Complexity in Natural and Human Systems — Why and When We Should Care

1. Introduction

The reader familiar with our ideas will promptly appreciate complexity theory’s role in shaping our understanding of human systems. But what is complexity, and why is it relevant to a discussion on software development?

As we address these questions, we will focus on fundamental concepts of complexity theory with examples of complex systems, from tiny living organisms to the entire universe and other systems in between.

These concepts will provide us with a fresh perspective that is especially useful for technically oriented staff trained in engineering. More precisely, it will help us understand why mechanistic and reductionist models frustrate our efforts to solve problems or improve processes within teams and social groups.

What will this article cover?

This article focuses on complex systems and the theory behind them. It describes the fundamental concepts in complexity with examples that cover a broad range of domains. The application of complexity to business management in general and software development, in particular, have been described in two articles (Operational Excellence and the Structure of Software Development and Delivery and Principles of Operational Excellence in Software Development). The interested reader is invited to go through them in that order.

2. Complex or Complicated?

We commonly use complex and complicated in our daily discourse to refer to problems that are hard to solve or systems that cannot be easily understood, using both terms interchangeably. However, it is valuable to distinguish between both domains (the complex and the complicated) as the tools used in each will be different.

By separating complex and complicated, we find ourselves equipped with two instead of one method of managing problems. Each method will apply best in one domain but not the other. Instead of universal tools that may work poorly outside their domain of applicability, we will have a richer and more specialized toolset.

The table below shows the main differences between complicated and complex domains. For a fuller discussion, see Dr Dave Snowden’s online talks on the Cynefin framework.

Attribute	Complicated	Complex
Approach	Reduction and logical decomposition	Reduction without loss of prediction power is not possible
Nature of their structure	The whole equals the sum of its parts	The whole is more than the sum of its parts
Solution finding	Engineering solutions exist	Multiple viable and competing alternatives might exist
Management and control	Setting a target state and closing the gap	Nudging in the desired direction
Constraint effectiveness	Constraints are effective.	Constraints are weak.
Nature of causality	If A appears before B, then A leads to B (causality can be established).	A may lead to B today, but tomorrow might lead to C or D (causality cannot be established).
Predictability	Cannot produce new behaviour and are, therefore, predictable.	Shows recognizable behavioural patterns but chaotic and hard to predict.
Innovation	Innovation is not possible.	Emergent properties lead to innovative behaviour.

Let’s take an example to illustrate the points. A computer is an intensely complicated piece of electronic equipment, but it is not complex. The same program with the same inputs always produces the same results. There is no stochasticity, non-linearity, or novel or emergent behaviour. The personal computer cannot self-organize, reprogram itself, or change the laws of physics, quantum mechanics, or data processing.

When modelling a computer, the complicated subsystems can always be abstracted away with simpler representations. Despite this reduction, the abstract model can faithfully replicate the behaviour of the actual physical system.

In contrast, we observe something very different in complex systems. A complex system can modify its internal structure and the interaction rules between its components in response to external stimuli. Attempting to understand and control a complex system, such as a social group, as though it was merely complicated can lead to much frustration.

3. What Is a Complex System?

3.1 Defining Complexity

While the previous section might have given the reader some idea of how complex systems behave, it is time to provide a more rigorous definition of complex systems. The issue, however, is that scientists and experts have not agreed on a single definition of complexity — Dr. Seth Lloyd provides a plausible explanation for this.

In his view, complexity is like physics; it cannot be easily defined, but the physical attributes of a system can defined and measured with sometimes arbitrary precision. Therefore, in this section, instead of giving a unique definition of complex systems, we will quote some experts on what they believe complex systems are. The coming sections will focus on complex systems’ specific, tangible attributes.

In an excellent paper, conveniently titled What is a complex system? the authors have collected nine definitions of complex systems, of which we found two highly insightful. We also present a third definition from Wikipedia.

3.2 Complex Systems — A First Definition

Definition 1 focuses on three important aspects of complex systems which will appear constantly in our discussions:

3.3 Complex Systems — A Second Definition

Definition 2 stresses two distinguishing aspects of complex systems, which will also feature consistently in our discussions:

3.4 Complex Systems — A Third Definition

Definition 3 tells us that complex systems produce emergent properties that cannot be explained by examining their parts in isolation. A complex system is more than the sum of its parts.

3.5 Complex Systems — A Fourth Definition

The authors of “What is a complex system?” argue that neither non-linearity nor feedback loops, spontaneous order, lack of central control, emergence, or hierarchical organisation are sufficient conditions for complexity. So, how can we determine whether a system is complex or not?

To assess the degree of complexity in a system, Professor Lloyd suggests we try to answer the following questions about that system:

Many measures can provide us with quantitative and precise answers to those questions. In the next section, we will explore some of these measures.

4. How Can We Measure Complexity?

Yes, complexity can indeed be measured in a few different ways. Some measures are concerned with the description of the system, such as the amount of randomness of regularity. Others focus more on how easy or difficult it is to create the system’s final state if one starts with the simplest form. Finally, a third measure attempts to quantify the degree of organization, such as the level of decomposition and hierarchies.

Let’s have a look at some of the measures in detail.

4.1 Descriptive Measures of Complexity

When assessing the characteristics of a complex system, it is essential to consider various measures that can provide insight into its structure and behaviour.

Unfortunately, all three measures are inadequate representations of complexity as they highly reward completely random systems, not exactly according to our intuitive notions of complexity. A complex system cannot reside in the extremes but somewhere in the middle, with sufficient order to produce patterned behaviour and enough flexibility to allow for adaptation.

4.2 Difficulty of Creation as a Measure of Complexity

What about the difficulty of creation? Is that a good measure of a system’s complexity? The below measures were proposed in different contexts to measure this attribute. Given a complex system, these methods can measure how easy or difficult it would be to recreate this system from its initial conditions.

Logical depth appears to be the most insightful and subtle of the lot and will be the focus of our attention.

To motivate the discussion, we will use a quote from Charles Bennet’s paper on the subject:

Given a random sequence of one million digits, logical depth can be understood as follows:

The second program would start from a simple rule and recursively add more digits to the sequence until it reaches one million. Instead of a depth of one million bytes, we can store the same information in a much smaller string, a stunningly simple formula for generating Pi. This shortest string is the sequence’s logical depth.

Charles Bennet explains why we assume complex objects should have had humble origins. In his view, the fewer assumptions we make on the creation hypothesis, the more plausible it should be. He uses “ad-hocness” to describe redundant assumptions that a more concise hypothesis can replace. This idea agrees with the principle of Occam’s razor for theory construction or evaluation.

4.3 Degree of Organization

Measuring the degree of organization in a system is a key aspect of assessing its complexity. Complexity is a multifaceted concept that involves understanding a system’s intricate interactions, structures, and behaviours. Measuring organization within the system provides valuable insights into its complexity for several reasons:

Various quantitative measures and metrics are used to assess the organization and complexity of systems, such as entropy, fractal dimension, network connectivity, and information theory measures like mutual information. These tools allow researchers to assign numerical values to aspects of the organization, providing a basis for comparison and analysis.

5. Examples of Complex Systems

5.1 Complexity in Nature

We normally associate complexity and dynamicity with the living. On the contrary, we expect the inanimate universe to be static and eternal. The following four examples show that this assumption does not always hold, and the richness of evolution and behaviour can be experienced in non-living systems.

These examples show how simple rules governing the interaction of a sufficiently large number of agents can produce innovation, emergent behaviour, and unpredictability, all fundamental properties of complex systems.

5.2 Cellular Automata and the Origins of Life

Living organisms are the epitome of complexity. Each organism, from a tiny bacterium to a majestic whale, is a harmonious symphony of intricate systems and processes. To help scientists understand the characteristics and behaviour of these amazing creations, they often turn to various computational models. One such model that has proven to be highly valuable is Cellular Automata (CA).

Cellular Automata, initially developed in the 1940s by Stanislaw Ulam and John von Neumann, provide a discrete computational framework for studying complex systems. The concept behind Cellular Automata is relatively straightforward. It involves a rectangular grid composed of individual cells, where each cell can exist in a certain state. In most cases, this state is represented by a binary alphabet, meaning a cell can be either on or off, alive or dead.

What makes Cellular Automata truly fascinating is how these cells interact and evolve over time according to predefined rules. In the standard model of Cellular Automata, these rules are time-independent and applied to the entire grid simultaneously at each iteration. This synchronous updating allows for the emergence of complex patterns and behaviours, making Cellular Automata an excellent tool for simulating and studying various phenomena.

Rule 110 of the Wolfram set, for example, is very intriguing; it generates intricate and seemingly random patterns akin to those found in natural phenomena, such as crystal growth or tree branches. It demonstrates how a few simple rules can give rise to complexity and emergent behaviour, mirroring the complexity we observe in our world.

One of the most remarkable aspects of Rule 110 is its ability to exhibit both ordered and chaotic behaviour. Initially, the rule starts with a simple row of cells, often called the “initial condition.” As generations evolve, intricate and intricate patterns begin to emerge, containing a mix of repetitive and irregular elements. The complexity increases with each subsequent generation, creating structures reminiscent of gliders, spaceships, and even collisions between patterns.

To appreciate the beauty and intricacy of CA, we recommend looking at Conway’s Game of Life. From random starting patterns, you will see how most evolutions will end in an empty grid with all the cells dying out. At the same time, a tiny minority will generate local structures that keep oscillating forever.

5.3 Explaining Innovation in Complex Systems

In the previous section, we have seen patterns that evolved under constant or fixed rules. However, a model where the dynamics are not allowed to change cannot explain innovation. To introduce the capacity for innovation, the agents of a complex system must be able to modify their interaction rules dynamically.

A 2017 paper by Alyssa Adams, Hector Zenil, Paul C. W. Davies, and Sara I. Walker titled Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems explains how this works:

There are other mechanisms through which complex systems can innovate, and one such system, abundant in nature, is the repurposing of existing features to deal with novel problems. Adapting existing features to solve new problems is a fundamental concept in evolutionary biology and is often called exaptation. Exaptation occurs when a trait or feature that evolved for one function or purpose is co-opted for a different function, often due to changing environmental conditions or selective pressures.

Stephen Jay Gould and Elisabeth Vrba introduced the term “exaptation” in the context of evolutionary biology. They argued that not all features or traits necessarily evolve for their eventual, current functions. Instead, some features may have evolved for a different purpose but were later repurposed or adapted to serve a new function. This concept challenges the notion that all traits result from direct adaptations to specific functions.

A classic example of exaptation is the evolution of feathers in birds. Feathers likely initially evolved in a non-avian dinosaur ancestor for insulation or display purposes. However, they were co-opted for flight over time, which is a completely different function. Feathers are a classic example of a trait that was adapted to serve a new and essential role in the evolution of birds.

Adapting existing features to solve new problems through exaptation is an important concept in evolutionary biology that underscores the complexity and non-linear nature of the evolutionary process.

5.4 The Quantum Computing Universe

One of the most beautiful and revealing passages I have ever read is Professor Seth Lloyd’s opening paragraphs from his book Programming the Universe — A Quantum Computer Scientist Takes on the Cosmos. It goes like this:

In Lloyd’s quantum computing universe, elementary particles in the universe register bits of information. Every interaction between particles is an elementary computation guided by the laws of physics. The universe is a gigantic computing machine. The complexity observed in stars, galaxies, black holes, and life are all emergent features of a large body of interacting particles. At the heart of physical rules are quantum mechanics and general relativity. Everything else (biology, chemistry, sociology, anthropology…) emerges from these “relatively simple” rules.

To support this model, we observe the following:

While the similarities are striking, two important differences set the cellular automata model and the universe apart:

The idea that the universe is computing its behaviour, rules and states, physical laws and objects is mind-bending.

The idea that the future evolution of the universe was fully encoded in the initial states and laws that it began with, just like two cells combine and evolve into a fetus, makes complexity so fascinating.

5.5 History and Human Progress

We think of history and human progress as a slow-paced, incremental build-up of tiny achievements here and there with occasional scientific discoveries and momentous events. This view, however, seems to be incongruent with observation, as Dr. Nassim Taleb tries to explain in his best-seller The Black Swan.

In today’s modern world, progress is dominated by extremely rare events with huge impacts, which Nassim Taleb calls The Black Swan. These events radically change some aspects of our lives such that life before them would seem like a remote past. Because they are so rare, they are unpredictable. Nobody could have predicted the two World Wars, the internet, smartphones, or the 2008 financial crisis. Yet these events happened, and they irrevocably changed our lives through the events of the technological innovations that ensued as a result.

We can think of today’s world as one big society due to globalization. It is an artificial structure that resembles living organisms, replicating, growing, adapting, changing, and self-organising. These complex properties make Black Swans more frequent, unpredictable, and severe (see Antifragile, also by Taleb).

Understanding the intricate web of causality is daunting in a complex and interconnected world. The dynamics of this complexity often elude easy description, and predicting its evolution can seem like an insurmountable challenge. This difficulty manifests through the system’s high sensitivity to initial conditions, which is sometimes called the butterfly effect.

The butterfly effect, a term coined by mathematician and meteorologist Edward Lorenz, is the idea that even the tiniest actions or changes in one part of a system can have profound and far-reaching consequences elsewhere. Lorenz’s metaphor is that a seemingly inconsequential flap of a butterfly’s wings in one corner of the world could potentially contribute to the formation of a hurricane in another.

Illustrating the implications of the butterfly effect, Yuval Noah Harari explores the relationship between social welfare in capitalist countries and the rise of communism in the East. He postulates that the implementation of social welfare policies emerged not solely from moral or ethical considerations but also as a means to pacify the working class and prevent the spread of communist ideologies.

The story of lions in ancient Caanan is another example of what complexity experts call “unintended consequences”. A seemingly insignificant decision on behalf of the Canaanites (a butterfly wing flap) caused wide-scale deforestation in Mount Lebanon.

The Butterfly Effect and Black Swans provided a more compelling explanation of how history unfolds. This realisation has important consequences on intervention policies and decision-making as it shows that intervening in complex systems will always have unforeseeable consequences. These consequences, which sometimes appear as Black Swans (extremely rare and impactful events), become more frequent as the system’s complexity rises.

6. Characteristics of Complex Systems

Complex systems are characterized by a set of defining features and properties that distinguish them from simple or linear systems. These characteristics make complex systems challenging to predict and understand fully. Here are some key characteristics of complex systems.

6.1 Emergence

Complex systems exhibit emergent behaviour, which means that the system’s global properties or patterns emerge from the interactions of its individual components. These emergent properties are often not predictable from the properties of the individual components alone.

6.2 Non-linearity

Complex systems are typically non-linear, meaning that small changes in one part of the system can lead to disproportionately large and unpredictable effects in other parts. This non-linear behaviour can make understanding and controlling the system’s dynamics challenging.

6.3 Interconnectedness

Complex systems consist of many interconnected components or agents that interact with each other. These interactions can be direct and indirect, creating a web of relationships that influence the system’s behaviour.

6.4 Adaptation

Complex systems often exhibit adaptive behaviour, where they can adjust and evolve in response to changes in their environment or internal conditions. This adaptability helps them survive and thrive in dynamic situations.

6.5 Feedback Loops

Feedback loops are common in complex systems and can be positive (amplifying) and negative (dampening). Positive feedback loops can lead to rapid and sometimes chaotic changes, while negative feedback loops can help stabilize the system.

Negative feedback loops are a regulatory mechanism in which a change in a system triggers a response that opposes the change, thus stabilising the system’s original state. These loops are essential for maintaining homeostasis and preventing excessive deviations from the set point in biological, physical, and social systems.

Positive feedback loops are a regulatory mechanism in which a change in a system triggers a response that amplifies the change, leading to a further deviation from the system’s original state. These loops can create self-reinforcing cycles, often resulting in rapid and dramatic changes. Positive feedback loops are important in growth, development, and physiological responses but can also lead to unstable or chaotic behaviour in some systems.

For example, a software system’s design shift can affect the development timeline, leading to cascading effects on the project schedule and further constraints on the development team.

6.6 Robustness and Fragility

Complex systems can be both robust and fragile. They can withstand certain disturbances or failures while being vulnerable to others. Understanding the factors contributing to robustness or fragility is essential for managing complex systems.

6.7 Self-organization

Complex systems can self-organize and spontaneously arrange themselves into structured patterns or states without external control. This self-organization is often a result of the interactions between system components.

6.8 Heterogeneity

Complex systems typically have diverse elements or agents with different properties, behaviours, or roles. This heterogeneity can lead to complex interactions and outcomes.

6.9 Equilibrium, Far-From-Equilibrium, and Strange Attractors

Complex systems can operate near critical points or phase transitions, where small perturbations can significantly change the system’s behaviour. This criticality is associated with power-law distributions and long-tailed events.

Complex systems can reach a state of equilibrium through self-organisation, where the interactions between components result in stable behaviour. For example, market supply and demand constantly reshape supplier-vendor relationships, driving the system to new equilibrium states.

However, strange attractors can sometimes emerge, leading to unpredictable behaviour, even in the short term. For example, a team’s attitude towards quality and responsiveness in software development might shift depending on the current SDLC stage. A typical example would be the (sometimes unexplainable) relaxed observation of processes and best practices when fixing bugs versus implementing new features.

6.10 Path-dependence and Dispositionality

Complex systems often exhibit path-dependent behaviour, meaning that their past history and the sequence of events can profoundly influence their current state and future evolution.

6.11 Multiple Scales

Complex systems can exhibit behaviours and patterns at multiple scales, from the microscopic interactions of individual components to macroscopic phenomena that span the entire system. Understanding these multi-scale dynamics is crucial for grasping the system’s complexity.

6.12 Unpredictability

Due to their non-linear and emergent nature, complex systems can be highly unpredictable. Small initial conditions or parameter changes can lead to divergent outcomes, making long-term predictions difficult.

6.13 Nature of Causality

The relationships between components in complex software systems are often non-linear, meaning that small changes in one component can result in radical changes in another.

Non-linearity and dense networks of interconnections can make predicting the system’s behaviour based on individual components impossible.

For example, changing team structures, tools, or processes to achieve a specified objective with one purpose may create unanticipated side effects (aided by positive feedback loops) that ultimately undermine them.

6.14 Constraints and Boundaries

Software development projects are often subject to constraints and feedback loops that shape their behaviour.

For example, budget and timeline constraints can affect the choices made by the development team.

Systems are also defined by their boundaries, which is another way of thinking about constraints. For example, team boundaries are created by setting rigid team structures, roles, and objectives, which are subsequently reflected in the system’s architecture.

7. Why is it Important to Understand Complex Systems?

As software developers, one might think that complexity theory might be more relevant for social scientists, chemists, or biologists — hardly a topic of concern for developers. That, however, is an underestimation of the importance of the topic.

Below are three reasons why we believe complexity theory is remarkably relevant to professionals working in any organisation.

7.1 The Risk of Mistaking a Complex System for an Ordered One

One of the highest risks in dealing with complex systems is mistaking them for ordered ones. When this happens, efforts to control and manage the system based on traditional approaches will likely fail. 

For example, a common mistake in software development is assuming that a certain category of software project (megaprojects or those with lots of novel technology or business requirements) can be managed like a linear process with a clear beginning, middle, and end. This assumption often leads to a rigid Waterfall approach, ill-suited to handle software projects’ complex, adaptive nature.

On the other hand, treating all software development projects as complex and, therefore, requiring (costly) Agile approaches is equally likely to fail.

The key message here is that, just like in software design, context is essential, and solutions have a bounded domain of applicability.

7.2 Abandoning the Notion of Long-Term Prediction and Control

Another critical characteristic of complex systems is that they are inherently unpredictable in the long term. Efforts to predict the future or control the outcome of a complex system are generally futile.

In software development, for example, it is not possible to predict with certainty the exact outcome of a project or to control all aspects of the development process. This unpredictability is compounded by size, novelty, and uncertainty, for example, in mega-projects.

Instead, an Agile approach combined with a modular design creates a flexible and adaptive process that can evolve as the project progresses. By embracing uncertainty and unpredictability, software development teams can be more effective in delivering successful projects.

7.3 Understanding the Agency of Individuals

The interactions of the individual components within them shape the behaviour of complex systems. While these components may have some level of agency, the entire system’s behaviour is not necessarily a result of the individual choices of these components.

In software development, individual developers can influence local decisions and outcomes. However, the group behaviour and the project outcome will result from interactions between all the stakeholders.

Systems constructed in this manner are hierarchical in nature and are known as Nearly Decomposable Systems.

Understanding how individual roles in complex systems can influence their behaviour is crucial for developing effective strategies not centred around leaders only and avoiding the long-term, centralized, command-and-control paradigm.

8. Approaches to Managing Complex Systems in Organizations and Teams

Complex systems in organisations and teams can be challenging to manage due to the inherent nature of their structure and behaviour. It is vital to consider four fundamental concepts of good management to overcome these challenges: 

1. Systems Thinking
2. Cybernetics
3. The role of informal networks
4. Dispositions in complex systems

8.1 General Systems Thinking

This approach to making sense of organisations is a radical shift from past paradigms that place the individual at the centre of the picture.

Systems thinking focuses on the system rather than individual components by considering the interactions and interdependencies between components.

8.2 Cybernetics

Cybernetics is a field of study that explores the science of control and communication in complex systems. The principles of cybernetics have been applied to various fields, including biology, psychology, engineering, and management.

When applied to organisations and teams, cybernetics provides a framework for understanding the role of feedback and regulation in the functioning of complex systems.

By focusing on the flow of information and control, cybernetics can help organisations and teams better understand the relationships between different system components and develop strategies for managing these relationships.

8.3 Informal Networks

Informal networks are individuals interacting informally (outside formal structures or hierarchies) to exchange information, ideas, and support.

These networks often form naturally within organisations and teams and can play a significant role in managing complex systems. For example, in software development teams, informal networks provide fallback mechanisms when formal processes break down or cannot respond adequately in challenging situations.

These networks facilitate the flow of information and ideas and provide a support system for individuals as they work to manage complex systems.

8.4 Dispositionality

Dispositionality refers to the propensity of a complex system to behave in a certain way based on its internal structure and external environment.

By understanding the dispositionality of the system, managers can work to amplify desired effects and dampen adverse ones. This approach contrasts command-and-control methods that identify a target state and attempt to close the gap.

9. The Role of Processes and Procedures

Processes and procedures play a crucial role in managing complex systems. However, their impact on the system can be positive or negative, and understanding their impact is essential to manage complex systems effectively.

9.1 What Roles Do Processes Play in Managing Complex Systems?

Processes provide instructions for people to follow to achieve specific outcomes. In complex systems, procedures and best practices can guide implementations, ensuring everyone is on the same page and working towards the same goals.

For example, in software development, processes such as the Software Development Lifecycle (SDLC) can ensure that projects are delivered on time, within budget, and to the expected quality standards.

9.2 How Mature Processes Can Serve as Knowledge Storage Devices

Processes can act as knowledge storage devices, capturing the expertise and experience of the team so that it is not lost when team members change. This characteristic can be vital in complex systems where knowledge is often decentralized and dispersed across the organization.

For example, in software development, a mature Agile process can provide a framework for the team to work within, allowing for flexibility and adaptability while still providing structure and guidance. By capturing the knowledge and experience of the team in the process, future teams can benefit from the hard-won lessons learned by previous groups, reducing the risk of repeating the same mistakes.

9.3 How Rigid Processes Accelerate Complex Systems Failure

However, overly rigid processes can sometimes lead to problems. Complex systems are characterized by change and unpredictability, and rigid processes can hinder the ability of the system to adapt.

Applying best practices designed from past experiences to novel situations is, by definition, flawed and can lead to destructive behaviours as it over-constrains systems that are not easily restrained.

The best example software developers are familiar with is following a rigid, sequential process such as the Waterfall model in projects where requirement volatility (and therefore uncertainty) is high, as the process does not allow for experimentation or short feedback loops.

Overconstrained systems are more prone to catastrophic failures (Black Swans) as risks are hidden, and force maintains order.

In addition, rigid processes can create a culture of conformity and stifle creativity and innovation. Complex systems thrive on diversity and creativity, and inflexible processes can limit the ability of the system to generate new solutions to problems.

10. Final Words

Complex systems are a fascinating aspect of our lives and our world. They encompass everything from software development projects to the structures and behaviours of human organizations.

Understanding complex systems is critical because they differ from ordered systems in ways that significantly affect the designing and managing of interventions effective in producing desired outcomes.

The study of complex systems involves several basic concepts, such as the nature of causality, the role of constraints and feedback, and the emergence of novel behaviour. It also requires a systems thinking approach, a recognition of the boundaries and connectedness, and an appreciation of the importance of self-organization and knowledge management.

Challenges in managing complex systems in organizations and teams are significant. They include misperceptions about their nature, difficulties in predicting and controlling their behaviour, and the need for effective processes and procedures to support knowledge storage and decision-making.

Approaches such as cybernetics and systems thinking have been developed to help organizations better manage complex systems, but each has pros and cons.

The challenges of managing complex systems can be addressed in the software development industry by understanding Agile, Waterfall, and scientific management methods.

These methods offer different approaches to working with uncertain and unpredictable situations, with Agile and Waterfall providing a more flexible approach and scientific management offering a more rigid structure.

The best approach for a given organization will depend on its specific context and the software products and technologies it adopts.

Operational excellence is critical to obtaining a competitive edge and increasing an organisation’s value proposition, as it allows organisations to manage their resources and achieve their goals effectively.

In conclusion, understanding the nature of complex systems, their challenges, and the approaches to managing them is essential for success in the software development industry and other complex systems.

Effective management of complex systems requires a balance of rigidity and flexibility, understanding processes and procedures, and focusing on operational excellence.

11. References

• Programming the Universe — A Quantum Computer Scientist Takes on the Cosmos — Seth Lloyd
• Antifragile: Things That Gain From Disorder — Nassim Taleb
• Logical Depth and Physical Complexity — Charles H. Bennett, 1988
• What is a complex system? — J. Ladyman, J. Lambert, K. Wiesner, 2013
• Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems — Alyssa Adams, Hector Zenil, Paul C. W. Davies, and Sara I. Walker, 2017
• Order Out of Chaos — Man’s New Dialogue With Nature — Ilya Prigogine, Isabelle Stengers