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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