During most of the time we spend awake, we find ourselves an integral part of a large and complex system that we know very 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 wealth of information available to our senses. Engaging our rational, slow-thinking minds in every decision we make is too inefficient. The mental models we so heavily rely on are a stark oversimplification of the complex reality we live in.
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 of those ideas to explore and analyze human groups’ structure and behavioural patterns.
We hope that it will provide the reader with useful tools to interpret some of 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 can be 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 quite sophisticated and a whole lot closer to reality.
This comparison between the 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.
2. Table of Contents
- 1. Overview
- 2. Table of Contents
- 3. Human Groups: Definition and Purpose
- 4. Natural Systems
- 5. Simple Hierarchies
- 6. Complex Hierarchies
- 7. Conclusion
- 8. References
- 9. Featured Articles
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.
Groups[…] meet human needs for affiliation and self-esteem. They provide individuals with a sense of security, they reduce anxiety and the sense of powerlessness, and they provide opportunities for individuals to test reality through discussions with others.
Below is a list of advantages that groups bring to the table:
- The emergence of an organizational culture that 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 a set of rules in the form of “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 completing a complex project without being able to see the big picture. Each worker simply does their part and trusts that everybody else will do theirs.
- Generating creative solutions to complex problems requiring many inputs.
4. Natural Systems
A system can be defined as a whole that can be decomposed into smaller, individual, and interacting parts.
Systems can be living or inanimate. Computers, engines, robots, and the Earth’s atmosphere are examples of an inanimate system.
Basic examples of living systems (or, more specifically, a human system) can be 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.
The particular approach that the manager is then supposed to take towards the organisation is that of the scientist, an objective observer, who regards the phenomenon as a mechanism. The whole mechanism is thought to be the sum of its parts, and the behaviour of each part is thought to be governed by timeless laws.
Taylor’s idea was as follows. To maximise efficiency, a company’s manager performs the following steps:
- Step 1: Closely observes and understand 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 state and the target state (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 and 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.
Positive feedback loops are activated by an attempt to change an organization in a fundemental way. This is because change upsets the balance and nature of power and raises levels of uncertainty and ambiguity.
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.
Not only did the corrective measure not produce the intended result, but it also brought in additional problems by introducing security vulnerabilities to the company’s network.
4.5 The Self-Organizing 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, however, were best described as self-organizing entities.
While mechanical systems can be homeostatic, i.e. constantly seeking to achieve a specific state of equilibrium, Kant postulated that self-organizing systems unfold to accomplish a goal encoded within them.
Kant described this unfolding as ‘purposive’ because although an organism is not goal-oriented in the sense of having a movement towards an external result, it is thought of as moving to a more mature form of itself.
In self-organizing 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:
- 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.
- The organization 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.
- 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.
- Predicting the future of organizations is challenging. This challenge arises from our inability to guess people’s responses to current and future stimuli.
- Loose coupling between the system components allows it to adapt more quickly to future challenges. This flexibility, however, increases its complexity and, along with it, any chances of predicting its future behaviour.
- Positive feedback loops are an integral part of the system and cannot be ignored when explaining the organisation’s behaviour.
5. Simple Hierarchies
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:
- Information flow is top-to-bottom. This information takes the form of directions, recommendations, and targets to be achieved within a specific timeframe, usually a year. It is not uncommon 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 to the proper functioning of the organization.
Compliance amounts to the suspension of intellectual and moral judgment about the appropriateness of superiors’ choices and actions. People then willingly do what the powerful want (Bacharach and Lawler, 1980)
- 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.
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. An individual’s 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 in formulating a strategy to be reexamined if they fail. This limiation can be crippling when considering how wild environmental variations can be in cases of major natural events, diruptive tech innovsations, 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.
Structure in human systems is subtle. Structure is the set of interrelationships between people and, because of negative and positive feedback loops, that structure can generate unintended results.
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):
A complex system is one whose evolution is very sensitive to initial conditions or to small perturbations, one in which the number of independent interacting components is large, or one in which there are multiple pathways by which the system can evolve.
Complexity in natural landform patterns is a manifestation of two key characteristics. Natural patterns form from processes that are nonlinear, those that modify the properties of the environment in which they operate or that are strongly coupled; and natural patterns form in systems that are open, driven from equilibrium by the exchange of energy, momentum, material, or information across their boundaries.”
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.|
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.
- Strategic Management and Organisational Dynamics (4th edition) — Ralph Stacey
- Atifragile — Nassim Taleb