Algorithmic Thermodynamics

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'

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: John C. Baez, Mike Stay
• Keywords: Algorithmic, Algorithmic Entropy, CORR 2010, Education, Entropy