Human Groups as Complex Systems: Structure, Organization, Power Distribution, and Dynamics

1. Overview

Most of the time we spend awake, we find ourselves an integral part of a large and complex system that we know little about. We experience events that bewilder us and prove to us, time and again, how poor our abilities to predict future events or people’s reactions can be.

Our intuitive brain cannot cope with the information available to our senses. Engaging our rational, slow-thinking minds in every decision is too inefficient. The mental models we rely heavily on are a stark oversimplification of our complex reality.

Many scientists have studied organization theory and put forward elaborate concepts that attempt to disentangle its complex reality and make sense of it.

This article investigates some ideas to explore and analyze human groups’ structure and behavioural patterns.

We hope it will provide the reader with useful tools to interpret their past and future experiences.

Human beings hardly function solo; in effect, the familiar modus operandi is through a group, held together by interdependent needs or the desire to achieve a common goal.

These groups include families, clubs, sports teams, or professional organisations. The latter has evolved significantly throughout the last century; its composition, structure, hierarchy, interactions, rituals, cultures, and internal politics have become tremendously intricate.

This article will look at two different models explaining organisational behavioural patterns.

One model, centred on a cybernetics view, is straightforward, albeit idealistic, oversimplified, and therefore unrealistic. The other, focused on dynamic systems theory, is sophisticated and much closer to reality.

This comparison between cybernetics and dynamic systems aims to argue the fallibility of any strategic planning that relies on cybernetics as its primary model of organisational behaviour.

Under such a model, we have the illusion of understanding and control, and strategic initiatives that involve transformation and change are doomed to fail. Let’s see why that is the case.

3. Human Groups: Definition and Purpose

A group comprises several individuals bound together by a common need (hunting and gathering food, survival in a hostile environment) or achieving a common goal (organization, football team).

The group members interact together and are aware of their membership in the group.

Below is a list of advantages that groups bring to the table:

• The emergence of an organisational culture creates a sense of belonging and identity and provides the tools necessary for dealing with internal and external challenges.
• Establishes a framework allowing the cooperation of many individuals. This framework consists of rules: “if you do this… you will suffer/obtain that…“
• The ability to accomplish tasks that individuals are incapable of carrying out alone. This collaboration can sometimes happen unconsciously, such as workers complete a complex project without being able to see the big picture. Workers do their part and trust that everybody else will do theirs.
• Generating creative solutions to complex problems requiring many inputs.

4. Natural Systems

4.1 Definition

A system can be defined as a whole that can be decomposed into smaller, individual, and interacting parts.

Systems can be living or inanimate. Inanimate systems include computers, engines, robots, and the Earth’s atmosphere.

Examples of living systems (specifically, a human system) include siblings, couples, business partners, or team members.

A more complex living system can be a large organization with many units and teams or even a population of organizations sharing and competing in the same environment.

4.2 The Mechanistic Model

In 1911, Frederick Taylor pioneered the Scientific Management methods of running an organisation. The objective of his approach was maximising efficiency. While Taylor worked in the US, Henri Fayol followed suit in 1916 in Europe.

Taylor’s idea was as follows. To maximise efficiency, a company’s manager performs the following steps:

• Step 1: Closely observe and understands the current production processes.
• Step 2: She then breaks up these processes into atomic tasks and identifies the skills required.
• Step 3: The duration of each task is measured, and a benchmark against which future performance can be gauged is set up.
• Step 4: The atomic tasks are standardised, and employees are upskilled to perform those tasks, ideally, according to the standard.
• Step 5: Employees are motivated via financial or other incentives.

In this model of human groups, the system is seen as a machine and the individuals as autonomous elements that can be directed to operate under specific rules.

Standards laid out by the management govern the quality of the products produced. Performance is measured and upheld against benchmarks collected from observation.

For a change in the production processes to occur, it must be initiated by the manager and applied automatically and without questioning by the workers.

We will see why this model is problematic a bit later.

4.3 Control Mechanisms and Negative Feedback Loops

Norbert Wiener proposed the concept of a feedback loop in 1948. He is also the first to have used the term cybernetics in his book Cybernetics: Or Control and Communication in the Animal and the Machine, a notion we will explore in the coming section.

In summary, a system that needs to follow a target must be continuously corrected (or regulated) as the target moves.

One way of achieving that is to measure the difference between the current and target states (let’s call it the output error) and use it to modulate the input with a corrective amount.

This connection between the input and the output target state is called a negative feedback look; its job is to regulate the output so that the system is always in equilibrium. The latter means:

You are probably more familiar with self-regulating systems, control mechanisms, and negative feedback loops than you think. The AC in your office, the budget planning and monitoring committee, and the project management team in your organization operate on similar concepts.

4.4 Positive Feedback Loops

While the negative feedback loop is designed to dampen a system’s movement and steer it towards the desired end state, positive feedback loops reinforce the current behaviour and drive the system away from equilibrium and the target state.

Familiar positive feedback loops are vicious cycles: a manager instates a punitive rule to alter the team’s behaviour, such as restricting internet access to prevent employees from shopping online. Employees then find alternative ways to regain access to online shopping sites by, for example, installing insecure VPN software. 

The corrective measure did not produce the intended result but also brought additional problems by introducing security vulnerabilities to the company’s network.

4.5 The Self-organising Model

Immanuel Kant (1724–1804) had a radically different approach to understanding natural systems.

While he agreed that inanimate systems were adequately described by a mechanistic-centred view, living organisms were best described as self-organising entities.

While mechanical systems can be homeostatic, i.e. constantly seeking to achieve a specific state of equilibrium, Kant postulated that self-organising systems unfold to accomplish a goal encoded within them.

In self-organising systems, a part’s role is not determined before assembly but rather through its constant interaction with the remaining elements in the system. Equally important was the notion that the whole is more than the sum of the parts.

In 1977, Karl Weick proposed a model of organizations as self-designing systems. In this model, he explains how self-designing systems emerge. The main points of his model were as follows:

1. The principal representation of an organization is that of a feedback system. This model applies to the entire organization and on every level: departments, units, teams, and even pairs of employees.

1. The organization’s structure, with its complex feedback channels, determines its pattern of behaviour. This shift from external (target state and output) to internal forces remarkably contrasts with the mechanistic approach.

1. The external environment is not static and objective; an organization understands the environment based on its perception and the mutual interaction constantly acting between them.

1. Predicting the future of organizations is challenging. This challenge arises from our inability to guess people’s responses to current and future stimuli.

1. Loose coupling between the system components allows it to adapt quickly to future challenges. This flexibility, however, increases its complexity and, along with it, any chances of predicting its future behaviour.

1. Positive feedback loops are an integral part of the system and cannot be ignored when explaining the organisation’s behaviour.

5. Simple Hierarchies

5.1 Definition

When we picture an organization, we mostly think of a hierarchy of several tiers with senior management on top, middle management in between, and teams of employees in the lower echelons.

Strategic initiatives such as company budgets, yearly targets, mergers and acquisitions, and opening of new markets or territories are formulated at the top, communicated to middle management for planning and coordination, and finally implemented by the teams on the ground.

This picture aligns with the mechanistic view described in the previous sections.

5.2 Properties and Mechanics

There are a few critical points that we can infer from this model:

1. Information flow is top-to-bottom. This information includes directions, recommendations, and targets to be achieved within a specific timeframe, usually a year. It is common to assume that management requires feedback and comments on proposed plans and goals. Still, the assumption is that major decisions are primarily conceived in the higher echelons and communicated down the hierarchy, with the lower echelons providing support and advice where required.

• Authority (Mendelow, 1981 defines authority as the right to enforce obedience) and power (MacMillan, 1978 defines power as the ability to restructure situations) stem from the individual’s position in the hierarchy. The exercise of authoritarian power is accepted and seen as legitimate for the proper functioning of the organization.

• This model places the organization’s management outside the group and, in this sense, is not part of the system. Management is an objective observer who can predict the organization’s behaviour and determine and use key leverage points to steer its development into the future.

• The group is closed; there is a solid boundary between inside and outside. The goal-seeking organization, directed by its regulating mechanism via negative feedback loops, adapts to the changing state of the environment.

• The definition of success is synonymous with stability and equilibrium.

5.3 Limitations

Despite its intuitive concepts, severe limitations apply to the cybernetics and mechanistic view of human groups:

• Leadership is irrevocably an integral part of the system, and, as such, its perception of reality is subjective and incomplete, especially as the organization grows in size.

• Human nature’s needs, irrationality, unpredictability, and spontaneity are not considered. These forces can generate significant side effects and render the system unpredictable. They can modify the power distribution and twist the structure in many different ways, as we will see later.

• Conflicting interests between the individual and group are not incorporated; it is implied that these are permanently aligned. Individuals’ natural tendencies drive them to create, innovate, and challenge the prevailing order. On the other hand, a group requires internal coherence and member compliance to overcome environmental challenges.

• A system where success is defined by stability and equilibrium with the environment cannot explain innovation.

• The environment’s paradigm, as perceived from inside the organization, is not questioned. The framework also does not allow for the methods used to reexamine a strategy if they fail. This limitation can be crippling when considering how wild environmental variations can be in major natural events, disruptive tech innovations, severe competition, and changing client preferences.

6. Complex Hierarchies

6.1 An Updated Model

In the previous section, we described a simple hierarchical model and exposed its limitations in explaining the behavioural patterns of human groups.

Looking at the updated hierarchy diagram above, we can note several essential differences between both representations:

• Although on the same team, some employees will naturally exercise more influence on the overall evolution of the system by virtue of their character, seniority, relationships, or contribution. The different sizes of the circles are used to illustrate this fact.

• Informal relationships can exist between colleagues of the same or different hierarchy levels. These bonds create alliances that can modify power distribution and impact the system’s evolution by either reinforcing or hindering enacted policies and planned changes.

• Finally, in our updated diagram, information flow is bidirectional. Decisions are jointly made between all stakeholders with varying degrees of influence. Decision-making monopoly is not an option as managers need to obtain support to govern.

This new model is more sophisticated than its predecessor but can it be accurately described by complex system thinking?

Let’s have a closer look at what complex systems actually mean.

6.2 Complex Systems

In a complex hierarchical system, behaviour is equally influenced by its structure as well as external inputs such as planned changes from management or environmental stimuli.

To complete this discussion, and since no formal definition of a complex system exists, we will use the following quotations, which appeared in the Science journal (issued on 2 April 1999) on the topic (full article):

Synthesizing these quotations, a definition of a typical complex system would involve the following elements:

• A large number of highly-connected and highly-interacting parts
• Stochastic, non-linear, and chaotic behaviour
• Exhibits new (or emerging) behavioural patterns

We will delve deeper into the properties of complex systems and try to connect these abstract principles with real-life examples from professional situations.

6.3 Simple vs Complex Hierarchies

This section will extend the prior model by adding the missing critical interactions, forces, and properties that generally occur when the human element and the accompanying restrictions on their cognitive abilities in understanding the system and the environment are factored in.

	Simple Hierarchy	Complex Hierarchy
Power	An individual’s power stems from their position in the hierarchy.	Power here is better explained by a combination of expertise, contribution, seniority, strong relationships with influential people, and personal character.
Employee Motivation	It is assumed that short-term goals and instant rewards (financial, promotions, …) are behind an employee’s motivation.	Employees are motivated by their internal commitments and long-term rewards.
Information Flow	Top-to-bottom in the form of a strategy to be implemented and goals to be achieved.	Information flows in both directions via mutual frequent interactions. Individual reality is created from shared experiences and is constantly validated through interactions with peers and the environment.
Authority	Legitimised by position in the hierarchy.	Relies on group acceptance and compliance. This happens if ideologies align, otherwise the group will seek to undermine the manager’s authority.
Informal Groups	Not accounted for.	Informal groups can develop from cooperation, proximity, and shared interests. Informal groups can choose to counter the operations of the formal group or support it.
Covert Politics	Not accounted for.	These defence mechanisms arise against change that threatens vested interests whereby everybody is aware of them but tacitly agrees not to discuss them (Argyris, 1990).
Decision-making	Conceived at the top by influential executives.	Requires some consensus between different stakeholders. Feedback and support from lower echelons are sought so that commitment can be expected.
Relationship with the Environment	It is assumed that a solid boundary separates inside and outside.	The system is “open” where parts are connected to the outside.
Predictions of System’s Response	Assumes a perfect ability to predict the system’s response to stimuli or planned changes.	Complex systems generally produce chaotic and stochastic behaviour. Their responses are also non-linear. This is produced by negative and positive feedback loops.
Causality	Linear. Cause-effect relationships are easily identified.	Circular. Difficult to identify causal relationships. A combination of small changes can produce large effects.
Novelty	Cannot be explained.	The system has a fluid and flexible structure that allows it to change in response to major challenges. This adaptability comes at the cost of efficiency.

7. Conclusion

Reinterpreting past experiences in the light of proper tools can be enlightening, instructive, and refreshing, especially when compared to drowning in one’s sorrow, bitterness, and feeling of powerlessness.

Unfortunately, formal education is too focused on technical subjects and almost totally ignores social sciences, the source of knowledge that allows us to obtain these tools.

But the story does not have to end here if we are willing to invest the time in continuous education and learning.

Examining one’s past experiences with scientific curiosity rather than emotions is no small achievement, but it can be done. Critical thinking, an open mind, patience, and passion for learning allow professionals to achieve operational excellence in their day-to-day activities.

We hope this article has provided the reader with valuable information that can be put to good use in one’s daily interactions with their peers.

8. References

• Strategic Management and Organisational Dynamics (4th edition) — Ralph Stacey
• Atifragile — Nassim Taleb