Britannica (reference below) defines complexity as “a scientific theory which asserts that some systems display behavioral phenomena that are completely inexplicable by any conventional analysis of the systems’ constituent parts.”
Britannica goes on to say:
“These phenomena, commonly referred to as emergent behaviour, seem to occur in many complex systems involving living organisms, such as a stock market or the human brain. For instance, complexity theorists see a stock market crash as an emergent response of a complex monetary system to the actions of myriad individual investors; human consciousness is seen as an emergent property of a complex network of neurons in the brain. Precisely how to model such emergence – that is, to devise mathematical laws that will allow emergent behaviour to be explained and even predicted – is a major problem that has yet to be solved by complexity theorists. The effort to establish a solid theoretical foundation has attracted mathematicians, physicists, biologists, economists, and others, making the study of complexity an exciting and evolving new scientific theory.”
Paul Cairney (video link on right and reference below) says:
“Complexity theory has been applied to a wide range of activity, from the swarming behaviour of bees, the weather and the function of the brain, to social and political systems. The argument is that all such systems have common properties, including:
- A complex system is greater than the sum of its parts; those parts are interdependent – elements interact with each other, share information and combine to produce systemic behaviour.
- Some attempts to influence complex systems are dampened (negative feedback) while others are ampliﬁed (positive feedback). Small actions can have large effects and large actions can have small effects.
- Complex systems are particularly sensitive to initial conditions that produce a long-term momentum or ‘path dependence’.
- They exhibit ‘emergence’, or behaviour that results from the interaction between elements at a local level rather than central direction.
- They may contain ‘strange attractors’ or demonstrate extended regularities of behaviour which may be interrupted by short bursts of change.
“As you might expect from a theory of many things, the language is vague and needs some interpretation in each field. In the policymaking field, the identification of a complex system is often used to make the following suggestions:
- Law-like behaviour is difﬁcult to identify – so a policy that was successful in one context may not have the same effect in another.
- Policymaking systems are difﬁcult to control; policy makers should not be surprised when their policy interventions do not have the desired effect.
- Policy makers in the UK have been too driven by the idea of order, maintaining rigid hierarchies and producing top-down, centrally driven policy strategies. An attachment to performance indicators, to monitor and control local actors, may simply result in policy failure and demoralised policymakers.
- Policymaking systems or their environments change quickly. Therefore, organisations must adapt quickly and not rely on a single policy strategy.
“On this basis, there is a tendency in the literature to encourage the delegation of decision-making to local actors:
- Rely less on central government driven targets, in favour of giving local organisations more freedom to learn from their experience and adapt to their rapidly-changing environment.
- To deal with uncertainty and change, encourage trial-and-error projects, or pilots, that can provide lessons, or be adopted or rejected, relatively quickly.
- Encourage better ways to deal with alleged failure by treating ‘errors’ as sources of learning (rather than a means to punish organisations) or setting more realistic parameters for success/ failure.
- Encourage a greater understanding, within the public sector, of the implications of complex systems and terms such as ‘emergence’ or ‘feedback loops’.
“In other words, this literature, when applied to policymaking, tends to encourage a movement from centrally driven targets and performance indicators towards a more flexible understanding of rules and targets by local actors who are more able to understand and adapt to rapidly-changing local circumstances.”
Brian Klaas, in his 2024 book, Fluke – Chance, Chaos, and Why Everything We Do Matters illustrates some of the features of complexity with recent analyses of locust swarming:
“Scientists have long been perplexed by why swarms form. Recent research may have finally solved those puzzles – and it’s all about density. When there are fewer than seventeen per square meter, each locust keeps to itself. The locusts’ movements lack coordination or purpose. Predicting their path is impossible because it’s so prone to fluctuations without clear patterns. It’s almost complete disorder. Each locust is mostly unaffected by others. Isolation and independence, rather than connection and interdependence, define the life of a solitary locust.
“When more locusts join the party, their behavior starts to shift. At medium densities, of an average of twenty-four to sixty-one locusts per square meter, they gather together in small groups. They move somewhat in unison, but these mini-swarms are independent. Each semiorganized cluster will move as one, but there is no coordinated motion between groups. They’re more like cliques in a high school than an army. And like cliques, they can be quite erratic, rapidly changing direction in an instant, as though they’re chasing one fad before darting toward another. Each locust can sway the clique, but it won’t affect other cliques.
“Locusts begin to march as a unified swarm at precisely 73.7 locusts per square meter (don’t ask how or why the locusts settled on that specific density; nature guards many secrets). “It’s a fairly firm tipping point,” Jerome Buhl tells me, a professor at the University of Adelaide, who conducted the research. At such teeming concentrations, marching emerges. These dense swarms are, by far, the most stable and predictable form for the gregarious locusts. They move as a unified whole, an arrangement that is ruthlessly enforced. If a locust moves against the swarm, it will be eaten, a cannibalistic punishment that ensures the swarm stays together. And it does. The cloud marches as one.
“Despite this ruthlessly enforced order, it’s impossible to predict where the frenzied locusts will go next, a similar erraticism to what we often see with a flock of birds swooping in a murmuration across the sky or a school of fish flitting in and out of coral reefs. “In the context of our laboratory experiments,” Buhl notes, “we have in fact shown that the direction changes are purely random and unpredictable.” That’s a bit of a problem if you’re a government hoping to spray pesticides in the right place, or a nineteenth-century farmer trying to position your hopper-dozer where the locusts are likely to arrive next. This is what we might call the paradox of the swarm. Out of complete chaos, the locusts produce astonishing order. But wait long enough and the swarm’s overall movement is complex and unpredictable. They march in unison, then suddenly switch direction without warning.
“It’s not a perfect analogy – we aren’t insects – but humans have, over thousands of years, transitioned from societies that mirror the medium-density locusts to a high-density swarm. We evolved to live in small, isolated mini-swarms. Now, all of us live in an enormous one, more frenzied and fragile than ever before.” (pp. 83-84).
Roger Martin and his colleagues at the Rotman School of Management have developed an approach to addressing complexity in management: Practicing Integrative Thinking.
Writing in the Harvard Business Review, Helga Nowotny (reference below) says that we need to confront the “embarrassment of complexity – when it dawns on us that the categories we normally use to neatly separate issues or problems fall far short of corresponding to the real world, with all its non-linear dynamical inter-linkages.”
“The truth is that complex systems are beset and energized by a phenomenon called non-linear dynamics. In other words, what produces complexity is not so much the presence of many direct cause-effect links which operate with subtlety versus precision, but rather the presence of indirect, non-linear relationships between the variables, parts, and dimensions of the whole. What make complex systems so complex, therefore, are their multiple feedback loops and their indirect cause-effect relations which, moreover, play out at different speeds and on different time scales.”
Britannica, Complexity, at https://www.britannica.com/science/complexity-scientific-theory, accessed 12 February 2024.
Paul Cairney (2013), Policy Concepts in 1000 Words: Complex Systems, at https://paulcairney.wordpress.com/2013/11/01/policy-concepts-in-1000-words-complex-systems/, including a 16-minute podcast.
Brian Klaas (2024). Fluke: Why Small Changes Make a Big Difference. Scribner, An Imprint of Simon & Schuster, LLC, New York.
Helga Nowotny (2013), The Embarrassment of Complexity, Harvard Business Review, 10 October 2013, at https://hbr.org/2013/10/the-embarrassment-of-complexity, accessed 27 February 2016.
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Page created by: Ian Clark, last modified on 13 February 2024.
Image: Tereza (Za) Procházková, at https://zasmastersproject.wordpress.com/2013/03/14/paul-cairney-complexity-theory-complex-adaptive-systems/, accessed 2 April 2016.