Posted on Oct 06, 2017 by Anisha
From Chaos we can derive order – If somewhat complex in its nature.
When dabbling with chaos theory and its complexity, a simple geographer like me can quickly turn and run back to my roots: finding a peaceful riverside spot where I can measure the river’s velocity using just a pole and oranges all to reassure myself that the world is quite normal and simple to assess.
Yet increasingly, to explain complex phenomenon inexplicable by traditional theories we integrate ideas derived from chaos theory, cognitive psychology, computer science, evolutionary biology, general systems theory, fuzzy logic, information theory and other related fields. This means we now deal with the natural and artificial systems as they are rather than simplifying them or breaking them down into their constituent parts. It recognises that complex behaviour emerges from a few simple rules and that all of these systems are networks of many interdependent parts which interact according to those rules.
Indeed, complexity is now one of the fastest growing and pervasive branches of general science. With the fascination of the non-linear and the ‘chaotic’ (NB last article), there is a constant human drive to try to understand and model the systems and processes that appear to be highly complex in nature.
Complex systems by their nature can have many variables. If these are out of users’ control, e.g. the weather, then when applying them to ideas such as IoT to create a response to the factors it is important to understand time lapse before modelling the system. The system must be designed to consider the time interval between cause and effect – the processes and influences of the mechanical response to the input. It is also important to understand the effect of one action or information source becoming dominant. Does this result in the rest of the variables having little or no impact and thus become wasteful on processing power and speed of the control system? If multiple complex systems are interacting with each other does one system’s response create an endless loop triggering a mechanical response which propagates back through the loop? If this happens, the natural trigger no longer has the intended response on the system.
Geography is particularly influenced and impacted upon in this arena. As we map the complex, and generate informative visualisations and dynamically changing, interactive maps, we are allowing the vocabulary of the geographer to be heard.
Whilst cartography remains the optimal tool, it will increasingly be the visualisation experience that will gain the plaudits and use. We have turned to the cloud with its huge processing capability and, by doing this and moving away from hardware constraints, we are able to deliver actionable information in minutes rather than hours.
We are living in an era of ‘technology abundance’. It is thanks to the decision of Amazon in 2006 to go down the route of Amazon Web Services, that the opportunity to take advantage of a world of complex data is here. The ability to decouple technology and reapply elsewhere is allowing tools previously locked down to one set of data, or handling one specific problem to be re-tasked and re-purposed to handle multiple questions in multiple environments and, in the case of the geographer, multiple locations.
As an example of complex systems reacting with each other we can look ahead a few years to the arrival of automated cars; in particular, the idea of the automated taxi and the simple problem of, “I am here - I would like to go there.” It is already possible to order and pay for a taxi via a mobile device but add to this the variables of time, number of people, etc. and in the future there will be multiple systems interacting with this one taxi. Most of the vehicles might be using a sat nav which primarily uses motorways and A roads, thus ignoring the rest of the road network. However, most systems use historic and live data to guide. In an automated world we create a complex system where all these parts need to work together. Will the current algorithms need to change? What issues will they cause the traffic of the future? What about those pesky old-school people who want to drive themselves?
The single question of change detection in a complex environment continues to have profound and challenging requirements under the hood. Indeed, the very need to also consider “No-Change” brings huge benefits to the end consumer. If we could automate systems to consume, process and deliver a simple message that ‘Nothing has changed’ we can concentrate on the change that matters.
CIO and Director