DarMM said:Summary: Seeking references for criticisms of Jaynes's information theoretic approach to Statistical Mechanics.
In his well known paper “Information Theory and Statistical Mechanics” Jaynes attempted to formulated statistical mechanics as "nothing more" than the inference theory of many body mechanical systems. I am looking for critiques of this approach. Also of use would be summaries or reviews of the "current state of debate".
Maxwell's demon is "driven out" by Szilard and precisely by applying the information theoretical approach to the notion of entropy.DEvens said:I have not read it. But that "nothing more" part is kind of worrying. How does he preclude Maxwell's Demon?
If I understand correctly, the usual way to preclude MD comes down to quantum mechanics. MD has to make observations.
Jaynes' approach to Statistical Mechanics is a Bayesian approach, which uses probability theory to describe the behavior of large systems of particles. It is based on the principle of maximum entropy, which states that the most likely distribution for a system is the one that maximizes its entropy, or disorder.
Some common criticisms of Jaynes' approach include the assumption of equilibrium and the use of subjective prior probabilities. Critics argue that systems may not always be in equilibrium and that subjective priors can introduce bias into the analysis.
Jaynes' approach differs from traditional approaches in that it uses probability theory and Bayesian methods to describe the behavior of systems, rather than relying on classical mechanics and deterministic equations.
There is ongoing debate and research regarding the validity of Jaynes' approach. Some studies have shown that it can accurately predict the behavior of certain systems, while others have found discrepancies with experimental data. Further research and testing is needed to fully evaluate the effectiveness of this approach.
Jaynes' approach has sparked much discussion and debate within the field of Statistical Mechanics. While some scientists have embraced his Bayesian approach, others have criticized it and continued to use traditional methods. However, his work has opened up new avenues for research and has encouraged the development of alternative approaches to understanding complex systems.