Angelo Vulpiani - Università di Roma La Sapienza # The role of chaos for the foundation of statistical mechanics # A basic question of the Statistical Mechanics, starting from the Boltzmann's ergodic hypothesis, is the connection between the dynamics and the statistical properties. This is a rather difficult task and, in spite of the mathematical progress, for long time basically ergodic theory had a marginal relevance in the development of the statistical mechanics (at least in the physics community). Partially this was due to a widely spreaded opinion (based also on the belief of influential scientists as Landau) on the key role of the many degrees of freedom and the practical irrelevance of ergodicity. This point of view found a mathematical support on some results by Khinchin.
On the other hand the discovery of the deterministic chaos beyond its e undoubted relevance for many natural phenomena, showed how the similar statistical features observed in systems with many degrees of freedom, can be generated also by the presence of deterministic chaos in simple systems.
Even after many years, there is not a consensus on the basic conditions which should ensure the validity of the statistical mechanics. Roughly speaking the two extreme positions are the "traditional" one, for which the main ingredient is the presence of many degrees of freedom and the "innovative" one which considers chaos a crucial requirement to develop a statistical approach.
We discusse the role of ergodicity and chaos for the validity of statistical laws. Detailed studies show in a clear way that chaos is not a fundamental ingredient for the validity of the equilibrium statistical mechanics. Therefore the point of view that good statistical properties need chaos is unnecessarily demanding: even in the absence of chaos, one can have (according to Khichin ideas) a good agreement between the time averages and the predictions by the equilibrium statistical mechanics.
Basically one can say that thermodynamics can be seen as an emergent property of large systems.