The existence of the typical set is key for the consistence of the ensemble formalization of statistical mechanics. We demonstrate here that the typical set can be defined and characterized for a general class of stochastic processes. This includes processes showing arbitrary path dependence, long range correlations or dynamic sampling spaces. We show how the typical set is characterized from general forms of entropy and how one can transform these general entropic forms into extensive functionals and, in some cases, to Shannon path entropy. The definition of the typical set and generalized forms of entropy for systems with arbitrary phase space growth may help to provide an ensemble picture for the thermodynamic paths of many systems away from equilibrium. In particular, we argue that a theory of expanding/shrinking phase spaces in processes displaying an intrinsic degree of stochasticity may lead to new frameworks for exploring the emergence of complexity and robust properties in open ended evolutionary systems and, in particular, of biological systems.
翻译:典型数据集的存在是统计机理整体正规化的关键所在。我们在这里表明,典型数据集可以定义和定性为一般类随机随机性过程,其中包括显示任意路径依赖性的过程、长距离相关性或动态抽样空间。我们展示了典型数据集如何从一般的酶状形式来定性,如何将这些一般的昆虫形态转化为广泛的功能,并在某些情况下转变为香农路径酶。为空间任意阶段增长的系统定义典型的成套和通用的酶状,可能有助于为远离平衡的许多系统的热动力路径提供共同图象。我们特别指出,在显示内在程度随机性的过程中,扩大/缩小阶段空间的理论可能导致新的框架,用以探索在开放的进化系统,特别是生物系统中出现的复杂和稳健的特性。