By Jean Boulton.
What is a useful way to clarify the underpinning ontological ground of complexity? What can we learn from the work of Edgar Morin (2006), who distinguishes between those working within the frames of restricted and general complexity? And how are these frames relevant to practice?
Morin makes a distinction between:
- a framing of complexity that sits within the ontology of classical science, which he calls ‘restricted complexity’
- the ‘general complexity’ of the ‘real world,’ where general complexity is more paradoxical, more integrating, more challenging, ambiguous and uncertain – but also ripe with potential.
Restricted complexity emanates from the world of models, maps and mathematics. The aim is to find ways to represent the complexity of the real world, by finding a good map.
General complexity, by contrast, starts further back into the primordial mud, and champions the attainment of knowledge through wandering the ‘territory’. It is complexity that is beyond (or before) mathematics and cannot be entirely captured by models, flowcharts or causal loop diagrams. It is messier and more concerned with engaging with the rich nature of the terrain than a representation of that terrain. General complexity often starts with experiment and observation – of forests, cells, swirls in chemical systems, galaxies, social groups or societies – rather than with conceptual abstractions.
Both approaches to complexity are designed to help us determine how to act and live in the complex world; both see a role for models and maps, but they are situated within distinct ontologies.
The underlying ontologies
Restricted complexity is based on classical science which searches for “the hidden order that is the authentic reality of the universe” (Morin, 2006, 6). The models and mathematics required to achieve this are far from simple, but there is an underlying assumption of order to be uncovered and of stability rather than instability. The implication is that, with clever mathematics and computational techniques, we can represent and understand ‘real-life’ pretty well.
Those embracing the fullness of general complexity point out that with restricted complexity, “one still remains within the frameworks of classical science” (Morin, 2006:10) and that the approaches “remain in important respects merely reductionist” (Byrne and Callaghan 2014: 2). For example, mathematical models generally need to define boundaries, cannot easily handle paradox and must severely restrict the diversity and ability to learn when representing human actors. They are helpful in exploring ‘the now’ and less able to handle an emerging future (Boulton 2024: 129).
In contrast, general complexity aims to focus on the real-life situation itself, rather than a simplified representation of it. It is based on recognition that, for patterns to emerge, situations need to be open to their wider surroundings, and there must exist a level of diversity and variation amongst the constituents. There is also an irreducible, entwined inter-relationship between the micro and the macro. There is a degree of order or form, but it never entirely stabilises, and there is always a degree of disorder. Situations are inherently paradoxical, not entirely definable, and liable to change over time.
This paradoxical, elusive dance – between order and disorder, between stability and chaos, between form and formlessness, between certainty and uncertainty – is the stuff of general complexity. There is always an irreconcilable, intrinsic tension that must be embraced. There is no assumption that there is an objective ‘order behind appearances’. Features that are paradoxical, subjective, sometimes emerging and sometimes stabilising cannot be reduced to objective and definable parameters and dimensions, as in classical science. Moreover, this ‘messiness’, a messiness that cannot be captured by analytical means, is both inevitable and a largely positive quality of the world. Indeed, it is the source of novelty, emergence, resilience and change.
Relevance to practice
In practice, both ontologies have value. And the truth is that, even if we try, we can never work with all the information that is there, so in some sense or another we are always making choices about what to include and what to ignore, whether computational models are involved or not. If we were to try to know everything before we decide anything, it would take us all our time, and the situation, in the meantime, would have been changed by the process of looking!
Sometimes we like our complexity to be general, and sometimes we know it has to have a degree of restriction, depending on the context and our aims. We all adopt some form of framing, some form of modelling, if only through the way we talk about something. We all ‘abstract’ to a degree and go beyond merely immersing ourselves in the world’s complexities.
Without an ability to abstract and consider the implications of a complex world, we would struggle to make sense, make decisions and take action. But we need to take care to not become blind to our simplifying assumptions and wedded to our preferred methods. We should continue to remain alert to what is missing and what is shifting and keep an eye on the skittishness and ‘wilfulness’ of the territory, and its ability to confound us.
Closing questions
Does this resonate with how you think about complexity and tackle it in practice? Are there other ontologies or practices that you think are useful for understanding and dealing with complexity?
To find out more:
Boulton, J. (2024). The dao of complexity: Making sense and making waves in turbulent times. De Gruyter: Berlin, Germany. See especially Chapter 31 “The raw and the cooked of complexity,” which contains additional ideas and references.
Embracing complexity—Jean Boulton’s website. (Online): https://www.embracingcomplexity.com/.
References:
Byrne, D. and Callaghan, G. (2014) Complexity theory and the social sciences: The state of the art. 1st edition. Routledge: Milton Park, Oxfordshire, United Kingdom.
Morin, E. (2006). Restricted complexity, general complexity. In, C. Gershenson, D. Aerts and B. Edmonds. (eds.), Worldviews, science and us: Philosophy and complexity, World Scientific: Singapore.
Use of Artificial Intelligence (AI) Statement: Artificial intelligence was not used in the development of this i2Insights contribution or the work on which the contribution is based. (For i2Insights policy on artificial intelligence please see https://i2insights.org/contributing-to-i2insights/guidelines-for-authors/#artificial-intelligence.)
Biography: Jean Boulton PhD is a Fellow of the Institute of Physics and a visiting academic with the Universities of Cranfield and Bath, in the United Kingdom. She has been deeply involved in the science and philosophy of complexity since the mid-1990s and the honing of these ideas continues to inform her research, consultancy work and personal practice.