复杂系统需要一个理论大框架吗？如果需要，首先必须解决通用复杂性度量的问题，也就是用定量的方式回答什么系统什么行为是复杂的，什么系统什么行为又是不复杂的。如果这种复杂性度量可以包容经典信息度量（也就是信息熵）那就更妙了。因为经典的统计物理框架就建立在熵的基础上，有了这种度量，我们就可以将原有的框架几乎原封不动地纳入到新框架之中，成为包括复杂系统在内的统一的广义统计力学框架。

      憋了很多年的一篇论文最近终于在Scientific Reports上发出来了（2013年4月5日）。题目取的还是比较吸引眼球的：“Unifying complexity and information”。文章编号srep01585。因为srep本来就是开放获取的，这里就不贴内容了。贴个链接和摘要吧：http://www.nature.com/srep/2013/130405/srep01585/full/srep01585.html。

Complex systems, arising in many contexts in the computer,life, social, and physical sciences, have not shared a generally-accepted complexity measure playing a fundamental role as the Shannon entropy H in statistical mechanics. Superficially-conflicting criteria of complexity measurement, i.e. complexity-randomness (C-R) relations, have given rise to a special measure intrinsically adaptable to more than  one criterion. However, deep causes of the conflict and the adaptability are not much clear. Here I  trace the root of each representative or adaptable measure to its particular universal data-generating  or -regenerating model (UDGM or UDRM). A representative measure for deterministic dynamical systems is found as a counterpart of the H for random process, clearly redefining the boundary of  different criteria. And a specific UDRM achieving the intrinsic daptability enables a general information measure that ultimately solves all major disputes. This work encourages a single framework coving deterministic  systems, statistical mechanics and real-world living organisms.