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 you saying 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."


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.  ;-)


  Algorithmic Thermodynamics

John C. Baez
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 relation dE =
    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 partition
function becomes uncomputable. Indeed, at these points the partition
    function 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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