Approaches to adaptive change – a brief review

As mentioned in the previous blog, I have recently finished the draft of a comparative review of eight existing approaches that are similar to Adaptive Iteration.  These eight approaches are listed in the table below, together with a listing of how each approach describes the the four phases of adaptive iteration.   As can be seen from the table, there is considerable similarity between the eight approaches, but the review also identified a range of subtle but significant variations, most of which are due to the context for which each approach was first developed.  A brief summary of each of eight approaches is provided below.  A more detailed summary, together with some comparative analysis is provided in the White Paper.



Phase of Iteration



Observe/ Sense





Experiential Learning

Reflective Observation

Abstract Conceptualiz-ation

Active Experimentation

Concrete Experience


OODA Cycle



Decide (Hypothesize)

         Act           (Test)

Deming/ Shewhart


Check/Study (Observe)

     Act       (Learn)


       Do          (Test)


Adaptive Loop






Organizational Learning Cycle






Cynefin: Complex Domain





Lean Startup





Adaptive Action


So What?

Now What?


Adaptive Iteration





Table 1.  Variants of the Adaptive Iteration approach

Kolb’s Experiential Learning model emphasizes concrete experience (action) as the source of learning.  Kolb highlights that Experiential Learning requires that the learner have four kinds of abilities – concrete experience, reflective observation, abstract conceptualization, and active experimentation. The effectiveness of Experiential Learning depends on the extent to which the learner can apply and integrate all four abilities to an experience.  Kolb believes that the ability to resolve and integrate the inherent conflicts between the four “adaptive modes” (abilities) is a “hallmark of true creativity and growth”.

The OODA (Observe-Orient-Decide-Act) loop was developed by John Boyd, a fighter pilot in the US Air Force, to explain why U.S. pilots flying F-86s fared so well in air-to-air combat against enemy MiGs during the Korean War.  Boyd subsequently developed a reputation as a  military strategist and ‘thinker’.   The simplicity of the OODA Loop model led to some criticism of its suitability for more general use (outside combat situations) and this led Boyd to propose a more elaborate version of the model.  In particular, the elaborated version included more feedback paths and provided for contextual considerations, especially for the Orient step.

The PDSA/PDCA (Plan-Do-Study-Act/Plan-Do-Check-Act) Cycle was developed in an early form by Walter Shewhart and refined and enhanced by W. Edwards Deming.  It was developed primarily as a participative approach to the continuous monitoring and improvement of manufacturing (production) processes.  It is one of the core methods of the Total Quality / Continuous Improvement approaches developed and refined in Japan through the 1950s, 1960s and 1970s and subsequently adopted widely by manufacturers in western countries.  Deming continued to refine and adapt the ‘Shewhart Cycle’, as he called it, and the terminology in the version published in 1986 is more applicable to less structured and predictable processes:  ‘Observe’ replaces ‘Check/Study’, ‘Learn’ replaces ‘Act’, and ‘Test’ replaces ‘Do’.

Stephan Haeckel incorporates his Adaptive Loop into his design for Sense-and-Respond organizations.  Haeckel’s Sense-and-Respond organizations are those that deliberately pursue the ability to dynamically sense market needs and rapidly organize to fulfil them.  This is in contrast with the more conventional Predict-and-Plan organizations which, because they are not structured for rapid response to market changes, place greater emphasis on predicting the market and then using those predictions as the basis for future plans.  The Adaptive Loop is less of a loop and more a repeated application of a linear Sense-Interpret-Decide-Act process.  It is distinguished by the precision and short cycle time of that end to end process.

Nancy Dixon uses an iterative model, called the Organizational Learning Cycle, to describe the requirements for organizational learning.  Not surprisingly given Dixon’s focus on learning rather than action, her emphasis tends be on people-related structures and processes.  The emphasis is on creating shared and contextually relevant meaning through widespread collection, sharing and interpretation of information.  As with Haeckel, there is limited emphasis on the creation of feedback based learning.

In their Cynefin sensemaking framework, David Snowden and Mary Boone propose a Probe-Sense-Respond approach as one of a suite of leadership actions in the Complex domain.  The Cynefin Complex domain corresponds with the domain of interest in this review, that is, domains that are partially structured, emergent and partially predictable.  Snowden emphasizes that the Probe phase consists primarily of safe-to-fail experiments that both test a hypothesis about potential improvements and generate additional learning about the nature of the Complex System.  He also allows the possibility of multiple parallel experiments to increase the scope and depth of the learning.  Snowden also advocates the use oblique or indirect approaches to identifying improvements and encourages the introduction of diverse perspectives by incorporating related-but-different expertise.

According to Wikipedia: “Lean Startup” is an approach for launching businesses and products, that relies on validated learning, scientific experimentation, and iterative product releases to shorten product development cycles, measure progress, and gain valuable customer feedback.  The Lean Startup approach began as a methodology for (software based) entrepreneurial startup activities.  However, it is increasingly being applied within large companies to address situations with that have significant levels of uncertainty – a defining characteristic of the Lean Startup approach.

Glenda Eoyang and Royce Holladay of the Human Systems Design Institute use a three phase iterative ‘Adaptive Action’ approach to address uncertain and unpredictable situations.  The approach draws heavily on the concepts and perspectives of Complexity Science, particularly those relating to the self-organizing behaviour of Complex Adaptive Systems.  Central to the Adaptive Action approach is the Containers, Differences and Exchanges (CDE) construct.  The language and case examples in Adaptive Action: Leveraging Uncertainty in Your Organization focus primarily on organizational development, organizational behaviour and leadership.  No doubt, the general approach has wider application, but it is unclear whether the current formulation and language of Adaptive Action will stimulate broad application outside the organizational/leadership context.  That remains to be seen.

We propose Adaptive Iteration as a framework to develop the core capabilities necessary to develop agility and adaptive capacity in organizations.  Adaptive Iteration emphasizes the co-evolution of learning and action and the importance of a clear and shared purpose to guide the adaptive process.  It draws on the lessons and approaches of those who deal with unpredictability and uncertainty every day – entrepreneurs, designers, researchers and developers.

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