On Sep 10, 6:13 pm, Brent Meeker <[EMAIL PROTECTED]> wrote:

> Knowledge is usually defined as true belief that is casually connected to the
> facts that make it true.  That has nothing to do with work (free energy?
> computational steps?).  You can certainly do a lot of work and end up with a
> false belief.

The 'Bit and The Pendulum' (Tom Siegfried) is a good popular book
discussing the difference between Shannon information and knowledge.

Bayesian is no doubt very useful and powerful, but the trouble with
Bayesianism is when it starts to become a sort of 'substitute
religion'  and you have people claiming it's got all the answers to

It doesn't.  It really only deals with *prediction sequences*; you can
only assign a meaningful probabilities to something which if there is
something being predicted , something you can observe in the future.
That's as far as Bayesianisms can get you.  There is no way to go from
mere prediction sequences to assessing the *meaning* (semantics) of
information, no matter what clever manipulations the Bayesianism


I think we are due for yet another extension to logic, one which will
contain Bayesianism as a special case.

I think Bruno had it right, it's all Category Theory-  and make the
next big leap forward in logic, we need to start using the concepts
from Category Theory and apply them to logic, to develop a new logic
capable of going beyond Bayesianism and dealing with the semantics of
information.  But how?  Listen to this:

<b>Given two categories C and D a functor F from C to D can be thought
of as an *analogy* between C and D, because F has to map objects of C
to objects of D and arrows of C to arrows of D in such a way that the
compositional structure of the two categories is preserved.</b>

And therein lies the big clue suggesting that the concepts from
category theory can be used to develop a new logic of analogies.

And there I shall leave you for now.  See you around the galaxy :D
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