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*> Eugene Leitl wrote:
*

*> What woulld be interesting to know 1) has this practical relevance to
*

*> physics?
*

There are a couple of links. But I'm not sure whether they are practical.

Measurements convert statistical uncertainty (statistical entropy)

into randomness (algorithmic information content of the outcome).

Measurements decrease our ignorance about the state of the system,

but also increase the inefficiency (randomness) of the encoding data

process (the acquired information).

S = H + K (at equilibrium, i.e. reversible computation)

where S is the physical entropy, H is the Shannon entropy,

K measures randomness (or the algorithmic information content)

of the information outcome.

Performing measurements far from equilibrium, H (Shannon entropy,

or ignorance) decreases rapidly, but K (randomness, algorithmic

information content) increases slowly.

Fortunately (as Wojciech H. Zurek said) we live in a far from

equilibrium universe (or multiverse, etc.). So "It pays to measure".

R.P. Feynman [Simulating Physics with Computers, Int. J. Theor.

Phys., 21-1982, p. 467] considered the possibility that "there is

to be an exact simulation, that the computer will do exactly the same

as nature". And that "everything that happens in a finite volume of

space and time would have to be exactly analyzable with a finite

number of logical operations". Now this brings us directly to the

halting problem, computability, analog-digital, quantum computing, etc.

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