Self-organization in Communicating Groups:Collective Intelligence
This is a very common situation in any kind of social interaction: individuals typically
come to the table with different backgrounds, habits, ideas, cultures, perspectives and even
languages. To be able to communicate at all, they should first agree about a common set of
terms and what those terms mean. This is the emergence of linguistic conventions. Then they
should agree about basic assumptions, such as what the situation is, what can be done about it,
and what should be done about it. Finally, they will need to agree about who will do what when.
If successful, this sequence of agreements will lead to a coordinated form of action, where the
different members of the group contribute in an efficient way to a collective solution of
whatever their problem was. This phenomenon, where a group of initially independent agents
develop a collective approach to the tackling of some shared problem that is more powerful than
the approach any of them might have developed individually, may be called collective
intelligence.
The emergence of collective intelligence is intrinsically a process of self-organization.
If the process were directed by a single individual (say, the group leader), who imposes a
consensus view on the others, then that perspective would not be more powerful than the
perspective of the leading individual. In other words, the collective would not be in any way
more intelligent than its leader. Self-organization happens in a distributed or decentralized
manner: the different members of the group all contribute to the emerging organization, and no
one is in control. This makes the process complex and intrinsically unpredictable, as tiny
differences in the initial state (such as who speaks first, or which word is initially used to
designate a particular item) may lead to very different outcomes. That is why such a process of
group discussion and emergent interaction patterns needs to be understood with the conceptual
tools of complexity science
Complex Systems
Classical science, as exemplified by Newtonian mechanics, is essentially reductionist: it reduces
all complex phenomena to their simplest components, and then tries to describe these
components in a complete, objective and deterministic manner . The philosophy of
complexity is that this is in general impossible: complex systems, such as organisms, societies,
languages, or the Internet, have properties—emergent properties—that cannot be reduced to the
mere properties of their parts. Moreover, the behavior of these systems has aspects that are
intrinsically unpredictable and uncontrollable, and that cannot be described in any complete
manner. Finally, Newtonian mechanics assumes that all changes are reversible, and therefore
that there is no fundamental difference between the past and the future. Complex systems, on
the other hand, are characterized by an irreversible evolution, by an “arrow of time” that points
unambiguously from the past to the future, and that allows no turning back.
The complex systems approach has done away with the old philosophy of dualism, which sees the world as made out of two distinct substances: matter, as described by the natural sciences, and mind, as described by the social sciences and humanities.
In the systems approach, matter and mind are merely two different aspects of the same basic
phenomenon of organization, with matter representing the simple, static, passive, causally
determined aspects, and mind the more complex, dynamic, active, goal-directed aspects. As
systems evolve, starting from elementary particles via atoms, molecules and organisms to
brains, societies, languages and cultures, they become more complex and adaptive, and
therefore more “mind-like” and less “matter-like”. However, that does not mean that mind
should be understood merely as a complex arrangement of pieces of matter: the material
components themselves can already be conceptualized as having rudimentary “mind-like”
qualities, such as sensitivity, intention, and action. For example, a molecule
may sense the presence of another molecule and act upon that molecule via electromagnetic
interaction between the charged atoms in the molecule. Its implicit “goal”.
Self-organization
The concept of self-organization is becoming increasingly popular in various branches of
science and technology. Although there is no generally accepted definition , a self-organizing system may be characterized by global, coordinated activity arising spontaneously from local interactions between the system's components or “agents”.
This activity is distributed over all components, without a central controller supervising or
directing the behavior. For example, in a school of fish each individual fish bases its behavior
on its perception of the position and speed of its immediate neighbors, rather than on the behavior
of a “central fish” or that of the whole school. Self-organization establishes a relation
between the behavior of the individual components and the structure and functionality of the
system as a whole: simple interactions at the local level give rise to complex patterns at the
global level. This phenomenon is called emergence.
A similar principle, “order through fluctuations”, was formulated a couple of years later
by the Nobel-prize winning chemist Prigogine , who applied self-organization
to explain the “dissipative structures” that appear in thermodynamic systems far
from equilibrium. In the same period, the physicist founded the domain of
synergetics, a mathematical approach towards understanding the spontaneous cooperation that
emerges in systems with many components, as exemplified by lasers and phase transitions.
Another early application of self-organizing mechanisms were neural networks: computer
simulations of how the neurons in the brain perform complex tasks (such as learning,
classification, and pattern recognition) in a very robust manner without centralized control.
Self-organization as a problem of coordination
Organization can be defined as structure with function: the components (agents) of the system
are arranged in an orderly way (structure) so as to achieve a certain goal (function). This is the
meaning used in sociology and management: a typical organization, such as a company or
government institute, consists of individuals who are arranged according to specified lines of
communication and control. This structure is intended to facilitate the work of the organization
towards its goals, such as providing a product or service. When we reflect a little more deeply,
though, the notion of structure tell us very little about how this arrangement is supposed to
contribute to the achievement of a function. Why cannot the same goal be reached by an
anarchic group of autonomous individuals each contributing his or her best effort?
The relation between structure and function becomes clearer when we introduce the
notion of coordination: what counts is not so much how individual agents are arranged (e.g. in some kind of hierarchy or network), but how their actions work together in a harmonic way towards their collective goals. At the very least, these actions should not hinder, obstruct, or oppose each other. This is what I have called the avoidance of friction
At best, they will smoothly complement each other, the one continuing the task where the other one stopped, or the one adding the necessary ingredient that the other one lacked. As such they can solve problems together that they cannot solve individually. This bonus added by collaboration may be called synergy . Coordination can then be defined as: the structuring of actions in time
and (social) space so as to minimize friction and maximize synergy between these actions.
Coordination can be subdivided in four elementary processes or mechanisms:
alignment, division of labor, workflow, and aggregation,
come to the table with different backgrounds, habits, ideas, cultures, perspectives and even
languages. To be able to communicate at all, they should first agree about a common set of
terms and what those terms mean. This is the emergence of linguistic conventions. Then they
should agree about basic assumptions, such as what the situation is, what can be done about it,
and what should be done about it. Finally, they will need to agree about who will do what when.
If successful, this sequence of agreements will lead to a coordinated form of action, where the
different members of the group contribute in an efficient way to a collective solution of
whatever their problem was. This phenomenon, where a group of initially independent agents
develop a collective approach to the tackling of some shared problem that is more powerful than
the approach any of them might have developed individually, may be called collective
intelligence.
The emergence of collective intelligence is intrinsically a process of self-organization.
If the process were directed by a single individual (say, the group leader), who imposes a
consensus view on the others, then that perspective would not be more powerful than the
perspective of the leading individual. In other words, the collective would not be in any way
more intelligent than its leader. Self-organization happens in a distributed or decentralized
manner: the different members of the group all contribute to the emerging organization, and no
one is in control. This makes the process complex and intrinsically unpredictable, as tiny
differences in the initial state (such as who speaks first, or which word is initially used to
designate a particular item) may lead to very different outcomes. That is why such a process of
group discussion and emergent interaction patterns needs to be understood with the conceptual
tools of complexity science
Complex Systems
Classical science, as exemplified by Newtonian mechanics, is essentially reductionist: it reduces
all complex phenomena to their simplest components, and then tries to describe these
components in a complete, objective and deterministic manner . The philosophy of
complexity is that this is in general impossible: complex systems, such as organisms, societies,
languages, or the Internet, have properties—emergent properties—that cannot be reduced to the
mere properties of their parts. Moreover, the behavior of these systems has aspects that are
intrinsically unpredictable and uncontrollable, and that cannot be described in any complete
manner. Finally, Newtonian mechanics assumes that all changes are reversible, and therefore
that there is no fundamental difference between the past and the future. Complex systems, on
the other hand, are characterized by an irreversible evolution, by an “arrow of time” that points
unambiguously from the past to the future, and that allows no turning back.
The complex systems approach has done away with the old philosophy of dualism, which sees the world as made out of two distinct substances: matter, as described by the natural sciences, and mind, as described by the social sciences and humanities.
In the systems approach, matter and mind are merely two different aspects of the same basic
phenomenon of organization, with matter representing the simple, static, passive, causally
determined aspects, and mind the more complex, dynamic, active, goal-directed aspects. As
systems evolve, starting from elementary particles via atoms, molecules and organisms to
brains, societies, languages and cultures, they become more complex and adaptive, and
therefore more “mind-like” and less “matter-like”. However, that does not mean that mind
should be understood merely as a complex arrangement of pieces of matter: the material
components themselves can already be conceptualized as having rudimentary “mind-like”
qualities, such as sensitivity, intention, and action. For example, a molecule
may sense the presence of another molecule and act upon that molecule via electromagnetic
interaction between the charged atoms in the molecule. Its implicit “goal”.
Self-organization
The concept of self-organization is becoming increasingly popular in various branches of
science and technology. Although there is no generally accepted definition , a self-organizing system may be characterized by global, coordinated activity arising spontaneously from local interactions between the system's components or “agents”.
This activity is distributed over all components, without a central controller supervising or
directing the behavior. For example, in a school of fish each individual fish bases its behavior
on its perception of the position and speed of its immediate neighbors, rather than on the behavior
of a “central fish” or that of the whole school. Self-organization establishes a relation
between the behavior of the individual components and the structure and functionality of the
system as a whole: simple interactions at the local level give rise to complex patterns at the
global level. This phenomenon is called emergence.
A similar principle, “order through fluctuations”, was formulated a couple of years later
by the Nobel-prize winning chemist Prigogine , who applied self-organization
to explain the “dissipative structures” that appear in thermodynamic systems far
from equilibrium. In the same period, the physicist founded the domain of
synergetics, a mathematical approach towards understanding the spontaneous cooperation that
emerges in systems with many components, as exemplified by lasers and phase transitions.
Another early application of self-organizing mechanisms were neural networks: computer
simulations of how the neurons in the brain perform complex tasks (such as learning,
classification, and pattern recognition) in a very robust manner without centralized control.
Self-organization as a problem of coordination
Organization can be defined as structure with function: the components (agents) of the system
are arranged in an orderly way (structure) so as to achieve a certain goal (function). This is the
meaning used in sociology and management: a typical organization, such as a company or
government institute, consists of individuals who are arranged according to specified lines of
communication and control. This structure is intended to facilitate the work of the organization
towards its goals, such as providing a product or service. When we reflect a little more deeply,
though, the notion of structure tell us very little about how this arrangement is supposed to
contribute to the achievement of a function. Why cannot the same goal be reached by an
anarchic group of autonomous individuals each contributing his or her best effort?
The relation between structure and function becomes clearer when we introduce the
notion of coordination: what counts is not so much how individual agents are arranged (e.g. in some kind of hierarchy or network), but how their actions work together in a harmonic way towards their collective goals. At the very least, these actions should not hinder, obstruct, or oppose each other. This is what I have called the avoidance of friction
At best, they will smoothly complement each other, the one continuing the task where the other one stopped, or the one adding the necessary ingredient that the other one lacked. As such they can solve problems together that they cannot solve individually. This bonus added by collaboration may be called synergy . Coordination can then be defined as: the structuring of actions in time
and (social) space so as to minimize friction and maximize synergy between these actions.
Coordination can be subdivided in four elementary processes or mechanisms:
alignment, division of labor, workflow, and aggregation,
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