Mindset that Embraces Complexity

Complexity requires special treatment. By definition, complex implies that it cannot be “figured out”. Complex Adaptive Systems, such as product development organizations, operate under a ruleset different from our habitual approach used for example to operating within mechanical systems (see Reductionist Mentality). Complex Adaptive Systems adhere to the following laws:

  1. The system cannot be reduced to the sum of its components (emergence property)
  2. Can’t predict exact behavior (non-determinism)
  3. Small changes may lead to dramatic outcomes (non-linearity)

Following are the examples of 1-3 in the organizational context:

  1. Examples of Emergent Properties. Cognitive divergences of multiple collaborating individuals in the enterprise may produce qualitatively new class of product ideas, technological solutions and implementation strategies that neither of individuals could produce. Effective test automation results in improved team morale (new property), as a result of eliminating the blame game, fear of refactoring and so forth.
  2. Examples of non-determinism. Precise effort required to deliver a project can never be reliably estimated. It is impossible to predict whether a feature will be well-received by the customer or not. It is impossible to exactly forecast the economic gain of certain functionality delivered. And so on.
  3. Examples of non-linearity. A single method signature change in the codebase may lead to a dramatic breakdown of a large solution. A slight simplification of UX may open access to a vast array of system’s capabilities to the end user and produce significant economic benefit to the business. A seemingly slight divergence in the implementation strategy may dramatically increase/decrease effort estimate. When the teams operate at the limit of their optimal WIP, a slight overload may significantly delay deliverables.

Building a Lean-Agile capability in the enterprise without internalizing these fundamental tenets leads to very poor outcomes.

Complex systems require Second-Order Thinking to navigate through complex phenomena and effectively operate in the environment of uncertainty.