Looking into the grey: how Complex Adaptive Systems can enlighten our understanding of the world
Topic: Complex Adaptive Systems
by Zachary, Tim, and Philip, 2022 Cohort
Introduction#
Binaries plague our vision. The good ‘Allies’ versus the evil ‘Axis’ powers, the Enlighted and the rabble, the ugly contrasted with the beautiful. These simplifications mould how we perceive the world, which are often misleading.
The unknown confuses us; thus, we shy away from it. Yet, it is in the grey where we find pertinent patterns that shape our world. This primer aims to introduce a way to view these patterns, namely Complex Adaptive Systems (CAS).
Problem-solving: Holism vs Reductionism#
Traditional examination of complex issues utilises two analytical methods, reductionism and holism. Reductionism reduces complicated issues into their constituent parts. For example, an international relations scholar may look to the interests and actions of different sub-national actors, including departments, political leaders, and lobby groups, to form an opinion on a government’s policy position.
Holism is reductionism’s counterpart. While reductionism preaches examining individual building blocks, holism instead encourages the examiner look at systems as a unitary object. For example, a holist would not only view a house as bricks, plumbing, and wiring, but also consider the social, cultural and spiritual factors innate within someone’s home. Below is an example of holism when examining cars:
Diagram: The complexity of using cars, and how it could impact the social, economic and environmental interactions. “+” represents a positive relationship and “-“ represents a negative relationship. This graph is adopted from Dyball and Newell (2014).
Complex Adaptive Systems#
Complex adaptive systems combine both ideas’ strengths, integrating the building blocks approach of reductionism while focusing on the large-scale interactions that shape a system to evolve into something greater than its constituent parts.
And thus, we describe complex adaptive systems through the strengths of reductionism and holism. We ought to examine systems through their building blocks - the interaction of heterogeneous agents - and how they produce emergent patterns - the unitary object.
The interaction of agents in a system becomes more meaningful with improvements in information flow. This leads to adaptive systems - the ability to adapt and change to evolving environments. This manifestation of information flows between heterogeneous agents produces emergent patterns. In the absence of analytical models, however, these patterns are difficult to understand, let alone predict.
We wanted to understand why patterns were emerging in societies. For example, why is wearing jeans in fashion, and why and how do these fashion trends change overtime? Scholarship provides generalisable models, which can help us understand these nascent behaviours - we provide the informational cascade model as an analytical framework. The generalised form of the informational cascade model by Bikhchandani et al. (1992) attempts to explain the convergence of behaviour on one choice or action. When this occurs, agents ignore their own private information, and imitates their predecessors (Bikhchandani et al. 1992: 1000). This novel research can explain why the interaction of heterogeneous agents produce emergent patterns in complex adaptive systems.
With sustainable innovation, development and the rise of artificial intelligence dominating the discourse of many societies, we ought to find answers not in new ideas but in the search of unseen linkages between problems. We must utilise other frameworks like Complex Adaptive Systems that can better inform our approaches to ever complicated problems and opportunities.
Further Reading#
- Bikhchandani, S., Hirshleifer, D. and Welch, I. (1992) A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades, Journal of Political Economy, 100(5): 992-1026.
- Dyball, R. and Newell, B. (2014) Understanding Human Ecology: A System Approach to Sustainability, Routledge, London.
Disclaimer#
This content has been contributed by a student as part of a learning activity.
If there are inaccuracies, or opportunities for significant improvement on this topic, feedback is welcome on how to improve the resource.
You can improve articles on this topic as a student in "Unravelling Complexity", or by including the amendments in an email to: Chris.Browne@anu.edu.au