Exploration of a complex environment is subject to the following inherent difficulties:
Uncertainty and unknown unknowns
Path-dependency of change
Complex cause and effect relations
Resistance to simplification
Active, ever-changing & increasing complexity
This has the following consequences:
Uncertainty and Unknown Unknowns
Not only are possible solutions hidden, but many are also unknown. One will even have difficulty describing the problem correctly to take action. This makes solutions non-obvious and thus looking for these directly does not work. Possible solutions emerge and evolve together with one’s understanding of the environment and the problem: all three evolve in conjunction.
Path-Dependency of Change
History, (current) state, and context (even beyond the scope) are important as all these strongly influence possible future developments. Although being that influential, many of these factors are impossible to research, let alone control. What works in the future is thus impossible to predict nor control, but can only be influenced.
Actual Exploration by Experimentation is the closest one can get to proof of ‘what is’ and ‘how it works’. The Design Thinking approach is Solution-Led, to learn about path-dependencies while working towards a solution.
Complex Cause and Effect Relations
Relationships between cause and effect are complex and hidden. The relationships can only be deducted in retrospect through Experimentation.
Many layers or mechanisms may exist between them, effects may be positive, negative, inverting, large, small, linear, exponential, etc. Effects may be unpredictable and even be the opposite of what was expected: don’t assume it will work, test it.
The consequences are that the Outcomes of a Design Thinking initiative include possible solutions, the learnings, as well as the potential and possible value of the outcomes, and are never predetermined. Progress and Success are thus defined differently than in delivery projects.
The Design Thinking approach is Solution-Led, to enable repeatability and relevance of (relations in) the outcomes in such an environment.
Resistance to Simplification
Fixing or reducing one’s scope, for example by narrowing of focus on known issues or familiar considerations, to reduce the overall complexity at hand is likely to have significant side-effects regarding relevance and value of potential outcomes.
Also a split into subsystems is likely to eliminate emergent behavior between parts (see Systems Thinking and Emergence), as well as incur re-integration costs (going back from the sub-problem to the whole).
The side-effects of some of these (over-)simplifications are significant, and can quickly mean irrelevance for the outcomes gained.
As an alternative, the foundations for Design Thinking listed on the 3 Foundations Reference page (and in particular the Capability Foundation) are used to manage the Complexity of the task and create a path forward without (over-)simplification to maintain relevance.
The approach includes a careful balance between Creativity and a systematic approach (Systems Thinking), as well as a balance between speculation (see Conjectures) and the requirement for proof (see Proof of Concept). See also the other Process and Artifact elements on their reference page to learn how the approach is constructed.
Active, ever-Changing & Increasing Complexity
Changes are pervasive and continuous, mainly hidden, and outside one’s control. Change drivers and relations change over time without notice. This creates time dependencies (‘it worked before, but not anymore’), as well as limited validity (‘the time is right, but only now or not yet’), and thus also creates urgency.
As systems evolve, Complexity increases. Sometimes the actions and experiments of participants are a factor of disturbance themselves: an experiment itself enlists a reaction from the system and changes it: the system is active, alive and reacts.