Bioteams Part 3: The Team Execution Zone

This article is the third in a four-part series which describes each of the 4 Bioteams Zones, continuing with the Execution Zone.

In a previous article I introduced the second principle of Bioteams – “Bioteams are highly connected virtual networks”. In this article I will introduce a third overarching principle of Bioteams: Bioteams are exceptionally good at taking action, co-operating and learning. I will then introduce the third triad of supporting action rules (rules 7, 8 and 9).

This article was originally published in 2005 by Ken Thompson and Robin Good.


In an earlier article,  I argued that traditional teams have key weaknesses and limitations and are now being replaced in organizations by Virtually Networked Teams.
I described the problems these teams face and pointed to critical issues that can make technology both part of the solution and part of the problem.
What emerged as being critical is the recognition of the emergent nature of Virtually Networked Teams, as if the team itself were a separate entity from the constituent members who make it up.
I also proposed that this new understanding could be further promoted and made useful to a greater number of people by working around the establishment of a new discipline centred on the study of Bioteams.
Bioteaming is a new research area focused on the systematic study of natures’ historically most successful living teams while identifying best methods and approaches to transfer and integrate nature’s best solutions into the daily life of Virtually Networked Teams to alleviate their present handicaps and limitations.
In a subsequent articles, I introduced the first two overarching principles of Bioteams:

“Treat all members like leaders” and “Bioteams are highly connected virtual networks”

I then introduced two triads of bioteams action rules (rules 1-6) which underpin these two principles:

Rule 1 – Send out timely information
Rule 2 – Everyone must broadcast
Rule 3 – Act don’t Ask (Permission Granted)
Rule 4 – Always On/Always Near
Rule 5 – Out-Team
Rule 6 – Nurture the Network

In this article I will introduce a third overarching principle of Bioteams:

“Bioteams are exceptionally good at taking action, co-operating and learning”

I will then introduce the third triad of supporting action rules (rules 7, 8 and 9):
Rule 7 – Swarm!
Bioteams develop consistent autonomous team member behavior
Rule 8 – Tit for Tat
Bioteam members use natural personal co-operation strategies
Rule 9 – Genetic Algorithms
Bioteams learn through experimentation, mutation and team learning sessions

Bioteams are exceptionally good at taking action, co-operating and learning

Rule 7 – Swarm!

Bioteams develop consistent autonomous team member behavior

Natures Way
Craig Reynolds [1] a computer graphics researcher 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’ is extremely complex however it turns out that the underlying member behaviours which produce it are very simple
This must be the case if you think about it otherwise the individual birds, with their ‘bird brains’, would not be able to follow the rules to the necessary consistency whilst performing 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”.
These computerised boids could be made to fly successfully in complex formation in 3-dimensional space provided the individual birds were programmed to consistently follow just 3 very simple 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 [2] has also established that the complex behaviour of nature’s other groups such as ants, turtles, geese and termites can also be explained in the same way through sets of very simple individual member rules.
So in nature very simple individual member actions, so simple they can be easily followed without error, produce very sophisticated group behaviour (without the members even being aware of the complex capabilities they are enabling).
Benefits of Rule to Nature
Nature’s bioteam members don’t have big brains or long memories. However their survival and positioning in the food chain depends on their ability to produce more sophisticated and more intelligent responses as a collective than they could manage as individuals.
Their simple rule-based approach allows them to react exceptionally quickly to situations because the skills they need at an individual level are totally present and ready without any thinking or preparation being required.
In human terms they are able to exhibit what educators would describe as “unconscious competence” – the basis of true expert behaviour – you don’t think about what you do – you just do it.
Application of Rule to Organisational Teams
Today’s common wisdom on creating high-performance teams is that you need to create a team environment where the individual members can fully exercise their creativity and innovation.
This is very true but I believe that nature’s team show us that it is only half of the story of high performing teams.
When we use the normal approach to high performing teams we are actually jumping to the higher team capability level of “complex individual behaviour” but skipping out the lower team capability level of “simple but highly consistent individual behaviour”.
In so doing we sacrifice a number of important benefits by not putting this foundation platform in place first because natures examples prove that :

“coordinated individual simple behaviour can produce more intelligent collective responses than un-coordinated individual complex behaviour”

Now obviously human teams are not going to gain much benefit from the kind of rules which ants or geese use. Human team members have the gift of human intelligence so we need to construct rules which are more abstract and allow space for team members to apply their own judgements.
O-R-G-A-N-I-C team member behaviours
I would suggest the following seven behaviour rules as a discussion starter for beginning to develop consistent autonomous member behaviour in your teams:

  1. Outgoing – get to know all your team colleagues
  2. Recruit – look out for new external partners to strengthen the team’s network
  3. Go! – network widely outside the team
  4. Ask – constantly ask for and offer help to other team members
  5. Note – keep aware/abreast of issues of ‘team intelligence’
  6. Investigate – when you see something interesting investigate it for the team
  7. Collaborate – join at least one team workgroup as an active member – don’t just be a “reviewer”

3-Dimensional Team Members
These seven behaviors are designed to ensure that team members are ‘3-dimensional’ in their operation, just like natures teams, with the ability to concurrently listen, communicate and act in the following 3 dimensions:
1. Member-Member
2. Member-External (i.e. Customers, Partners and Competitors)
3. Member-Colony (i.e. Host Organisations and Teams)
Benefits of Rule to Organisational Teams
Nurturing consistent autonomous team member behaviour provides distributed intelligence in our teams which will result in

  • reduced coordination overheads
  • better fault tolerance – ability to continue even when a particular part of the team is out of order
  • increased speed of spotting problems and opportunities

Rule 8 – Tit for Tat

Bioteam members use natural personal co-operation strategies

Natures Way
Research has discovered that many species in nature use a surprising strategy for cooperation known as TIT FOR TAT (TFT).
The rules of TFT are very simple:
1. Never be the first to defect
2. Retaliate only after your partner has defected
3. Be prepared to forgive after carrying out just one act of retaliation

It appears this strategy is highly popular in nature even in situations where the individuals are only able to recognise their species rather than specific individuals.
Sticklebacks play TIT FOR TAT
One of the best empirical tests of TFT in nature is Milinski’s laboratory experiments with stickleback fish in 1987 [3]
During the early stages of an attack by a stalking pike, some sticklebacks may leave their shoal to approach the predator, for a ‘predator inspection visit’.
They do this as a small group so that they can get very close and if the pike turns on them the fact they are in a group is confusing to it and increases all their chances of escape.
Milinski gave sticklebacks the chance to alter their behaviour according to that of an imaginary companion fish – their reflection in a mirror. The mirror could be angled to give the illusion of a companion keeping up (co-operating) or lagging behind (defecting).
In the experiment the stickleback followed the rules of TFT exactly – for example those fish with co-operating mirrors went closer to the predator and stayed there longer than the fish with defecting mirrors. Also the fish would usually forgive their cowardly companions up to a point and approach the predator again and again.
Some weaknesses in TFT
Very recent research [4] has revealed some weaknesses in TFT – the biggest of which is that it the two players can become locked into a spiral of retaliation.
The problem is this can happen by accident such as errors in communication or interpretation but it may be impossible to break out of the cycle.
Consequently another strategy WIN STAY- LOSE SHIFT (WSLS) (“if its working keep doing it if its not change it”) may be better in certain situations such as those with error-prone communications environments.
Benefits of Rule to Nature
TIT FOR TAT (and other biologically-based strategies such as WIN STAY LOSE SHIFT) provides the most effective long-term cooperation strategies for many species in nature.
In the longer term cooperation is better for the whole community but is open to abuse by individual opportunists.
TFT allows for cooperation to be achieved but not at the expense of being exploited through the retaliation and forgiveness responses which enable conflicting parties to then recover the co-operation after a breakdown.
Application of Rule to Organisational Teams
Recent research has shown that TIT FOR TAT is also the best long-term strategy for human co-operation [5].
Human teams and their members often say that they are committed to “playing Win-Win” which is great!
But what does this actually mean?
I propose that the best strategy for achieving Win-Win is not Win-Win but in fact TIT FOR TAT!
Team members who say they are playing ‘win-win’ are generally referring to one of two very different personal collaboration strategies:
Mr Nice Guy
“I will assume you are cooperating with me until it is proven you are not – then I won’t work with you again”.
In this situation you can be easily taken advantage off at which point you are be too resentful to try and put it right.
Relationships that start in this kind of naivety generally end in tears!
Mr Stand-off
“I will assume you are not cooperating until it is proven you are – and if it is not conclusively proven after a certain time I will assume (privately) you are not a good partner”.
Relationships which start in this kind of distrust usually become self-fulfilling prophecies – start cautiously and you won’t be disappointed!
Win-Win is a state not a strategy
So Win-Win is actually a highly desirable outcome/state but is itself not the best strategy for getting there because Win-Win (in both forms above) has no means of checking a non-cooperating partner and then recovering
Team members need practical personal collaboration strategies such as TFT based on the three simple principles:
1. Never be the first to defect
2. Retaliate only after your partner has defected
3. Be prepared to forgive after carrying out just one act of retaliation

The other key point is to make it clear to all your team members that these are the rules you go by – secret TFT does not work!

Biological research also shows that a “cluster” of TFT players will grow and eventually convert other non-cooperative players to TFT.

However it also shows that if more than three quarters of a population are using non-cooperative strategies then the team is beyond cooperation and is destined to stay in destructive behaviour and its consequences.
Benefits of Rule to Organisational Teams
Viable natural personal co-operation strategies such as TIT FOR TAT keep the team together to create the environment for a Win-Win state to emerge in the team.
Absence of such strategies creates distrust which results in a huge amount of waste such as:

  • people checking up on each other
  • team members falling out
  • people playing politics
  • members raising personality issues with the leader rather than the offending person
  • email wars
  • team cliques

Consistent use of TFT in a bioteam by its members can avoid all this.

Rule 9 – Team-based Genetic Algorithm

Bioteams learn through experimentation, mutation and team learning sessions

Natures Way
Natures teams solve problems using a trial and error process called genetic algorithms
To be more precise a genetic algorithm is a technique for solving computing problems based on evolutionary techniques.
It is so called because the required solutions to the problem are represented as genomes (or chromosomes) as in DNA.
The genetic algorithm automatically creates a population of new solutions derived from this genome by random mutation.
Various selection criteria are applied to pick the best solutions which are then “mated” to create a new batch.
The process then continues for thousands of cycles until the optimum solutions are eventually produced.
So in essence a genetic algorithm works like this:
1. Breed new potential solutions
2. Evaluate how effective they are and pick the best ones
3. Breed new solutions from the best ones
4. Continue this way until the solution is optimum
But normal evolution is too slow
The big problem with genetic algorithms in nature is that progress only happens when a new generation is born. To put is simply

“no giraffe can increase the length of its own neck – only its childrens”.

Thus normal evolution proceeds only at the pace of generations which is far too slow to be useful to human bioteams.
However nature’s most intelligent living systems have found a way to achieve accelerated evolution through ‘intergenerational learning’.
The main prerequisite for intergenerational learning is language.
Language can enable group learning (it’s necessary but not sufficient for this) through ‘social propagation’.
The second pre-requisite is that the species must use their language in stable groups – i.e. flocks.
Songbirds – an example of accelerated evolution
In the 1950s British bluetits as a species mastered the ability to peck open milk bottle tops which the milk men were delivering to the doorsteps of homes in the UK [6] . However the British robin never mastered this skill as a species even though they had the same degree of intelligence and language as the bluetits
Robins were every bit as innovative and mobile as blue tits and individual robins could indeed peck open milk-tops.. However robins are territorial birds and do not flock and did not pass on this skill to their community. Bluetits, however, move around in flocks of 8-10 birds and operating in this way were able to transfer the learning through entire the species via Social Propagation.
Benefits of Rule to Nature
This team-based genetic algorithm provides massive benefits to a species which have it
They can achieve intergenerational learning and therefore adapt much quicker than species who are constrained to develop at evolutions normal speed
It allows them to achieve innovation which provides their species with more options for competition and survival.
Effectively they can choose how they compete within their food chains and whether they are the predator or the prey.
Application of Rule to Organisational Teams
Our teams generally take a totally different approach to developing solutions and learning than the approach used in nature.

Our normal approach is Ready-Aim-Fire whereas nature is really Fire-Aim-Ready.

Nature’s approach will seem counter-intuitive to us and the chasm between our current practice and nature might seem to be too vast to cross!
So to show this is not the case let’s start with a real organisational example:
Capital One Bank
Capital One became one of the worlds leading credit card issuers using the following approach based on Rapid Evolution. [7]
Capital One conducts some 15000 new product introduction (credit card) “experiments” per annum! Each of these is low cost to run and only 1% of them are taken forward based on the early pilot market feedback.
They refer to these as “Evolving Vicarious Systems” – in other words mechanisms to enable them make a rapid prediction of the likely value the market will place on the prospective new product before costly roll-out – a kind of “market proxy”
Implementing Team-based Genetic Algorithms
To achieve this we need to be able to:

  • Create a rich variety of potential solutions to a problem
  • Test the best of these without major risk or cost
  • Discuss the results as a team
  • Refine the best ideas and try again

For this to be effective we need to create a culture where individual failures are seen as essential building blocks for eventual success and rather than being covered up should be explored for learning and improvement.
In the diagram below I outline a simple process a bioteam can use to facilitate rapid learning using the biological method:

Benefits of Rule to Organisational Teams
The benefits of a Team-based biological approach to learning are huge and include :

  • Development of rapid innovation and problem solving capabilities
  • Ability to produce breakthrough solutions from unexpected quarters
  • Early feedback from customers
  • Avoidance of costly failures by spotting them before they happen
  • Accelerated Learning as a team
  • Creating an exceptional staff development environment


You dont have to study nature’s teams for long to realise their biggest strength:
the ability to execute i.e. taking action, co-operating and adapting.
They achieve this by:
Rule 7 – Swarm!
Bioteams develop consistent autonomous team member behavior
Rule 8 – Tit for Tat
Bioteam members use natural personal co-operation strategies
Rule 9 – Genetic Algorithms
Bioteams learn through experimentation, mutation and team learning sessions
If we can incorporate these three action rules into our teams on top of the other bioteam foundations of leadership and connectivity we can achieve massive improvements in productivity, customer-focus, agility and speed of learning.


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
3 Meredith, C., 1998. “The Story of Tit for Tat”, Article for Australian Broadcasting Corporation, ABC Online,
4 Nowak, M., 2005. “Why we cooperate”, Webcast for Royal Society, London
5 Axelrod, R., 1990. “The Evolution of Cooperation”, Penguin
6. De Geus, A., 1997. “The Living Company – Growth, Learning and Longevity in Business”, NB Publishing
7 Clippinger, J., 1999. “The Biology of Business – Decoding the Natural Laws of Enterprise”, Jossey-Bass