Emergent Behaviour

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?

  1. 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.
  2. 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 or understanding to achieve a goal (e.g. social systems in warehousing).
  3. 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

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

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