I am worried that engineering has become a mechanism for financial returns in an economic system that values profit above everything with the result that many engineers are unwittingly, or perhaps in a few cases wittingly, supporting the concentration of wealth into the hands of a few capitalists. At the start of the industrial revolution, when engineering innovation started to make a difference to the way we live and work, very few engineers foresaw the impact on the planet of the large scale provision to society of products and services. Nowadays most engineers understand the consequences for the environment of their work; however, many feel powerless to make substantial changes often because they are constrained by the profit-orientated goals of their employer or feel that they play a tiny role in a complex system. Complex systems are often characterised by self-organisation in which order appears without any centralised control or planning and by adaptation to change and experience. Such systems are familiar to many engineers and perhaps they do not, but should, think of the engineering profession as complex system capable of adaptation and self-organisation in which the actions and decisions of individual engineers will cause the emergence of a new order. Our individual impact might be tiny but by acting we influence others to act and the cumulative effect will emerge in ways that no one can predict – that’s emergence for you.
Sadly my vacation is finished [see ‘Relieving stress‘ on July 17th, 2019] and I have reconnected to the digital world, including the news media. Despite the sensational headlines and plenty of rhetoric from politicians, nothing very much appears to have really changed in the world. Yes, we have a new prime minister in the UK, who has a different agenda to the previous incumbent; however, the impact of actions by politicians on society and the economy seems rather limited unless the action represents a step change and is accompanied by appropriate resources. In addition, the consequences of such changes are often different to those anticipated by our leaders. Perhaps, this is because society is a global network with simple operating rules, some of which we know intuitively, and without a central control because governments exert only limited and local control. It is well-known in the scientific community that large networks, without central control but with simple operating rules, usually exhibit self-organising and non-trivial emergent behaviour. The emergent behaviour of a complex system cannot be predicted from the behaviour of its constituent components or sub-systems, i.e., the whole is more than the sum of its parts. The mathematical approach to describing such systems is to use non-linear dynamics with solutions lying in phase space. Modelling complex systems is difficult and interpreting the predictions is challenging; so, it is not surprising that when the actions of government have an impact then the outcomes are often unexpected and unintended. However, if global society can be considered as a complex system, then it would appear that its self-organising behaviour tends to blunt the effectiveness of many of the actions of government. This seems be a fortuitous regulatory mechanism that helps maintain the status quo. In addition, we tend to ignore phenomena whose complexity exceeds our powers of explanation, or we use over-simplified explanations [see ‘Is the world incomprehensible?‘ on March 15th, 2017 and Blind to complexity‘ on December 19th, 2018]. And, politicians are no exception to this tendency; so, they usually legislate based on simple ideology rather than rational consideration of the likely outcomes of change on the complex system we call society. And, this is probably a further regulatory mechanism.
However, all of this is evolving rapidly because a small number of tech companies have created a central control by grabbing the flow of data between us and they are using it to manipulate those simple operating rules. This appears to be weakening the self-organising and emergent characteristics of society so that the system can be controlled more easily without the influence of its constituent parts, i.e. us.
For a more straightforward explanation listen to Carole Cadwalladr’s TED talk on ‘Facebook’s role in Brexit – and the threat to democracy‘ or if you have more time on your hands then watch the new documentary movie ‘The Great Hack‘. My thanks to Gillian Tett in the FT last weekend who alerted me to the scale of the issue: ‘Data brokers: from poachers to gamekeepers?‘
When faced with complexity, we tend to seek order and simplicity. Most of us respond negatively to the uncertainty associated with complex systems and their apparent unpredictability. Complex systems can be characterised as large networks operating using simple rules but without central control which results in self-organising behaviour and non-trivial emergent behaviour. Emergent behaviour is the behaviour of the system that is not apparent or expected from the behaviour of its constituent parts [see ‘Emergent properties‘ on September 16th, 2015].
The philosopher, William Wimsatt observed that we tend to ignore phenomena whose complexity exceeds our predictive capability and our detection apparatus. This is problematic because we try to over-simplify our descriptions of complex systems. Occam’s razor is often mis-interpreted to mean that simple explanations are better ones, whereas in reality ‘everything should be made as simple as possible, but not simpler’, (which is often attributed to Einstein). This implies that our explanation and any mathematical model of a complex system, such as the nervous system in the image, will need to be complex. In mathematical terms, this will probably mean a non-linear dynamic model with a solution in the form of a phase portrait. ‘Non-linear’ because the response of the system not proportional to the stimulus inducing the response; ‘dynamic’ because the system changes with time; and a ‘phase portrait’ because the system can exist in many states, some stable and some unstable, dependent on its prior history; so, for instance for a pendulum, its phase portrait is a plot of all of its possible positions and velocities.
If all this sounds too hard, then you see why people shy away from using complex models to describe a complex system even when it is obvious that the system is complex and extremely unlikely to be adequately described by a linear model, such as for the nervous system in the image.
In other words, if we can’t see it and its too hard to think about it, then we pretend it’s not happening!
The thumbnail shows an image of a fruit-fly’s nervous system taken by Albert Cardona from HHMI Janelia Research Campus. The image won a Wellcome Image Award in 2015.
William C. Wimsatt, Randomness and perceived randomness in evolutionary biology, Synthese, 43(2):287-329, 1980.
For more on this topic see: ‘Is the world comprehensible?‘ on March 15th, 2017.