> 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 had this conclusion in practice in AGI design for a while -- as did Joscha Bach -- which is why OpenCog and MicroPsi get multiple top-level goals not a single top-level goal... where the regulation of goal-weightings is part of the cognitive dynamic... > One criticism is: why frame the theory in terms of > simpliciity? Everyone else seems to use complexity > metrics. It is like describing your temperature metric > as "coldness". In both cases, there's a lower bound, > but no real upper bound. It makes sense for complex > systems to score highly, and simple systems to have > low scores. The "simplicity" framing suggests inverting > this. It seems wrong to me. > Either way is right, it doesn't matter does it? Just a matter of aesthetic taste... ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T7f31810a817f8496-M62279e7b87f891694f444e07 Delivery options: https://agi.topicbox.com/groups/agi/subscription
