The paper lacks an experimental results section. So I don't know how this simplicity measure compares to Solomonoff induction. Theoretically, Solomonoff induction works because all possible probability distributions over an infinite set of strings must favor shorter strings because for every element, there must be an infinite set of longer and less likely strings, and finite sets of the other 3 possible combinations.
Distributions that favor fast programs are allowed but not favored by Occam's Razor. We only use them because of practical limitations. But we still believe that a multiverse is more likely than a universe because it is a simpler description of our observations, in spite of requiring more physics computation. Remember that finding simple theories to fit the data is not computable, which means that algorithms that do this well are necessarily complex. The practical approach in data compression is to combine a lot of approaches including lots of special cases. On Fri, Sep 4, 2020, 12:21 PM Ben Goertzel <b...@goertzel.org> 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 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-M68e48ed86fc864642d65752d Delivery options: https://agi.topicbox.com/groups/agi/subscription
