p4wnc6 comments on A Defense of Naive Metaethics - Less Wrong
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I'd be very gracious if you could take a look at my recent question and the comments. Your statement
is interesting to me. What is a counter-argument to the claim that the only way that one could claim that " "X is true" means something" is to unpack the statement "X is true" all the way down to amplitudes over configurations (perhaps in a subspace of configuration space that highly factorizes over 'statistically common arrangements of particles in human brains correlating to mathematical conclusions' or something.
Where do the intuition-sympathizers stand on the issue of logical names?
I don't think something like 'ought' can intuitively point to something that has ontological ramifications. If there is any "intuition" to it, why is it unsatisfactory to think it's merely an evolutionary effect?
From the original post above, I find a point of contention with
'I ought to do X' does correspond to something that exists... namely, some distribution over configurations of human minds. It's a proposition like any other, like 'that sign is red' for example. You can track down a fully empirical and quantifiable descriptor of 'I ought to do X' with some sufficiently accurate model and measuring devices with sufficient precision. States of knowledge about what one 'ought' to do are states of knowledge like any others. When tracking down the physics of 'Ought', it will be fleshed out with some nuanced, perhaps situationally specific, definition that relates it to other existing entities.
I guess more succinctly, there is no abstract concept of 'ought'. The label 'ought' just refers to an algorithm A, an outcome desired from that algorithm O, an input space of things the algorithm can operate on, X, an assessment of the probability that the outcome happens under the algorithm, P(A(X) = O). Up to the limit of sensory fidelity, this is all in principle experimentally detectable, no?
Just to be a little clearer: saying that "I ought to do X" means "There exists some goal Y such that I want to achieve Y; there exists some set of variables D which I can manipulate to bring about the achievement of Y; X is an algorithm for manipulating variables in D to produce effect Y, and according to my current state of knowledge, I assess that the probability of this model of X(D) yielding Y is high enough such that whatever physical resources it costs me to attempt X(D), as a Bayesian, the trade-off works out in favor of actually doing it. That is, Payoff(Y) * P(I was right in modeling the algorithm X(D) as producing Y) > Cost(~Y)*P(I was incorrect in modeling the algorithm X(D)), or some similar decision rule.