Table of Contents
- Past research on groups and limitations
- Complex Adaptive Systems (CAS)
- Features of teams as complex adaptive systems
In the last thirty years there has been increasing research on understanding teams as complex adaptive systems. This offers a much richer picture of real-life teams and enables us to understand and perhaps influence the dynamic processes that take place within teams.
Humans have worked in small groups for tens of thousands of years. The development of our language skills allowed us to work together and form small groups, tribes, and later nations in increasingly complex sociocultural structures. Our ability to share information, both about our environment and people within our group, enabled us to organise more successfully, and undertake more complex tasks and work. Eventually, a great diversity of cultures evolved around our unique languages and shared narratives, stories and beliefs.
Past research on groups and limitations
Despite our natural ability to work and live together, it was not until the the late 19th century that researchers started methodically studying groups. In the early 20th century, group research became a field in social psychology. Between the 1940’s and 1960’s research in this field spiked. From the 1990’s onwards, and as the information technology industry grew rapidly, there was a renewed interest in group research.
During the 20th century researchers defined and studied groups in different ways, creating multiple schools of thoughts.
Overall, groups have been defined as systems for:
- Influencing members and fulfilling their needs
- Observing and coding human interaction
- Performing tasks
- In which individuals learn, and grow in self-understanding
- Sociotechnical systems (people, tools, resources, and technology) embedded within larger contexts with multiple outcomes
- Informal work within organisational settings (focus on individual productivity)
- Understanding the dynamic interplay of intergroup and intragroup processes (groups studied within embedding contexts)
- Processing information (problem-solving systems)
- Managing conflict (perspective-blending systems)
- Motivating, regulating, and coordinating the activities of members (sport teams, dynamics of cooperation)
- Enhancing the learning and psychosocial adjustment of members in classroom, clinical, and community settings
Of course, the above approaches in many cases overlap and/or complement each other, contributing significantly to our understanding of teams.
Types of Groups
The term groups is all encompassing. It covers from collections of people that come together temporary to engage in particular activities to long-lived work teams.
Based on the group’s definition and purpose there are four main types of groups.
Teams are long-lived, multi-project, open-ended work groups comprising of members that are strongly interconnected. Because team members work together indefinitely, interpersonal relationships are key to the good functioning of the team. In addition, establishing conflict resolution mechanisms, defining values, rules, and principles can help the team find its groove. But usually, it takes time for teams to get in tune, and create a shared mental model.
Some obvious examples of teams, are all kinds of sports teams, music bands, and technology product teams.
2. Task Forces
Task forces are temporary groups of experts that are put together for short-term projects and disband once they are complete. The group members are mainly focused on their individual contribution on allocated tasks. They do not really identify themselves with the group. Relationship issues are addressed only as impediments to progress rather as opportunities to strengthen ties between the group members.
An example of a task force is traditional project teams, comprising of people from different functions within a matrix organisation.
Similarly, crews are small groups that come together to perform a pre-defined set of tasks with a standardised set of tools. For example, a medical team coming together for a specific surgery, or a flight crew put together for a flight. These are short-term groups that are teaming for a few hours to perform predetermined tasks before they move on.
The members of a crew can be complete strangers, similarly to dancers that meet on the dance floor for the first time. There is always a brief initial socialisation that removes the awkwardness. This helps people get a sense of each other before they engage in dancing or flying an airplane. Trust comes from the brief initial encounter, but also from the mutual understanding that the members of the crew are knowledgeable, trained and competent in the activities they perform.
In contrast, there are social groups, known as clubs, where people come together primarily to fulfil their need for affiliation, networking, and social interaction. Based on their main goal there are social clubs, activity clubs, professional clubs, and other types of clubs.
Definition of Teams
Previously, I defined teams as a special type of work groups that are long-lived, and its members engage in multiple projects indefinitely. As such, the relationship of its members are of paramount importance, compared to other types of groups that are temporary in nature. Teams share collective goals and outcomes, and usually have or need to develop a shared mental model. Furthermore, multiple teams interconnect into larger collectives (tribes, families) that share a common vision.
To be considered a team, a group needs to have at least 3 members and ideally no more than 5. However, the number of members may vary and in some cases the extended team can reach up to 20 people. By extended, I mean people that are partially in the team and have weak ties with the other members. Nevertheless, usually the “core” full-time team members are or should be no more than 9.
Teams have 3 main elements, people, tasks, and tools or technology. When it comes to people, everyone has their own personality, knowledge and skills, including different interpersonal, cognitive and behavioural attributes. Furthermore, they have their own values, beliefs, and mental models. They may be coming from very different demographic backgrounds while having different needs, motivations, and intentions within the group.
The need for a new perspective – teams as complex adaptive systems
In the 20th century the field of social psychology and its related domains used mostly a positivist-reductionist methodological approach. This means that research has been relying heavily on laboratory experiments and quantitative research. Teams were, for the most part, treated as isolated systems, with strangers coming together in the lab for a limited amount of time to perform tasks and then disband. In addition, researchers were more focused on measuring a small number of variables in a static, linear (cause and effect), and additive way, ignoring any real-world context.
As a result, this has created a number of limitations in the existing research.
Limitations in existing research
First, teams were treated as oversimplified linear systems, where the behaviour of its members could add up to explain the behaviour of the team. However, teams are complex entities with behaviour that emerges from the interactions of its constituent parts in a non-linear way.
Second, studying teams in isolation with no consideration of their real-world context is limiting, if not wrong. Teams are adaptive systems that influence and are influenced by their environment.
Third, having groups of strangers with no previous history, no commitment to each other, and no future expectations, coming together for a few hours in a lab setting doesn’t help understand how real teams form, and continuously evolve over a long period of time.
Nevertheless, it was not until the 1980’s that scientists started studying complex dynamic systems in a systematic way. This has enabled us to see the world through new lenses.
In the next few sections, I will try to give an overview of CAS, and define their characteristics and properties. My aim is not to do a thorough scientific analysis but rather to summarise the key points that will enable the reader to get an initial understanding of CAS, in the context of teams.
Complex Adaptive Systems (CAS)
Alan Turing, in one of his last papers in 1952, was the first to present a mathematical model on complexity. In the late 1960’s scientists Evelyn Keller and Lee Segal drew ideas from this paper to understand the behaviour of the slime mold. This was the first time slime mold was treated as a complex adaptive system. Up to then, no-one had been able to explain its behaviour using conventional approaches. CAS allowed scientists to make a giant leap forward.
A few years later, in 1984, a number of multidisciplinary scientists came together and founded the Santa Fe Institute. This was set up as an independent, non-profit research institute dedicated to the interdisciplinary study of complex adaptive systems. Their work has contributed significantly to our understanding of CAS across different fields.
Definition of Complex Adaptive Systems
First, I will start by providing a definition of a complex adaptive system.
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
To put it simply, in a CAS the whole is greater than the sum of its parts. As such, a CAS must be studied holistically in a multilevel approach.
Some common examples of Complex Adaptive Systems:
|Examples of CAS
|Flock of Birds
|Crowds of People
A Complex Adaptive System Model
A CAS model comprises of a number of self-similar agents or component parts. The agents can be anything from cells or antibodies to ants in a colony, or people in a team. Through their two-way interactions the agents utilise one or more levels of feedback to self-organise. As a result they exhibit non-linear dynamic behaviour, while the system exhibits emergent properties.
Although the behaviour of CAS is complex, this doesn’t mean that the agents are centrally controlled. There is no central leadership, no governing equation, no tidy mathematics, no cause and effect, and no central planners. Instead, their global behaviour derives from the interactions of its agents that are largely autonomous, following their own simple rules. In addition, the agents are self-similar and can be replaced without disrupting the emergence of the system.
In other words, there is a distributed rule-based structure that can constantly adapt to new situations. For instance, traffic patterns can be quite complex. But every single car follows a few simple rules in relation to the car in front of them. For example, accelerate or break to keep moving without colliding.
Complex Adaptive Systems share three key characteristics
The key thing to remember is that the agents of the system only get information from their neighbouring agents through local feedback mechanisms. Then they make decisions and act locally. These rich patterns of correlated interactions at a local level produce emergent system properties at a global level. – Aggregate Behaviour
Another point is that agents are intelligent and adaptive, constantly adapting to their surroundings. With time they learn, evolve and reorganise by revising their rules for interaction. For instance, the immune system adapts to viruses by adapting its antibodies. To do this, they use a credit assignment mechanism, that reinforces ‘good’ performance. A rule that produced a good outcome in the past, most probably will be preferred in similar situations in the future. Furthermore, through a rule discovery process, parts of ‘good’ strong rules recombine into new rules, balancing exploration with exploitation. However, the system might break down if the agents diverge too far in their rules. – Evolution
Third, the rules the agents follow are parsed into simple condition-action rules. However, due to their heterogeneity, agents are conditioned in different ways. As such, they can anticipate the consequences of certain responses from other agents, making advantageous stage-setting moves. – Anticipation
Features of teams as complex adaptive systems
A term that we often use to describe CAS is Organised Complexity as these systems are operating at the edge of chaos. They are always on their way to somewhere, evolving, but never reaching. Their value lies in the constant process of evolution. Complex systems are neither rigid nor chaotic, there is order in their chaos, and it is this characteristic that makes them adaptive and resilient.
On the other hand disorganised complexity describes system that have agents that interact randomly and their behaviour can aggregate and described by statistics. There is no order in their chaos and there is no self-organising behaviour either.
Teams are complex systems
Complex means that teams are self-organizing systems in which global patterns of behaviour emerge from the local interactions of its members. These global patterns are in constant change while new are emerging as the team members (agents) continuously adapt to their surroundings. Examples of emergent behaviours in teams are: trust, agility, learning, leadership, conflict, coherence, and more.
Complex adaptive systems are open systems which interact intelligently with their environment, exchanging information and energy in two-way flows. However, the boundaries of an open system are not always clear, and usually need to be defined. When there is persistent feedback coming from outside the system, it constraints and influences the dynamics of the system over time. As such, this is something we need to consider when studying the system.
Teams are Open Systems
Open means that there are regular two-way interchanges between the team members, between the team and other teams, and between the team and its embedded context (i.e. organisation). In a way, the three different levels, individuals, teams and organisations are ‘nested’ systems.
Nonlinear Dynamic Behaviour
Most of us, through school, we learn how to think in an analytic, methodic, rational way. This means that for every problem we make simple cause and effect connections, and think sequentially, from a starting point to an end point. In other words, our thinking goes through steps in a linear style. Moreover, linear equations are solved easily as the output is proportional to the input. Everything adds up. For solving well-defined repeatable problems this approach works well, and our minds are good at it.
However, most phenomena in nature are complex and hence nonlinear. In complex systems a small local change can create a very large unpredictable global change. Nonlinear equations are really hard to solve as the character of the equation keeps changing. For instance, the weather is a complex system that behaves in a nonlinear and unpredictable way. It is constantly changing in an aperiodic fashion, never settling to a steady state (non-equilibrium), and never repeating itself. As such, they are inherently unpredictable. Extremely small differences in input can have really major differences in output, which means that there is sensitive dependence on initial conditions.
This is commonly known as the butterfly effect.
The Butterfly effect
The notion that a butterfly stirring the air today in Peking can transform storm systems next month in New YorkJames Gleick – Chaos
A linear approach ignores the small differences and misses completely the chaotic properties. In contrast, complex systems are nonlinear, because both their starting point and history influence their future behaviour. However, no matter how complex the calculations of the many attributes of the parts of the system, they essentially are the expression of a handful of simple rules (heuristics).
Nonlinearity in Teams
Small differences during the initial stages of the team’s life (forming) can lead to completely divergent trajectories for the team. Initial team behavioural patterns influence the future ones. Due to the nonlinear interaction of the team members and their interdependence, we cannot decompose the emergent behaviour of the team to its constituent parts.
It is not easy to replace team members without affecting the dynamics of the team. By leaving, the network of interactions that this person had with other people, the tools and the work itself, through his knowledge and skills, are also removed.
All teams have three generic functions, to fulfil member needs, to complete work, to maintain team integrity. In other words, there is a continuous interplay between activities and socio-emotional needs of the members.
During the life of the team, which is operations, there is continuous change due to learning and experience, which in turn affects how the team members work between them.
There are three level of nonlinear dynamics that continuously shape a team.
The local dynamics of a team are shaped by the two-way interactions of its members and the rules, norms or processes that define these interactions. The local dynamics shape a coordination network. There are three levels of coordination. First is coordination of actions – how people synchronise to complete the work. Second is coordination of understanding – agreement on processes, approaches, tools, who does what. Third is coordination of goals – priorities at a team level, mix of motivations for a common purpose.
Global (or Team-level) Dynamics
Local dynamics give rise to team-level or global dynamics. These are the emergent properties of the team, which in turn shape and constraint the local dynamics of the members in two-way interactions. The emergent behaviour of the team is not the aggregation of individual behaviour but the outcome of a complex process of interactions between diverse, autonomous, and intelligent individuals.
The embedded context (environment) in which the team exists define its contextual dynamics. These contextual parameters affect the patterns of the team-level dynamics over time. For instance, the culture of their organisation, the availability of money and talent, the availability of space and tools, the market conditions, and other contextual parameters, they all influence the team.
Self-organisation (or spontaneous order) is a process where emergent structures, and order arise from the correlated interactions of the autonomous agents of the system, without any external instruction or guidance. However, there needs to be sufficient internal energy available to trigger the self-organisation of the system and overcome its natural tendency toward disorder. Furthermore, self-organisation is facilitated by perturbations or external pressure that “push” the system to explore a variety of emergent states.
An example of this is a flock of birds. When the birds are flying in formation they don’t follow a plan or the instruction of a leading bird. Instead they adjust their position based on the birds next to them. The flying patterns observed are the result of local interactions and adaptations of loosely connected agents. If the connections are too rigid it will not be possible for the system to self-organise and adapt.
A self-organising system moves toward more complexity.
Teams can self-regulate through monitoring and feedback loops, which are key for achieving its goals and desire states. Although the goals might change due to internal team dynamics or external circumstances, the team has the ability to adapt and invent new ways of doing things.
Overall the general patterns of team behaviour over time can be predictable while the details of its behaviour are inherently unpredictable.
Emergence is the result of self-organisation, where new patterns and properties emerge at a global-level, due to the rules and interactions of its constituent parts at a local level. For instance, trust or learning in a team or an organisation are emergent features. Alternatively, a political revolution or the development of a city are also emergent behaviours, without any central control. Another example is our behaviour and thoughts, which emerge from the activity of the neurons inside our brain. Similarly in physics, gravity is an emergent property deriving from the interactions of particles.
Emergent phenomena are persistent patterns with changing components
On the other hand, in complicated systems, such as a car, emergence is a bug. Building a car is complicated because its constituent parts are specific, and interact through a strict hierarchy. There shouldn’t be any deviations or surprises. Any emergent behaviour is a bug.
Emergent Behaviours in Teams
As mentioned earlier, with emergence we refer to the team-level behaviours (global level) that derive from the nonlinear, correlated interactions of its members at a local level using simple rules and local feedback.
For instance, trust in a team emerges from the local interactions of its members and how they exchange information, how they communicate, their history of interactions and their expectations. The level of trust in the team is never constant but dynamically changes over time. Other examples of emergent team behaviours are conflict, leadership, coherence, and agility. These emergent properties are in constant change but tend to be attracted to and settle into a small region of values, called attractors. In practice, this means that although the level of trust in the team fluctuates over time it tends to settle more often in a region of outcomes. Similarly, leadership within a team is an emergent behaviour that keeps changing as different members lead in different activities or circumstances. However, as with other variables, leadership behaviours can settle in specific patterns over time.
Attractors are small regions of numerical values toward which a dynamic system tends to settle, for a wide variety of starting conditions. Contextual parameters influence whether a system settles in a single or multiple states. Identifying the type of attractors for given global variables are important in order to understand the evolution of the system through different states and the contextual parameters that affected this evolution. For instance, a contextual parameter of a team is the accepted leadership style in an organisation (egalitarian or hierarchical). This constrains the patterns of emergent leadership in the team. Another example of a contextual parameter is the distance from the equator of a specific location. This influences the weather pattern in that location, and whether there will be changing seasons.
Due to its sensitive dependence on initial conditions, after many iterations, any two arbitrarily close alternative initial points on the attractor will lead to points that are arbitrarily far apart. But then after more iterations it will lead to points that are arbitrarily close together. One of the most famous attractors is Lorenz attractor, which macroscopically resembles the wings of a butterfly. It reveals a global structure within a disorderly stream of local data. The trajectory never interjects itself, while the loops carry on around forever, never settling.
For thousands of years people have worked in groups to perform activities and fulfil their socio-emotional needs. During the 20th century and because of the mass industrialisation, there was an extensive academic interest on how people work effectively together.
Scientists, for the most part, used a positivist-reductionist-linear approach in their research. Groups were studied usually in lab conditions as static, predictable, controllable entities. They used a simple cause and effect approach to describe their behaviour, wrongly decomposing team-level behaviour to individuals. In the last 30 years, and as knowledge work has become the predominate type of work, there has been an intense research focus on long-lived, multi project, open-ended teams.
In parallel, the scientific shift towards chaotic and complex adaptive systems across multiple disciplines has changed the way we view teams. This hasn’t been a comfortable shift as people had to completely change their mental models and their pre-existing assumptions. Nevertheless, it has provided us new lenses to understand deeper the complex phenomena that are taking place in real-life teams. There is so much more we can learn if we abandon our outdated and limiting views of the world. But perhaps this is one of the hardest thing to do.