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As does Chesterton, less explicitly:
Mere light sophistry is the thing that I happen to despise most of all things, and it is perhaps a wholesome fact that this is the thing of which I am generally accused. I know nothing so contemptible as a mere paradox; a mere ingenious defence of the indefensible.
and at length.
I get the impression that he (thankfully!) eased off on that particular template as time went on.
I suspect most self-identified communists would baulk at the description of their ideology as "complete state control of many facets of life".
I will reframe this to make sure I understand it:
Virtue Ethics is like weightlifting. You gotta hit the gym if you want strong muscles. You gotta throw yourself into situations that cultivate virtue if you want to be able to act virtuously.
Consequentialism is like firefighting. You need to set yourself up somewhere with a firetruck and hoses and rebreathers and axes and a bunch of cohorts who are willing to run into a fire with you if you want to put out fires.
You can't put out fires by weightlifting, but when the time comes to actually rush into a fire, bust through some walls, and drag people out, you really should have been hitting the gym consistently for the past several months.
Here's how I think about the distinction on a meta-level:
"It is best to act for the greater good (and acting for the greater good often requires being awesome)."
vs.
"It is best to be an awesome person (and awesome people will consider the greater good)."
where ''acting for the greater good" means "having one's own utility function in sync with the aggregate utility function of all relevant agents" and "awesome" means "having one's own terminal goals in sync with 'deep' terminal goals (possibly inherent in being whatever one is)" (e.g. Sam Harris/Aristotle-style 'flourishing').
Cool; I take that back. Sorry for not reading closely enough.
Yes, but in this setting maximum a posteriori (MAP) doesn't make any sense from a Bayesian perspective. Maximum a posteriori is supposed to be a point estimate of the posterior, but in this case, the MAP solution will be sparse, whereas the posterior given a laplacian prior will place zero mass on sparse solutions. So the MAP estimate doesn't even qualitatively approximate the posterior.
Ah, good point. It's like the prior, considered as a regularizer, is too "soft" to encode the constraint we want.
A Bayesian could respond that we rarely actually want sparse solutions -- in what situation is a physical parameter identically zero? -- but rather solutions which have many near-zeroes with high probability. The posterior would satisfy this I think. In this sense a Bayesian could justify the Laplace prior as approximating a so-called "slab-and-spike" prior (which I believe leads to combinatorial intractability similar to the fully L0 solution).
Also, without L0 the frequentist doesn't get fully sparse solutions either. The shrinkage is gradual; sometimes there are many tiny coefficients along the regularization path.
[FWIW I like the logical view of probability, but don't hold a strong Bayesian position. What seems most important to me is getting the semantics of both Bayesian (= conditional on the data) and frequentist (= unconditional, and dealing with the unknowns in some potentially nonprobabilistic way) statements right. Maybe there'd be less confusion -- and more use of Bayes in science -- if "inference" were reserved for the former and "estimation" for the latter.]
Yes, I mixed up x and y, good catch. It's not trivial for me to fix this while maintaining wordpress-compatibility, but I'll try to do so in the next few days.
This problem is called the "compressed sensing" problem and is most famously used to speed up MRI scans. However it has also had a multitude of other applications, see here: http://en.wikipedia.org/wiki/Compressed_sensing#Applications.
Many L1 constraint-based algorithms (for example the LASSO) can be interpreted as producing maximum a posteriori Bayesian point estimates with Laplace (= double exponential) priors on the coefficients.
So here's a question for anyone who thinks the concept of a utility monster is coherent and/or plausible:
The utility monster allegedly derives more utility from whatever than whoever else, or doesn't experience any diminishing returns, etc. etc.
Those are all facts about the utility monster's utility function.
But why should that affect the value of the utility monster's term in my utility function?
In other words: granting that the utility monster experiences arbitrarily large amounts of utility (and granting the even more problematic thesis that experienced utility is intersubjectively comparable)... why should I care?
This is just the (intended) critique of utilitarianism itself, which says that the utility functions of others are (in aggregate) exactly what you should care about.
Can something be optimization-like without being ontologically mental? In other words, if a higher level is a universal Turing machine that devotes more computing resources to other Turing machines depending on how many 1s they've written so far as opposed to 0s, is that the sort of optimization-like thing we're talking about? I'm assuming you don't mean anything intrinsically teleological.
What does "intrinsically teleological" mean?
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This is one of the more brilliant illustrations I've seen, and I suspect that what it illustrates is that the Deep Wisdom of a statement is mostly the cumulative Deep Wisdom points scored by each deep-sounding concept. Thus, reversing the meaning of a sentence has little effect on its Deep Wisdom points, so long as the same concepts are being invoked.
Opposing Bohr's interpretation.