In Short:
We need to go beyond current understandings of complexity that aim at understanding and controlling complex systems. Current complexity models are still based on reduction and thus, might miss important information in their analyses. New sense-making methods, based on cognitive, affective and behavioural perceptions, foster the emergence of holistic outcomes based on collective wisdom.
In the sustainability research and policy community, the traditional perspective of simple cause-and-effect relationships is increasingly giving way to an acknowledgment that our world is complex. “Complexity challenges the problem-solving model of separating issues into singularly defined parts and solving for the symptoms.”, states a UN report on risk, for example. Systems-thinking courses are now incorporated in various forms into most university curricula in the environmental and sustainability field. Despite these developments, I want to challenge the prevailing understanding of complexity and systems-thinking and invite you to explore yet another perspective on it.
The field of complexity science studies how small components of a system interact. Complex systems are defined as emergent, dynamical, unpredictable, and adaptive. While there are many schools of thought across physical, biological and social sciences that have evolved within complexity science since the 1940s, the dominant methods today focus on mathematics, modelling and numerical simulation.
Even socio-economic or political complex systems are often reduced through quantitative methods, such as network analysis or agent-based models. A set of variables, such as persons or organisations, is chosen, and inferences about their relations are made. The goal is to understand and control systems that were previously not accessible to scientific understanding (as described e.g. here).
Regarding the experiential learning principles (see Knowledge-Action Gap), complexity science currently relies solely on the cognitive sphere and ignores the affective and behavioural spheres.
What if we changed that? What if we stopped trying to tame and reduce complexity? How would we make sense of it?
I want to draw on a comparison that the systems thinker and artist Nora Bateson likes to make between two types of complexity. Complexity can describe a mathematical model of nodes and edges that reduces reality to a set of variables interacting in dynamic and non-linear ways. However, complexity can also describe a living system, such as a forest ecosystem, that is so complex that we cannot possibly understand all the interconnectedness that is taking place in it.
Instead of trying to reduce, understand and control its complexity, we dive into it with head, heart and hand (see Knowledge-Action Gap) and start making sense of it from a holistic perspective.
Linking this thought to the Three-Horizon-Thinking (see Looking Beyond the Horizon), linear cause-and-effect represents the H1 mindset, while complexity science, as described on the right side of the picture above, represents H2. It acknowledges the need to recognize complexity but still adheres to an H1 mindset of command and control. The work of Awledge follows an H3 paradigm of holistic sense-making that resists reductionism.
The following drawing helps explain this distinction in more detail.
The reductionist way of making sense of our complex reality, prevalent in most current scientific approaches, involves reducing the overwhelmingly complex reality through models and filters to understand them from a cognitive perspective (left figure side).
In economics, this might manifest as the assumption of uniform rational individuals. In medicine, it could be the neglect of women in most medical studies or the ignorance of cultural complexities in policy-making. Most research yields an ‘optimal solution’ and clear recommendations for action. However, people increasingly recognize that such a reductionist approach might filter out relevant ideas, problems, or entire groups of people, based on the underlying assumptions of these methods. Consequently, valuable insights or impulses for action are missed.
If we were to embrace approaches that acknowledge complexity rather than reducing it, trusting that tangible impulses for action can emerge from a practice that doesn’t aim solely at understanding and controlling a system, how would our outcomes and decision-making shift?
A growing body of methods, practices, and tools is emerging that holds space for complexity, utilizes holistic sense-making, and allows for the emergence of ideas, outcomes, and insights based on an H3 mindset. These outcomes differ from traditional problem-solving and decision-making as they are dynamic in time rather than stable. They emerge from the collective wisdom within a group or organization, rather than being prescribed from the outside (right figure side).
As these sense-making methods encompass the cognitive, affective, and behavioral spheres, outcomes and impulses for action are holistic, representing a broader perspective beyond a few dominant actors or theories. Consequently, outcomes from these practices and methods are more sustainable than top-down decision-making, which still prevails in our current policy landscape.