Enhance team performance by consistent individual behavior

A key principle of bioteams is team member self-management. Nature's teams achieve this through a surprisingly small number of simple rules which operate at the individual member level and result in sophisticated team behaviour. For example, complex 'bird flocking' behaviour can be simulated on a computer using just three rules. I propose that human bioteams can be effectively self-managed in a sophisticated way by adopting a small set of "model behaviours" at the individual team member level.


Bioteams are self-managing

Nature's teams don't issue orders they communicate information and expect the receiving members to take the appropriate action. For example, in an ant colony the Queen's job is to reproduce - not to try to control what the other ants do. This is just as well as some colonies can have up to a 300 million members!

Clearly the Queen can't have any real idea of what each of them are doing at any given time. Instead of issuing orders, nature's teams function by providing timely information to the team members and then expecting them take the appropriate action.

This information is not provided by the 'leader' but by the other 'members'

For example, it is not the Queen Bee in a beehive who finds the good nectar source and dances the "waggle dance" to show the others where it is. Neither is it the Ant Queen who spots the ant from the rival colony out on a scouting mission and recruits the nearest available ants into an instant SWAT team to eliminate it.

So natures teams are the very opposite of traditional organisational teams which embody 'command and control'.

But 'self-management' is a scary concept...

For a traditional team leader a self-managed team sounds like a recipe for anarchy and chaos. It may work for ants and bees and dolphins and even army teams operating behind enemy lines but there is a real fear "it wont work in my team in my organisation for this particular project".

It would be helpful if there were some guidelines (and limits) that we could use to focus individual team member behaviour in a self-managed environment - a set of model behaviours?

Bird Flocking Behaviour

Craig Reynolds [1] a computer graphics researcher asked himself the same question about bird flocking behaviour. He studied how bird flocks fly in formation to see whether there were simple rules, which could be simulated in computer software.

As a group activity flocking looks extremely complex - what are the underlying member behaviours which produce this complex activity?

They must be relatively simple otherwise the individual birds (with their 'bird brains') would not be able to follow them to the necessary consistency whilst in the process of high speed flight manoeuvres in close formation.


Reynolds came up with a virtual programmable bird called a "birdoid" which quickly and appropriately got shortened to "boid". You pronounce 'boid' the way a New Yorker would say 'bird'!

He found that computerised boids could be made to fly successfully in formation in 3-dimensional space if they were programmed with just 3 simple behavioural rules:

  1. Separation: steer to avoid crowding other local flock-mates

  2. Alignment: steer towards the average heading of the local flock-mates

  3. Cohesion: steer to move toward the average position of local flock-mates

Similar research has also been developed extensively by organisations like the Santa Fe Institute in New Mexico who specialise in simulating massively parallel environments (micro worlds) to model behaviour of ants, turtles, termites, traffic jams and even city sprawls [2].

From Boids to Biods!

This research led me to ask the key question for bioteams - are there a small set of simple generic behaviour types, which members of a bioteam can exhibit?

These behaviours, though simple in themselves, if consistently applied would produce effective self-management and allow complex team behaviour without a command and control system.

Resonating with Reynolds 'Boid' I would also suggest the new term 'Biod' as an abbreviation of "bioteam memberoid"

7 Bioteam Model Behaviors

In the table below I propose 7 'model behaviours' for bioteam members. They can be remembered via the mnemonic O-R-G-A-N-I-C.


I propose that if bioteam members start to follow these in a serious and co-ordinated way they will lay the foundations for effective team self-management

O-R-G-A-N-I-C Behaviors

These 7 behaviors are designed to ensure team members are '3-dimensional' in their operation just like natures teams:

  1. Member-Member Behavior
  2. Member-External Behavior (i.e. Customers, Partners and Competitors)
  3. Member-Colony Behavior (i.e. Host Organisations and Teams)

An important trait of nature's teams is that random interactions if done systematically are vital for the health of the team. For example, Ant colonies constantly rotate their foraging directions to ensure they do not overgraze and miss opportunistic food sources.

This "systematic randomness" principle can be embodied in a bioteam by the "Investigate" behavior which encourages all team members to constantly look out for and report anything 'interesting'.

Nurturing Organic Behaviors

Bioteam members should be encouraged to develop all of these behaviours - it is not an "a la carte" list from which you just select the couple that you like.

However the model behaviours are only a starter-set - for them to be useful they need to be tailored to suit the team and its individuals.

The behaviours need to be carefully nurtured if they are to become authentic and personal rather than done by 'rote'.


Based on research into bird flocking and massively parallel environments I have suggested a way that human bioteams can begin to become self-managed if their members cultivate just seven 'model behaviours'.

If team members consistently and seriously develop these behaviours then the virtually networked team will be able to produce complex responses and outcomes without the penalty and problems of centralised command and control.


1. Reynolds, C., 1987 "Flocks, Herds and Schools - a distributed behaviour model", Computer Graphics, pp. 25-34

2. Resnick, M., 1997. Turtles, Termites and Traffic Jams - Explorations in Massively Parallel Microworlds, MIT Press, pp. 49-68

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