On 1/17/2013 9:25 PM, Stephen P. King wrote:

On 1/17/2013 4:21 PM, Russell Standish wrote:From just the abstract alone, I can't see how this differs from the Solomonff universal prior?Hi Russell,OK, is that a good thing? It seems to me that it is. Are yousaying that the content of the paper is trivial?

`Did you see the part about how it is "that it is not computable and`

`thus can only be approximated in practice."`

Cheers On Wed, Jan 16, 2013 at 07:29:33PM -0500, Stephen P. King wrote:Dear Bruno and Friends, The paper that I have been waiting a long time for. ;-) http://arxiv.org/abs/1010.2067 Algorithmic Thermodynamics John C. Baez <http://arxiv.org/find/math-ph,math/1/au:+Baez_J/0/1/0/all/0/1>,Mike Stay <http://arxiv.org/find/math-ph,math/1/au:+Stay_M/0/1/0/all/0/1> (Submitted on 11 Oct 2010) Algorithmic entropy can be seen as a special case of entropy as studied in statistical mechanics. This viewpoint allows us to apply many techniques developed for use in thermodynamics to the subject of algorithmic information theory. In particular, suppose we fix a universal prefix-free Turing machine and let X be the set of programs that halt for this machine. Then we can regard X as a set of 'microstates', and treat any function on X as an 'observable'. For any collection of observables, we can study the Gibbs ensemble that maximizes entropy subject to constraints on expected values of these observables. We illustrate this by taking the log runtime, length, and output of a program as observables analogous to the energy E, volume V and number of molecules N in a container of gas. The conjugate variables of these observables allow us to define quantities which we call the 'algorithmic temperature' T, 'algorithmic pressure' P and algorithmic potential' mu, since they are analogous to the temperature, pressure and chemical potential.We derive an analogue of the fundamental thermodynamic relationdE =T dS - P d V + mu dN, and use it to study thermodynamic cycles analogous to those for heat engines. We also investigate the values of T, P and mu for which the partition function converges. At some points on the boundary of this domain of convergence, the partitionfunction becomes uncomputable. Indeed, at these points thepartitionfunction itself has nontrivial algorithmic entropy. Now to discuss how this is useful to define a local notion of a measure for COMP.

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