:PM, Ben Goertzel wrote:
The paper addresses what to do about the issue of there not
being any single completely satisfactory single metric of
simplicity/complexity. It proposes a solution: use an array
or such metrics and combine them using pareto optimality.
I think that is basically correct. You are likely to
have multiple measures of simplicity/complexity, and
pareto optimality seems like a fairly reasonable
approach to combining them.
Well it seems like weighted-averaging valid simplicity measures does
not generally yield a valid simplicity measure with nice symmetrics
(even if you're doing simple stuff like weighted-averaging of program
length and runtime, say...). So you kinda have to go Pareto.
I am usually pretty skeptical about the relevance of Pareto optimality
to machine intelligence. It typically conflicts with utility-based
frameworks.
A utility calculation typically doesn't care if some parties are worse off -
and will happily sacrifice in the name of the greater good - whereas
the notion of Pareto optimality will dismiss solutions if only one
party is a teeny tiny bit worse off. It seems like a childish way to
negotiate.
Perhaps, if I think it through further, I will find similar flaws in
this proposal too.
A weighted average might be appropriate on log scales. Otherwise, maybe a
weighted product would be better. As well as weights, you need log
scaling - if
attempting to compare and combine things like program size and runtime.
I currently need to think about it all further, though.
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