Complexity and  the Implications for  Leadership  Development

In order to respond effectively in today’s highly changing environment, organizations need managers that can perceive, understand and effectively work with complex and chaotic systems. To this end, models that incorporate complexity theory, like the Cynefin1 framework, are excellent tools for sense making and decision strategy. But developmental research has shown us that only 15 % of managers have consistent access to the cognitive and emotional capacity to operate effectively in complex and chaotic environments, implying that system models like Cynefin must be supplemented with individual and organizational developmental capacity building, in order to effectively address the demands on today’s organizations. Cynefin David Snowden, et al, developed the Cynefin framework for understanding organizational knowledge exchange, decision-making, strategy, and policy-making that incorporates perspectives from complexity theory. The framework expands the conventional view of organizations to challenge three underlying assumptions in organizational decision support and strategy: the assumptions of order, rational choice, and intent. The Cynefin framework describes four primary domains of interpretation and action: Known, Knowable, Complex, Chaotic, and can be used to describe contexts, situation analysis, domains of attention and work. Briefly, the Known domain encompasses situations where cause and effect, as well as solutions and strategies, are known and at hand; The Knowable domain encompasses causes and effects, solutions and strategies, that are not readily at hand but can be acquired through research or consultation with an expert. The Complex domain is the area of complexity theory in which cause and effect exist but are too complex or distributed to be knowable ahead of time. Patterns emerge in retrospect and do not necessarily persist. The Chaotic domain considers conditions in which the system is turbulent and no cause and effect relationship are discernable