Skip to 0 minutes and 10 secondsComplex systems involve interacting elements, individuals also responding to the environment without full central control. In this lecture, we briefly discuss some of their common characteristics. First, complex systems show sudden transitions, tipping points. After a long period of apparent stability, all of a sudden, the system can become very unstable and even break down. Behaviour of a complex system is non-linear. Chaotic behaviour is a limit to that. It is a deterministic process but totally unpredictable. And that is because of the importance of the tiny, unobservable changes in initial conditions. Chaotic systems are very sensible to changes in where they start from, in their initial conditions. That introduces another term, path dependency.
Skip to 1 minute and 10 secondsWhere the system is is very much dependent on where it started, on its history, on the past that it has gone through, path dependency. That means that history matters. And this will be elaborated in the lecture on history later in this course.
Skip to 1 minute and 29 secondsLet's take the example of society, of nonlinearities in society. The most dramatic example is, of course, revolutions. Revolutions are really turning points where a stable system, or a relatively stable system, all of a sudden becomes violent. Another example is the financial crisis. The crisis, the recent crisis of 2007, 2008, is a clear case in point. After a long period of increasing stability, there was a sudden tipping point in August, 2007 and September, 2008. The long period of stability before that was called the Great Moderation. It was a very stable period in which volatility came down. Inflation came down. And everything seemed to be very stable. And then we had a sudden turning point.
Skip to 2 minutes and 22 secondsThis crisis, the crisis of 2007, 2008, will also be discussed in greater detail as a case study in the last week of this course. The second example of common elements has to do with the distribution of characteristics in a complex system. We are used, in life, to the normal distribution, the so-called bell curve, the Gauss distribution. The Gauss distribution is named after the famous mathematician, Gauss. But the interesting thing is that the Gauss distribution, the normal distribution, does not often apply to the distribution of characteristics in a complex system. We will elaborate this in one of the lectures all network theory.
Skip to 3 minutes and 10 secondsFinal characteristic that's often found in complex system is evolutionarily dynamics. Evolutionary dynamics. Networks, complex systems, evolve over time. They adapt to changing circumstances by eliminating those that have characteristics that make them less fit for the changed circumstances.
Skip to 3 minutes and 32 secondsEvolution is a process without central direction. It's a process of unconscious trial and error at the level of the system as a whole. Evolution can be described as variation, selection and elimination, application or scaling of what works well, and that in continuous repetition. Evolutionary dynamics will be developed further in this course. In this lecture, we have discussed sudden transitions and tipping points, non-normality of distributions, and evolutionary dynamics as characteristics of complex systems.
Common characteristics of complex systems 2
This lecture continues to highlight characteristics complex systems have in common. Specifically, we will introduce the notions: sudden transitions / tipping points, non-linearity, chaos, path dependence, statistical distributions and evolutionary dynamics.
© University of Groningen