7.1 Self-Organization “like Magic”
133
units, the neurons. On average, each neuron is connected to about a thousand others,
and the resulting network exhibits properties that cannot be understood by looking
at a single neuron in isolation. In concert, this neural network doesn’t only control
our motion, but it also supports our decisions, and the mysterious phenomenon of
consciousness. And yet, even though our brain is so astounding, it consumes only as
much energy as a 100 Watts light bulb! This shows how efficient self-organization
can be.
However, self-organization does not mean that the outcomes of the system would
necessarily be desirable. Traffic jams, crowd disasters, or financial crises are good
examples for this, and that’s why self-organization may need some “assistance”.
However, by modifying the interactions (for instance, by introducing suitable feedbacks), one can let different outcomes emerge. The disciplines needed to find the right
kinds of interactions in order to obtain particular structures, properties, or functions
are called “complexity science” and “mechanism design”.
“Assisted self-organization” slightly modifies the interactions between system
components, but only where necessary. It uses the hidden forces acting within
complex dynamical systems rather than opposing them. This is done in a similar
way as engineers have learned to use the forces of nature. We might also compare
this with Asian martial arts, where one tries to take advantage of the forces created
by the opponent.
In fact, the aim of assisted self-organization is to intervene locally, as little as
possible, and gently, in order to use the system’s capacity for self-organization
to efficiently reach the desired state. This connects assisted self-organization with
the approach of distributed control, which is quite different from the Big Nudging
approach discussed before.
1 Distributed control is a way in which one can achieve a
certain desirable mode of behavior by temporarily influencing interactions of specific
system components locally, rather than trying to impose a certain global behavior on
all components at once. Typically, distributed control works by helping the system
components to adapt when they show signs of deviating too much from their normal
or desired state. In order for this adaptation to be successful, the feedback mechanism
must be carefully chosen. Then, a favorable kind of self-organization can be reached
in the system.
In fact, the behavior that emerges in a self-organizing complex system isn’t just
random, nor is it totally unpredictable. Such systems tend to be drawn towards particular stable states, called “attractors”. Each attractor represents a particular type of
collective behavior. For example, Fig. 7.1 shows six typical traffic states, each of
which may be considered to be an attractor. In many cases, including freeway traffic,
we can understand and predict these attractors by using computer models to simulate
the interactions between the system components (here, the cars). If the system is
slightly disrupted, it will usually return to the same attractor state. This is an interesting and important feature. To some extent, this makes the complex dynamical
1 The diversity of approaches and the plurality of goal functions is an essential aspect for complex
economies and societies to thrive. This and the conscious decision what to engage in and with whom
distinguishes assisted self-organization from the top-down nudging approach discussed before.
133
units, the neurons. On average, each neuron is connected to about a thousand others,
and the resulting network exhibits properties that cannot be understood by looking
at a single neuron in isolation. In concert, this neural network doesn’t only control
our motion, but it also supports our decisions, and the mysterious phenomenon of
consciousness. And yet, even though our brain is so astounding, it consumes only as
much energy as a 100 Watts light bulb! This shows how efficient self-organization
can be.
However, self-organization does not mean that the outcomes of the system would
necessarily be desirable. Traffic jams, crowd disasters, or financial crises are good
examples for this, and that’s why self-organization may need some “assistance”.
However, by modifying the interactions (for instance, by introducing suitable feedbacks), one can let different outcomes emerge. The disciplines needed to find the right
kinds of interactions in order to obtain particular structures, properties, or functions
are called “complexity science” and “mechanism design”.
“Assisted self-organization” slightly modifies the interactions between system
components, but only where necessary. It uses the hidden forces acting within
complex dynamical systems rather than opposing them. This is done in a similar
way as engineers have learned to use the forces of nature. We might also compare
this with Asian martial arts, where one tries to take advantage of the forces created
by the opponent.
In fact, the aim of assisted self-organization is to intervene locally, as little as
possible, and gently, in order to use the system’s capacity for self-organization
to efficiently reach the desired state. This connects assisted self-organization with
the approach of distributed control, which is quite different from the Big Nudging
approach discussed before.
1 Distributed control is a way in which one can achieve a
certain desirable mode of behavior by temporarily influencing interactions of specific
system components locally, rather than trying to impose a certain global behavior on
all components at once. Typically, distributed control works by helping the system
components to adapt when they show signs of deviating too much from their normal
or desired state. In order for this adaptation to be successful, the feedback mechanism
must be carefully chosen. Then, a favorable kind of self-organization can be reached
in the system.
In fact, the behavior that emerges in a self-organizing complex system isn’t just
random, nor is it totally unpredictable. Such systems tend to be drawn towards particular stable states, called “attractors”. Each attractor represents a particular type of
collective behavior. For example, Fig. 7.1 shows six typical traffic states, each of
which may be considered to be an attractor. In many cases, including freeway traffic,
we can understand and predict these attractors by using computer models to simulate
the interactions between the system components (here, the cars). If the system is
slightly disrupted, it will usually return to the same attractor state. This is an interesting and important feature. To some extent, this makes the complex dynamical
1 The diversity of approaches and the plurality of goal functions is an essential aspect for complex
economies and societies to thrive. This and the conscious decision what to engage in and with whom
distinguishes assisted self-organization from the top-down nudging approach discussed before.