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Lecture 1a: IntroductionComplexity
A complexity approach is multidisciplinary. The complexity approach can be applied to many
different fields. A complex system is a system that consists of many different parts/elements.
They interact massively. The remarkable thing about the interacting components is the
behaviour/phenomena that the system produces as a whole. This is surprising if you only study
the individual parts. The system is more than the individual components together, this
surprising behaviour is called emerging behaviour. This arises through self-organization.
In practise, we tend to focus on the elements of the system. In complex systems this approach
doesn’t work. There is a paradigm shift, a totally different way of dealing with things.
Linear systems vs. complex systems
o Components vs. system interactions Complex systems are sometimes
o Linear change vs. nonlinear change referred to as nonlinear
o One scale vs. multiple scales dynamical systems (behaviour
o Static vs. dynamic evolves over time.
o Single causality vs. multiple causality
o Reductionism vs. self-organization an emergence
Complicated vs. complex systems
We often treat complex systems as complicated systems. In both systems, there are components.
A complicated system can be understood if you understand the underlying components. In a
complex system, all the interactions of the components should be taken into account as well
when you want to understand the system. This is because everything is connected to everything.
If you know the components, you still don’t know much about the system. Complicated systems
work by reductionism while complex systems work by self-organization.
For example: The brain. We often see it as a complicated system; a computer. If you think about
the brain, it is actually a complex system. A problem (for example in the brain) is often seen as
the absence of one component of the complicated system. However, it seems that the average
brain doesn’t look the same as an individual brain. So there needs to be some sort of interaction
between processes in time. The interaction is often more important than what happens in a
specific point in time.
What did we learn from linear statistics so far?
Focus in traditional statistics
In the research field, we often start with categorizing groups and making general statements
(e.g. Women are more emotional than men). These statements are very broad and are about
groups of people. The statements are also almost always linear (e.g. the older one gets, the less
impulsive they will be).
In comparing groups, you compare mean values. If your comparison is significant, it is important
to see how much variance is explained. This is often very little. In individual cases, you cannot
make conclusions based on the averages.
Focus in complexity theory
We can focus on groups, but we usually start with analysing individuals. This is often measured
over time because people evolve. There isn’t only attention for linear relationships, but also for
nonlinear relationships. We don’t talk about the component, but the interaction of components.
Components are connected and dependent on each other. Also, we don’t talk about dependent
and independent variables, but about dynamical change processes.
Linear vs. nonlinear
, o Linear: A change in X is associated with a change in Y.
o Nonlinear: Far more common than linear
behaviour. A change in Y is not proportional to a
change in X.
Static vs. dynamic
Complex systems are dynamic. A data point represents a change process of an individual over
time. In complicated systems, a data point usually represents a measured value for an individual.
They are static.
Causality in complex systems
X-axis is the predictor for Y-axis in complicated systems. In complex systems, the outcome is
dependent on the data earlier in time. For example: Word vocabulary on time point t is
dependent of the word vocabulary on time point t-1. It isn’t random, but some kind of time
series.
Lecture 1: Diversities A
Why care about diversities?
Most evidence-based studies are based on population statistics, this means that they are
effective on average. So they aren’t effective for everyone. In encountering diversity we want to
know what works best for whom, with what condition, under what set of circumstances.
Complex science may help with that.
Diversity as error
The traditional view on diversity is that it is some kind of error. In the normal distribution, the
mean is seen as normal. Deviation of the mean then is some kind of error. So the mean is often
used to judge people. The idea is that the average represents most people, therefore something
designed for the average should work for the most people. But in reality this is not the case.
The average doesn’t resemble the most people. The average person often doesn’t exist. For
example: The average brain. An MRI-scan can measure brain activity. Researchers than can
calculate the average brain activation. This average doesn’t look like the individual brain
activation.
The fact that the average doesn’t resemble the most is due to the many dimensions that are
involved. For example: Symptoms of a disorder. There are many ways to diagnose a specific
disorder, patients do not show the same symptoms even though they have the same disorder.
When multiple dimensions are involved, nobody is likely to score average on all of those
dimensions.
Main message: The average person doesn’t exist!
Diversity as a rank (correlation)
The idea is that someone’s deviation from the average gives you an idea of how that person is. In
this kind of studies, correlations can be measured. For example: People who are taller, tend to be
heavier. Variables are correlated to each other. The problem is that this doesn’t always work, it
often doesn’t give information about individuals.
Main message: Group differences do not generalize to individual processes.
, Between subjects data may show completely different results from within subjects data.
Diversity in processes
When you study a process, you follow something over time complex systems.
There is also diversity in processes. Norms aren’t always what happens in real life. For example:
The norm is ‘sitting-crawling-walking’. But some children follow a different order. This order
doesn’t matter for the outcome, all the children start to walk at some point. In this case, the
average is seen as the norm.
Main message: There is many diversity in individual processes.
This is called equifinality. Multifinality is the same starting point, but a different outcome.
Lecture 2: Diversities B
Principles of individuality
1. Principle of jaggedness: A quality is jagged when it consists of multiple dimensions and
these dimensions are weakly correlated. When something is jagged, it is typical that
someone doesn’t score average on all of the dimensions. Another example is in factor
analysis. Inter individual patterns may differ from intra individual patterns. On a
questionnaire, the group outcome may by five factors while the outcome of an individual
may only be three or four factors.
o Sum score is the same, but the dimensions differ.
2. Principle of context: Individual
behaviour cannot be explained
apart from a particular situation. In
complex systems this is called
system-environment coupling. A
person/system cannot be not in an
environment, there is always a
context. Individuals are in
interaction with their contexts and
also shape their future contexts.
o Example Diversities in Youthcare: Mental disorders are for a large extent influenced by
culture (somatization differs among cultures) or … different mental disorders may exist in
different cultures (depression is not a word in African languages).
Small changes in context may have strong effects on individuals, this is nonlinearity in
cause-effect relations. In linear systems, small influences have small effects. In complex
systems small influences can have a huge effect while large changes have no effect. This
is disproportional.
3. Principle of pathways: For any outcome, there are many equally valid ways to reach is.
Your optimal pathway depends on your own individuality (= equifinality). Multifinality
means that the starting point is the same for everyone, but the outcome differs
individually.
Multifinality
There is a difference between skill acquisition (e.g. learning to walk) and more fluid
psychological constructs (e.g. mood swings). Skill acquisition is part of equifinality.
Psychological processes keep changing and there is different time scales (slow time scale =
temperament; fast time scale = mood). So there are multiple processes on different time scales.
Pathways in complex systems are often unpredictable. This is because of sensitive dependence
to initial conditions. It is the butterfly effect. This means that a very small influence can have a
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