Publication date (electronic): 31 December 2009
The complexity of complexity theory: An innovative analysis
Steven E. Wallis earned his Ph.D. in 2006 at Fielding Graduate University, focusing on the rigorous analysis and integration of conceptual systems. He has a decade of experience as a facilitator and organizational development consultant in Northern California and a broad range of interdisciplinary interests. At Capella University, Steve mentors doctoral candidates. As Director for the Foundation for the Advancement of Social Theory (FAST) he supports emerging scholars who are working to identify rigorous paths for the validation of theory through critical metatheory and metapolicy analysis. His academic publications cover a range of fields including ethics, management, organizational change, and policy. His recent book “Avoiding Policy Failure” shows how a systems view of policy models can be used to estimate the effectiveness of policies before implementation as well as improving policies for reducing cost and improving results. Finally, Dr. Wallis serves as a Fulbright Specialist to help improve the capacity of academic institutions with a focus on theory, strategy, and policy.
As more scholars join the conversation around complexity theory (CT), it seems a useful time to ask ourselves if we are talking about the “same thing?” This concern is highlighted by the present survey, which finds more conflict than agreement between definitions. In contrast to the conflict, a path toward common ground may be found by applying the idea of a “robust” theory. A robust theory is expected to be more effective in application and more reasonably falsifiable. In this paper, Reflexive Dimensional Analysis (RDA) is used to analyze existing definitions of CT. These definitions are deconstructed, redefined as scalar dimensions, combined, and investigated to identify co-causal relationships. The robustness of CT is identified as 0.56 on a scale of zero to one. Paths for advancing the theory are suggested, with important implications for complexity science.
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