From ancient to modern times and from world philosophies and religions to music, sports and science, triadic structures appear in almost every facet of life. For instance in music theory, triads form the basis for almost every chord and tonal harmony. In sports, like basketball or football, players organise better and more effectively in adaptive triads. While in human relationships triads are more balanced compared to dyads, which are more volatile and prone to conflict.
The Dutch economist Bart Nooteboom has done a detailed analysis of Simmel’s work on triads. He writes that social networks were always seen as aggregation of dyads. However, he says that in the last 30 years many experts have embraced the importance of triads, and the role that third parties play in a relationship.
As such, adaptive triads seem to be the ideal building blocks for structuring teams and networks of teams, taking advantage of our natural tendency to organise in threes.
Table of Contents
- Adaptive Triads
- Why Adaptive Triads
- Definition of Teams
- Adaptive Triads and Teams as Complex Adaptive Systems
- Team Structure and Dynamics
- Forming Teams with Adaptive Triads
- Scaling Teams with Adaptive Triads
Naturally, teams break down their work into small digestible chunks (tasks, activities or stories) with different individuals or sub-groups picking activities based on their skills, capacity and interest. However, not everyone in the team is involved simultaneously in every single activity, neither should they. But individuals do not work in silos either.
As we saw in our analysis of the triangle offence in basketball, people organise very effectively in triads.
Triads are composed of multi-skilled individuals that can act as a single problem-solving entity. For instance, a team of 5 can combine in 10 different triads, each with different potential.
Triads should be adaptive and temporary, not fixed or static. This means that people should keep forming new triads based on the problems they need to tackle. Organising in adaptive triads is a much more creative and resilient approach than people locked into roles or working in sub-teams that rarely change. As such, team dynamics are constantly adapting to challenges posed by their environment.
Ideally, no more than 3 people should be working on a specific activity at any given moment.
Why Adaptive Triads
- Have most stable structure over time
- Are balanced (thirds party acting as mediators and connectors)
- Tackle conflict more effectively than dyads
- Reduce communication complexity without reducing creativity
- Require less effort to maintain compared to bigger or smaller structures
- Create internal feedback loop (third person creates the ability to give impartial feedback on what the other two are discussing)
- Define team dynamics (adding more people in a triad doesn’t change dynamics significantly)
The level of dynamic collaboration that adaptive triads provide allows cross-pollination of ideas and shared learning with continuous flow of information, people and tasks, while the whole team remains connected through rotation, shared channels, shared working space, and frequent team alignment sessions (daily stand-ups, retros, planning).
As such, adaptive triads can be described as complex adaptive systems, nested inside wider complex adaptive systems, such as the team or organisation, where self-organisation emerges naturally.
Definition of Teams
Over the decades groups have been defined in many different ways, using various models and assumptions. The term groups can cover everything from temporary task forces or crews to long-lived work teams or even social clubs.
This framework focuses on long-lived, multi-project or product-focused, work teams.
Overall, teams have three main functions:
- Complete work
- Maintain team integrity
- Fulfil the socio-emotional needs of its members.
Adaptive Triads and Teams as Complex Adaptive Systems
When we talk about teams we usually make a number of unconscious assumptions, based on our mental model. Our mental model influences how we perceive, process, and make meaning of the world around us based on our experience, knowledge, and set of core values and beliefs.
However, different people have different models of the world. Our reality is not everyone’s reality. As a result when we interact with people who come from a very different background or culture, our assumptions are challenged.
In the past, the majority of models used to define teams has been based on linear, reductionist thinking, which was prevalent at the time. Experts would explain team dynamics by adding up individual behaviours in a simple, linear way, while relying on short-term lab experiments.
In reality, teams demonstrate non-linear behaviour. This means that the interaction of its members create emergent team-level behaviours that are not the aggregation of individual behaviours.
Furthermore, the contextual environment significantly influences the emerging team behaviours and dynamics.
Therefore, to understand teams better we need to model them as complex adaptive systems.
A complex adaptive system is a system of agents that interact with each other and their environment, such that even relatively simple agents with simple rules of behaviour can produce complex, emergent behaviour.Carmichael and hadzikadic (2019) – Fundamentals of complex adaptive systems
Some typical examples of complex adaptive systems are: ant colonies, traffic flow, the climate, the economy, marketplaces, sport teams.
Key model characteristics
The key characteristics of teams as complex adaptive systems are:
Teams interact with their embedded context in two-way flows of energy and information.
Global patterns of behaviour emerge from the local interactions of its members, who constantly learn and reorganise, adapting and evolving their rules of interaction.
Non-linear Dynamic Systems
Teams behave in a nonlinear, unpredictable way. Their seemingly chaotic global behaviour is based on simple rules of interaction between its members at a local level. As such, team interactions and behavioural patterns are in constant flux, never settling.
There are three levels of dynamics that influence each other:
- Local Dynamics – Coordination of actions, understanding, and goals through two-way interaction of individuals
- Global Dynamics – Emergent behaviours at a team level as the result of local dynamics between intelligent, autonomous agents.
- Contextual Dynamics – The culture of the organisation, the availability of money and talent, market conditions, and other contextual parameters influence the patterns of the team.
Dependent on initial conditions
The forming stage of a team shapes dramatically its course over the long-term. Small differences during the forming stage can lead to very different team behavioural patterns that persist over time, despite the ever-changing dynamics.
When a team self-organises, spontaneous order and structure emerge at a team level through the local interaction of individual team members. Self-organisation is a property of the team. There is no external, top-down instruction. However, teams need to have enough internal motivation to overcome their natural tendency towards disorder.
Usually, environmental stimulation triggers self-organisation. For instance, a team has an organisational goal to achieve. Then through internal and external feedback loops the team continuously adapts until it achieves desired outcomes. Gradually, self-organisation leads to more complex behaviours and patterns.
Self-organisation leads to emergent team behavioural patterns, such as learning, trust, agility, leadership, problem-solving, conflict, coherence and others. These behavioural patterns dynamically change over time and never settle.
Although the emergent behaviours and properties of teams are in perpetual motion, they tend to settle into a small region of behavioural patterns, called attractors. For instance, the level of trust in a team fluctuates over time and tends to settle in a region of outcomes.
Attractors can help understand the behavioural patterns of teams and act as a feedback loop of desired or undesired states. The set of points that are “pulled” towards a particular attractor are known as the basin of attraction.
When teams are attracted to fixed points (or fixed behaviours), this is indication of low performance.
When teams are attracted to their own internal rhythm (limit cycles) their performance is average, as they maintain their rhythm, even if they are externally perturbed. Therefore the behavioural trajectory of the team remains stable.
However, when teams are attracted to low-dimensional strange attractors, this is indication of high-performance. In other words, teams perform the best when they operate at the edge of chaos. This means that the team’s behaviour is aperiodic, sensitive to initial conditions, and in the short-term unpredictable. However, in the long-term the team’s behaviour tends to evolve to a set of values, although remaining in constant motion and never repeating exactly the same values. A good example of this is a jazz band that improvises, based on basic rhythms and structure.
Team Structure and Dynamics
As humans, we are really good in forming small intimate groups, where we can trust each other and achieve complex goals.
However, for a group to be considered a team, it needs to be well bounded in terms of members and goals, and stable over time. But teams are not monoliths that stay the same forever. They comprise of autonomous individuals who collaborate and coordinate, creating a greater whole. These individuals may change over time, as people leave or join the business, and dynamics can change substantially.
What keeps the team together is having clear boundaries, maintaining its core members for a longer period of time, and having a strong culture that outlives its members.
Apart from the core team, there is usually a number of people who interact with the team frequently, and contribute to its goals. These are frequent collaborators, and can be external contractors, suppliers, consultants, subject-matter experts, or any other parties that contribute to the team. Business reality is very complex and it is rare to have small teams that operate in isolation and not as part of a wider network of people.
Therefore, a team usually comprises of two nested parts, the core team which owns the team goals and activities and the wider team or frequent collaborators. Of course, the core team should be able to complete most of the work themselves. However, there is a wider network of people and relationships that help the team achieve its goals.
In “What is the ideal team size and why it is important?”, we discussed that most academics, researchers, and practitioners agree that teams that do complex interdependent work need to ideally be no more than 5 people.
This keeps communication overhead to a minimum (no more than 10 channels) while keeping everyone fully engaged and avoiding social loafing. However, for this to work, the core team needs to have a minimum of 3 people, or else they may struggle to complete the work. Therefore, the ideal team size should be between 3 and 5, with a preference for 5. Of course, this is not prescriptive. There are no magic numbers and I have seen bigger teams (up to 9 people) also working quite well together.
However, the rule of thumb is go for a basketball team size (5 people team) rather than bigger teams.
Teams need to be as balanced as possible in terms of skills and not rely too much on external experts. In a way there needs to be a level of skill redundancy. So when someone is unavailable or leaves the team, the rest can still cover the work until they find a replacement.
Therefore, teams need T-shaped people or generalising specialists. People who have deep expertise in one area but are capable in other areas as well. People who are well-rounded, open-minded and are happy to up-skill themselves by learning from their teammates and trying new tasks and roles.
Prefer Intelligent Performers over Lonely Superstars
In “The myth of the genius expert” I talked about how in our increasingly complex world, teams cannot rely anymore on the lonely superstar to save the day. Our focus should be on teams, not individuals. However, teams are more effective when they comprise of individuals who can both think autonomously and work as an integrated whole. People with a growth mindset, who are comfortable in continuously learning and adapting to the needs of the team.
Great teams need autonomous thinkers with diverse opinions that can collaborate effectively together as a whole.
This approach values unselfishness. Everyone is equally important and equally capable of completing the work. Although there might still be one or two individual superstars, the team shouldn’t be relying on them, neither they should be dominating with their opinions. Instead they should learn how to become more effective within the context of the team, and bring out the best from those around them.
Eventually, this approach builds intelligent performers with higher level of skills and abilities, who find more motivation and meaning in their work, are able to self-organise, and feel more responsible for the success or failure of the team.
Team Dynamics – The Yin and Yang of self-organising teams
The behaviour and dynamics of a team are more the result of a decentralised bottom-up process rather than top-down, hierarchical instructions.
Team members self-organise through simple, local interaction rules and principles. They identify what works and doesn’t through experimentation, while getting frequent feedback from their environment. It is a continuous self-organising, learning process that increases the intelligence of the team but also influences the emerging behavioural patterns at a team level.
However, self-organisation or spontaneous order cannot happen without rules that constrain the interactions of the team members. For example, agile frameworks, such as Scrum or Kanban, use constraints as a way to enable the creativity, and workflow of product teams. On the other hand if there are too many constraints teams become too rigid and unable to adapt to changing circumstances. Teams need to be comfortable to operate at the edge of chaos, continuously experimenting with enabling constraints.
Self-organising teams are in a continuous dance between order and disorder, chaos and rigidity. This dynamic balancing act enables flexibility, creativity, innovation and emergence of new forms of behaviour. Both freedom and constraints coexist and complement each other, like the Yin and Yang in Taoism.
Team Leadership – Coaches, not Managers
Teams as complex adaptive systems find what combinations of people and activities work better together in a bottom-up approach. This is so effective, because it can adapt to fit both the strengths of teams and individuals.
However, it is not all about bottom-up, autonomous, self-organisation. In fact, self-organising teams can go spectacularly bad. As such, seeing the whole is equally important to understand whether team dynamics work. It is an important feedback loop that helps teams adjust locally by tweaking their constraints.
Because the team is working well together now it doesn’t mean it will continue working well in the future. New goals and external circumstances will test the team.
Does this mean that teams require leaders giving them top-down instructions?
The answer is NO.
However, they do require someone who will have a global view. Individual team members organise locally and do not always have awareness of the global behaviour of the team. They need someone who will influence the self-organising process by setting up behavioural boundaries and goals while using constraints as levers to steer the team towards better patterns and performance.
In other words teams need Coaches as Leaders, not Managers.
Don’t try to manage the team, instead use goals and constraints as levers of influence.
Similarly to sports, the role of a coach is to see the whole, understand the context, and make adjustments accordingly. Then it is down to individuals to operate within the constraints set up by the coach. Hopefully, in the long-term the team will develop a shared mental model and settle around desirable patters of behaviour (attractors).
Forming Teams with Adaptive Triads
It all starts with a triad as the basic building block.
When teams are initially formed, people often have no connection between them (shape 1). Soon after, individuals start creating ties between them, forming open triads. (shape 2). The term open triads indicates that there is still lack of communication between some team members.
Gradually relationships start forming, and open triads become closed (shape 3). Every closure sparks even more connections, creating new open triads, which in turn become closed (red lines – shapes 4 to 7). Eventually, a dense triadic structure appears with overlapping closed triads (shape 8). Over time triads create their own local culture, shared rules and stories. This results to a more egalitarian and balanced structure.
The above assumes long-lived teams of 5 people, who develop strong ties between them. Of course, the creation of new connections and ties depends on the novelty-seeking or novelty avoidance tendencies of individuals. There is a spectrum within which individuals are curious enough to talk to each other and establish a connection. When they are too similar or extremely different the probability of a connection is less likely. Hence, there are teams where the dynamics never quite work and lack the dense triadic structure shown above.
This is why, it is important to consider people’s skills, personality styles, and ability to collaborate when putting a new team together.
Scaling Teams with Adaptive Triads
A team doesn’t exist in a vacuum but is interconnected with multiple other teams within an organisation, through a web of interdependencies. Trying to predict or control the evolution of a complex social network is impossible.
However, as we saw here, people or groups of people within a social network have the natural tendency to form triads. As relationships shape over time, open triads become closed. This has a knock-on effect inside the network as more new connections are established through open triads. Below there is an example of an open triad, marked in red, which evolves into a closed triad. This makes the coordination of 3 teams much easier through a triad of representatives.
Then triads of teams can then connect with other triads of teams.
The simplicity of this model is that it follows a fractal approach where the effectiveness of working in adaptive triads of individuals can scale to triads of teams and then triads of team of teams.
Fractals are patterns that are self-similar across different scales. In other words the same pattern repeats irrespective of scale.
The diagram above shows how 9 teams of 45 people can connect like a triad of 3 actors. This resembles the formation of a snowflake.
Although this may look like a static structure, it keeps changing dynamically over time with different representatives from each team or group of teams connecting with people from other teams on dependencies.
The above diagram shows the minimum structure required to enable performance. The key principle is to drive coordination through adaptive triads of core actors, irregardless of scale.
Of course, in a network of multiple teams, there are more social ties that are not shown above while teams do not all comprise of 5 people. In reality, networks are more asymmetrical, complex, and ever-evolving than shown above.
Challenges to scaling
Adaptive triads can be used to scale to 3, 9, 27 or more teams through the fractal process described earlier. As such, team of teams can theoretically scale to hundreds of teams.
However, is this efficient? Or even desirable? Should so many teams be so tightly coupled together?
The answer is NO.
There is no need for all teams in a business to be tightly coupled. In fact, they shouldn’t as most teams need to only interact loosely or sometimes never at all.
Scaling assumes multiple teams working together on a single product, service or value stream. But most organisations have multiple products, services, and value streams that do not require everyone working with everyone all of the time. In fact, when the scope is too large, the goal should be to break the down the work into more manageable chunks rather than scaling.
Below are the common problems with scaling.
There is a cognitive limit of around 150 people (Dunbar’s number) with who we can maintain a stable social relationship. Once we cross this boundary, we simply don’t know enough about others to understand or remember their role. This cognitive limit makes people naturally split into smaller groups.
Horizon of Observability
In every social network, there is an horizon of observability beyond which individuals are unlikely to be aware of the role of others in the network. In other words, we may know the contacts of our contacts but it is highly unlikely to know the contacts of the contacts of our contacts.
Density of ties and emerging conflict
Theoretically the density of a social network grows exponentially until every node connects to every other node. However, this never happens as through the scaling process, the probability of conflict also increases and spreads through the network.
Eventually the density of the network (closed triads) reaches a critical mass which is constrained by the emerging conflict. The network never becomes fully homogenised as different opinions, within an acceptable range, coexist in a dynamic equilibrium and in a constant evolution of relationships.
How much to scale
In Aerospace, the development of a new commercial aircraft often includes thousands of people in hundreds of teams working as a whole on one of the most complex industrial programs in the world.
However, the aircraft is not a monolith. It comprises of assemblies, sub-assemblies and components that integrate into a whole. As such, teams organise into smaller product groups, composed often of 4-6 teams. These focus on a particular component or system, i.e. Landing Gear Systems, and have their own boundaries. As such, they interact mostly loosely with other product groups and teams within the wider program. Being loosely coupled in an asynchronous manner allows a level of flexibility and adaptability between the teams, while still achieving the wider program goals.
Using a fractal approach helps us scale easily by following the same principle at different scales – Connect people and teams through adaptive triads.
However, there isn’t really a valid reason to tightly couple more than 9 teams together. Instead, a loosely connected decentralised network of teams can lead to better autonomy, collaboration and performance.
Eventually, treating triads, teams and network of teams as complex adaptive systems can help us design more effective organisations that can truly harness the potential of human dynamics.