Chaos
Not disorder, but sensitivity. A deterministic system that becomes unpredictable through its sensitivity to initial conditions.
In everyday speech, chaos usually stands for disorder. In mathematics and physics it means something quite different, and it is well to make the distinction — otherwise one ends up with a term that fits everywhere and nowhere.
A chaotic system in the technical sense is usually described as a system that is deterministic (the laws are fixed) and at the same time in practice unpredictable. Not because we do not know the laws, but because the system is so sensitive to its initial conditions that a minute deviation at the beginning leads to enormously different outcomes further on in time.
The meteorologist Edward Lorenz discovered this by accident in 1961. He was running a weather simulation on his computer and wanted to repeat a portion of it. To save time he entered his initial values to three decimals instead of the original six. He expected a comparable result. What he got was a weather picture that after a few days was entirely different. The small rounding had multiplied itself into an unrecognisable other weather. Lorenz later called this the butterfly effect: a butterfly that flutters its wings in Brazil may in principle cause a tornado in Texas.
What chaos is not
What is meant here is, in our view, not disorder. A chaotic system has structure. It runs along an attractor — a kind of trajectory in the dynamics on which the system moves. The Lorenz attractor has the shape of two wings joined together (hence its name). The system wanders on that attractor, and where it will go in the short term cannot be exactly predicted, but the large form — the attractor itself — is predictable.
Chaos is therefore a kind of structured unpredictability. We know roughly in which domains the system moves, but not exactly when it is where.
And in the psyche?
Whether human functioning is truly chaotic in this technical sense remains, for now, an open question. There are studies that find indications in EEG data, in heart rate, in mood time series. But it is hard to distinguish strict chaos from ordinary noise with much noise. One who speaks too enthusiastically of "chaos" sometimes actually says: "we do not yet understand it".
What does seem useful to us is the realisation that even simple psychological systems may behave in such a way that exact prediction is not properly possible — not merely from lack of knowledge, but possibly from the nature of the system itself. This might have several practical consequences.
Three practical consequences
First: if a patient's system has chaos-like properties, "what will happen tomorrow?" is a question to which we cannot, in honesty, give an exact answer. We can indicate within which domains it moves.
Second: small interventions can in the longer term have unexpectedly large outcomes. Or none at all. This makes timing more important than dosing.
Third: a messy time series is not necessarily a messy patient. Sometimes it is the nature of the system. To this we must adapt our measurements — not by removing more noise, but by looking at the patterns differently.