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11 The Self-Organizing Society
• The most important issue is whether a system is stable or unstable. In case of
stability, variations in individual behavior do not make a significant difference, i.e.
we don’t need to know what the individuals do. In contrast, in case of instability the
system is often not predictable or controllable due to amplification and cascading
effects.
5
• In complex socio-economic systems, surprises will sooner or later happen. Therefore, our economy and society should be organized in a way that can flexibly
respond to disruptions. Socio-economic systems should be able to resist shocks
and recover from them quickly and well. This is best ensured by a modular,
“resilient” system design.
6
• In complex dynamical systems, which vary a lot, are hard to predict and cannot
be optimized in real-time, distributed control can outperform top-down control
attempts by flexibly adapting to local conditions and needs.
• While distributed control may be emulated by centralized control, a centralized approach might fail to identify the variables that matter.
7 Depending on
the problem, centralized control is also considerably more expensive, and it may
be less efficient and effective.
8
• Filtering out information that matters is a great challenge. Explanatory models
that are combined with little, but suitable kinds of data are best to inform decisionmakers. Such models also indicate what kind of data is needed.
9
• Diversity and complexity are not our problem. They come along with innovation, socio-economic differentiation and cultural evolution. However, we have to
learn how to use diversity and complexity to our advantage. This requires us to
understand the hidden forces behind socio-economic change as well as to use
self-organization and digital assistants to support the coordination of actors with
5 In case of cascade effects, a local problem will cause other problems before the system recovers
from the initial disruption. Those problems trigger further ones, etc. Even hundreds of policemen
could not avoid phantom traffic jams from happening, and in the past even large numbers of security forces have often failed to prevent crowd disasters (they have sometimes even triggered or
deteriorated them while trying to avoid them), see Helbing and Mukerji [2].
6 Helbing et al. [3].
7 Due to the data deluge, the existing amounts of data increasingly exceed the processing capacities,
which creates a “flashlight effect”: while we might look at anything, we need to decide what data
to look at, and other data will be ignored. As a consequence, we often overlook things that matter.
While the world was busy fighting terrorism in the aftermath of September 11, it did not see the
financial crisis coming. While it was focused on this, it did not see the Arab Spring coming. The
crisis in Ukraine came also as a surprise, and the response to Ebola came half a year late. Of course,
the possibility or likelihood of all these events was reflected by some existing data, but we failed to
pay attention to them.
8 The classical telematics solutions based on a control center approach haven’t improved traffic
much. Today’s solutions to improve traffic flows are mainly based on distributed control approaches:
self-driving cars, intervehicle communication, car-to-infrastructure communication etc.
9 This approach corresponds exactly how Big Data are used at the elementary particle accelerator
CERN; 99.9% of measured data are sorted out immediately. One only keeps data that are required
to answer a certain question, e.g. to validate or falsify implications of a certain theory.
11 The Self-Organizing Society
• The most important issue is whether a system is stable or unstable. In case of
stability, variations in individual behavior do not make a significant difference, i.e.
we don’t need to know what the individuals do. In contrast, in case of instability the
system is often not predictable or controllable due to amplification and cascading
effects.
5
• In complex socio-economic systems, surprises will sooner or later happen. Therefore, our economy and society should be organized in a way that can flexibly
respond to disruptions. Socio-economic systems should be able to resist shocks
and recover from them quickly and well. This is best ensured by a modular,
“resilient” system design.
6
• In complex dynamical systems, which vary a lot, are hard to predict and cannot
be optimized in real-time, distributed control can outperform top-down control
attempts by flexibly adapting to local conditions and needs.
• While distributed control may be emulated by centralized control, a centralized approach might fail to identify the variables that matter.
7 Depending on
the problem, centralized control is also considerably more expensive, and it may
be less efficient and effective.
8
• Filtering out information that matters is a great challenge. Explanatory models
that are combined with little, but suitable kinds of data are best to inform decisionmakers. Such models also indicate what kind of data is needed.
9
• Diversity and complexity are not our problem. They come along with innovation, socio-economic differentiation and cultural evolution. However, we have to
learn how to use diversity and complexity to our advantage. This requires us to
understand the hidden forces behind socio-economic change as well as to use
self-organization and digital assistants to support the coordination of actors with
5 In case of cascade effects, a local problem will cause other problems before the system recovers
from the initial disruption. Those problems trigger further ones, etc. Even hundreds of policemen
could not avoid phantom traffic jams from happening, and in the past even large numbers of security forces have often failed to prevent crowd disasters (they have sometimes even triggered or
deteriorated them while trying to avoid them), see Helbing and Mukerji [2].
6 Helbing et al. [3].
7 Due to the data deluge, the existing amounts of data increasingly exceed the processing capacities,
which creates a “flashlight effect”: while we might look at anything, we need to decide what data
to look at, and other data will be ignored. As a consequence, we often overlook things that matter.
While the world was busy fighting terrorism in the aftermath of September 11, it did not see the
financial crisis coming. While it was focused on this, it did not see the Arab Spring coming. The
crisis in Ukraine came also as a surprise, and the response to Ebola came half a year late. Of course,
the possibility or likelihood of all these events was reflected by some existing data, but we failed to
pay attention to them.
8 The classical telematics solutions based on a control center approach haven’t improved traffic
much. Today’s solutions to improve traffic flows are mainly based on distributed control approaches:
self-driving cars, intervehicle communication, car-to-infrastructure communication etc.
9 This approach corresponds exactly how Big Data are used at the elementary particle accelerator
CERN; 99.9% of measured data are sorted out immediately. One only keeps data that are required
to answer a certain question, e.g. to validate or falsify implications of a certain theory.