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What is Emergent Behaviour?
Economies - beehives - financial markets - animal markings -
team building - consciousness - locust swarms - mass hysteria - geese
flocking - road networks and traffic jams - bacterial infection - town planning
- evolution - the Web ... these are all examples of emergent phenomena
where a collection of individuals interact without central control to produce
results which are not explicitly "programmed".
Qualities of Emergent Behaviour
What can emergent systems do that other systems can’t?
- They are robust and resilient. There is no single-point of failure, so if
a single unit fails, becomes lost or is stolen, the system still works.
- They are well-suited to the messy real world. Human-engineered systems may
be “optimal” but often require a lot of effort to design and are fragile
in the face of changing conditions. Importantly, they don’t need to have
complete knowledge/understanding to achieve a goal (e.g. social systems in
warehousing).
- They find a reasonable solution quickly and then optimise. In the real
world, time matters - decisions need to be taken while they are still
relevant. Traditional computer algorithms tend to not produce a useful result
until they are complete (which may be too late, e.g. if you're trying to avoid
an oncoming obstacle) .
How it works
The individuals interact with each other directly or indirectly (via their
environment). Interacting via an effect on, and response to, their common
environment is called stigmergy. For example, termites work together to
build termite mounds without any "queen" to co-ordinate activity and without any
pre-existing plan of what to build. They change the environment and the changed
environment modifies their behaviour. For example, to build a single termite
mound in an environment consisting of randomly-scattered wood chips, a group of
termites each has only to follow one simple rule :
Whilst wandering randomly
If you find a chip
then pick it up
unless you're already carrying a chip
in which case drop it
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To begin with, several small mounds will start to emerge, but then the
largest mound will grow at the expense of the smaller ones until there is only
the larger one left. This is because termites are more likely to find the large
mound than the small ones. You can gain an intuitive understanding of this by
downloading the StarLogo application by Mitchel
Resnick of MIT.
Interestingly, through emergent behaviour "selfish genes" can cause
apparently social behaviour By forming into schools (using simple emergent
“flocking” rules), animals like fish and zebras reduce their individual
chances of predation.
Applications
Which problems of today can emergent systems solve?
- Robotic systems capable of operating in the real world, e.g. planetary
exploration, demining, domestic. Robots can
share their information.
- There are a host of military applications, for example the work done by
DARPA on groups of
small (<5cm) distributed robots. MAVs
(Micro Autonomous Flying Robots).
- Toys - a technology
platform for social games?
- Financial systems, from the stock market to local and global economies,
can be modelled using a "SimCity"-style simulation of thousands or millions of
agents all following simple rules (e.g. "if my stock tanks then sell").
Likewise traffic flow can be modelled with agents following simple rules such
as "if the car in front gets too close then brake).
Who's working on this?
Much of the work is being done in the USA, especially at Santa Fe.
Work in the UK includes:
Bibliography
General References
Relevant Books
- Chaos, James Gleick
- The
Pattern On the Stone, Daniel Hillis
- Emergence, Steven Johnson
- Swarm Intelligence, Eric Bonabeau, Marco Dorigo, Guy Theraulaz
at Santa Fe Institute
- Great Mambo Chicken and the Transhuman
Condition, Ed Regis
- Ashley Book of
Knots, contains diagrams and descriptions of 3854 things that can
be done with rope and string, virtually all of which involve some version of
over and under.
- Engines of Creation, K Eric
Drexler, the quintessential nanotech promoter.
- Analog VLSI and Neural Systems, Carver Mead
- Self-Organizing Maps, Kohonen
- Brainmakers, David H
Freedman
- Pulsed Neural Networks,
edited by Wolfgang Maass and Christopher
Bishop
- A Fire upon the Deep, a novel by Vernor Vinge, an interesting
insight into how distributed individuals might think.
© 2003 Pilgrim Beart