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Abstract : |
This brief essay reviews an approach to defining and then detecting the emergence of complexity in nonlinear processes. It is, in fact, a synopsis of Reference [1] that leaves out the technical details in an attempt to clarify the motivations behind the approach. The central puzzle addressed is how we as scientists--- or, for that matter, how adaptive agents evolving in populations--- ever "discover " anything new in our worlds, when it appears that all we can describe is expressed in the language of our current understanding. One resolution--- hierarchical machine reconstruction--- is proposed. Along the way, complexity metrics for detecting structure and quantifying emergence, along with an analysis of the constraints on the dynamics of innovation, are outlined. The approach turns on a synthesis of tools from dynamical systems, computation, and inductive inference., |
