TEDxSPSU was held on March 12, 2011 at Sir Padampat Singhania University, Udaipur, India, with the theme Order from Chaos. This series of posts are what my TEDx presentation was based on. There are six parts that shall be published sequentially over the next few days. This is Part Five.
We all like order. We love order. Order means getting dinner on time, flights without delays, people not jumping the queue, police to keep criminals in check, doctors to give the right medicine, politicians to govern responsibly, teachers to teach well….the list is endless.
On the other hand, we all hate chaos. Chaos is messy. It is unpredictable. It cannot be controlled. It creates confusion.
And my belief is that rather than wanting Order from Chaos, it’s time we started wanting more chaos from this order.
I am not saying we address deficiencies in the system we have conceived. Rather I am saying that we ought to question our conception of what our educational system is.
In fact, by the early 20th century, people started looking at phenomena that could not be described by this classical, ordered view of a system. There were many phenomena, they argued, that did not fit into this classical notion of order – there was an element of probability that threatened the concept of order and predictability. For example, weather is impossible to predict in detail, but general patterns can be observed and predicted.
This started work on what is called Systems Theory which focused on “arrangement and relations between parts which connect them into a whole”. Some of the earliest work in this area was from people like Alexander Bogdanov, Ludwig von Bertalanffy, Norbert Weiner (who founded Cybernetics in 1948) and von Neumann. Cybernetics concentrated on the interface between man and electronics particularly for mechanisms of feedback, complexity, self-organization and adaptation.
The basic ideas around systems theory was that all around us we have systems or models that are complex. They are complex because they are made up of elements that have strong relationships with each other and with the environment in which the system exists.
What is interesting is that none of these elements completely describe the system they are part of and looking at their behavior may not provide us a deterministic way of predicting the behavior of the system as a whole.
For example, a gas particle is defined by its position and velocity. However the gas has properties like temperature and pressure. Not just that, under different environmental conditions, the gas may exhibit entirely different sets of properties i.e. new behavior may emerge.
Or look at the behavior of a flock of birds. You must have noticed how beautifully they fly in a self-organized formation even though there is no one bird that acts as the head. They follow some simple rules such as:
- Follow a flight path that is aligned to your closest neighbor
- Keep a safe distance from your neighbors
- Avoid hitting obstacles
All the birds in the flock follow these simple rules, but as you may have seen, their collective behavior is unpredictable and does not repeat itself.
Or the music produced by a jazz band, in which members agree to obey some general rules but are free to create their own variations, producing impossible to predict music. What’s more, members of the band may be influenced by their environment (e.g. changing audience preferences) and adapt their music in unpredictable ways.
One of the most amazing phenomena to be studied is Chaos. Small changes in initial conditions could lead to very large differences in outcomes. This was first found when Edward Lorenz studied weather patterns.
Since the elements of a system are networked, there is a huge value in deciphering patterns of behaviours in a network. For example, organizations are built hierarchically. But the way work gets done in the organization resembles a network.
Stakeholders are connected to each other in multiple ways spanning across traditional silos in an attempt to get the job done. We observe that information has many cores of distribution, not just one. We observe that an individual when replaced in an organization changes the network structure and consequently some of the efficiencies in the system, especially if she is a link between multiple sub-networks.
It has become apparent that closed-loop predictable systems are just one form of a system that exists in real life. All around us we have systems or models that are complex, open and distributed.
They are made up of networks of elements that have strong relationships with each other and with the environment in which the system exists. Like the educational system.
These systems exhibit very interesting behaviors. As the environment changes, these systems adapt. Small changes in initial conditions bring about large changes in outcomes. New behaviors emerge rather than being designed to occur. Not only that these systems do tend to self-organize and self-regulate.
Some of this work also inspired the thinking around the early web. Joseph Licklider wrote about man-computer symbiosis in 1960, extending from Norbert Weiner’s work on Cybernetics. Licklider wrote on the Computer as a communication device in 1968, where he saw the universal network as a network of people, connected to each other, and producing something that no one person in the network could ever hope to produce. Lick’s efforts led to the creation of the first Internet.
The rest is history. The ARPANET emerged in 1969. By 1990, Tim Berners-Lee had created the hypertext transfer protocol (http) which marked the birth of the web, and the internet started growing exponentially.
By 2005, Tim O’Reilly had marked another phase of the evolution of the Web and called it Web 2.0. While the earlier web was about connecting people to resources, this web was about people being able to create their own content, search it, share it and digitally collaborate. It was about harnessing collective intelligence ushered in by Amazon and its recommendation service. The last 5 years or so have seen tumultuous development on the Internet.
Neuroscientific advancements are also pointing to a different conception of the brain – not as an information processing unit, but as a system of massively parallel and networked connections. Symbolic language is being considered to be at a level higher that what occurs in the brain and that has important consequences for the way we treat the written or spoken word.
Analytics have changed in turn – there is a move from analyzing relationships to analyzing underlying patterns in a huge and growing, now digitally available, data, also called BIG data.
There is an even greater change that is looming on the horizon – that of the Semantic Web. Web 2.0 is collapsing under its own weight. The gigantic amount of information that is being created everyday is burying search. This is a really important development. So instead, we are moving towards Web 3.0 – the promise of a ubiquitous, semantic, location aware and contextual web – that Tim Berners-Lee originally envisaged.
But in our quest for ORDER, we have consciously excluded precisely this kind of emergent, self-organizing, chaotic, adaptive behavior.
And I believe we need to correct this. Once we shift that perspective, powerful alternatives emerge. And one of them could be to challenge scale with scale itself.
<< Part Four >> Part Six