The big difference between complicated and complex systems: The myth and trade-offs of complexity reduction and how to do it right.
Is work becoming complicated, or is it becoming complex? There’s a huge difference between the two. Unfortunately, many people, including business owners and leaders, cannot tell the difference. Learning to differentiate between the two takes skill and training. However, once you learn, you can positively impact the outcome of your life and your business or organization. In the following article, we will go over the importance of the skills required and relevant methods you can use.
The Importance of Decision-Making
Decision-making is what leads to the outcome and degree of success or failure. For example, picture the following scenarios:
- You are about to become an executive, but you have a great idea for a startup that could make you wealthy. Should you take a risk and leave a successful career behind?
- You just graduated from college and have received several job offers. Which job will you select?
- You’re an entrepreneur or the CEO of a company struggling with declining product sales. You have two choices: invest in marketing and facelifting an existing product or invest in an entirely new product. You only have enough funds to choose one option; which would it be?
In each of these scenarios, you are faced with a decision. Each decision we make will carve out the path of our life or business. Our minds are limited to how we assess the outcome of each decision. Like in a chess game, our minds have a maximum capacity for how many future moves we can anticipate. In many personal and business situations, outcomes can depend on many factors that lead to numerous scenarios. As we try to forecast further into the future, our levels of uncertainty increase exponentially. Our brains can quickly start resisting the number of outcomes we can create and process. Once that happens, we fall back into heuristics and intuitive decision-making.
What Is Linearity?
A linear system adheres to two superposition principles:
- Homogeneity is where the output of a linear system is directly proportional to the input, so if we put ten times as much into the system, we will get out ten times as much. For example, if I pay $1 for a banana, I get one banana, and $10 will get me 10 bananas.
- Additively states that if we add up the output of two systems, the resulting aggregate system will be equal to the addition of each system’s separate output. For example, if two monkeys can each eat one banana per hour, two monkeys will eat two bananas per hour.
When this relationship is plotted on a graph, we always get a straight line — what we call the relationship linear. Linear systems are typically found in administrative departments with repetitive and sequential tasks such as payroll, general ledger, employee onboarding, or production factories. Scale curves, best practices, workflow automation, process reengineering, kanban systems, and data analytics can significantly improve system performance, for example, by maximizing system throughput at the lowest possible cost.
A linear system’s maximum capacity and performance are usually limited to a theoretical maximum. We can measure the performance improvement as a percentage equal to the system’s maximum capacity minus its losses. For example, adding an employee to the payroll increases output by a factor equal to the employee’s output.
Nonlinear systems typically have different areas that perform various functions or roles. In other words, it’s when the output is an iterative function of itself that is usually unpredictable. Consider it like developing a chess strategy; your next move will depend on your opponent’s current move, which is unpredictable. After the second move, the number of possible scenarios grows exponentially. When iteratively designing a new product, you beta test the product by putting it on the market in limited quantities. Then, you iterate and improve the design based on market feedback.
An example of a nonlinear system is a positive feedback loop from the viral effect of a new product launch. For example, a company may launch a new product on a platform such as Amazon. If the initial sales receive positive feedback and top reviews, it will drive others to buy and share the product, thus creating a positive feedback loop. Negative feedback loops produce the opposite effect. For example, introducing a more desirable substitute to an existing competitive product of an incumbent.
Problems Caused by Indifference to Linearity
After working with over 400 organizations, I discovered that the most common mistake made by business leaders is that of prediction. Business leaders expect that what worked successfully in one place will work the same and succeed in another. However, this is rarely the case, owing to the fact that most organizations are nonlinear complex systems. They must be adjusted to deal with unique circumstances such as the industry, strategy, or organization.
One commonly used practice is reductionism — the process of breaking down or reducing a large system to its constituent parts and then considering the entire system as the sum of these parts. This approach is adopted across most problem-solving methodologies in areas such as management, engineering, and consulting. The key drawback of the analytical method is the use of the concept “Ceteris Paribus,” meaning “other things equal.” Variables within the environment are artificially held constant to isolate and perceive the linear effect on a limited number of variables under observation. When these individual components are analyzed separately and then reassembled, the interaction and the feedback loops that characterize nonlinear systems are not accounted for.
That said, this linear method contributed to remarkable feats such as the Industrial Revolution, primarily due to success in linear systems. However, it is inadequate for nonlinear systems. In contrast, one of the guiding principles of holism, which is opposite to “Ceteris Paribus,” is called “Panta Rhei,” meaning “everything flows.” The idea that everything changes is derived from the Greek philosopher Heraclitus’ observation that one cannot step into the same river twice.
What are Complex Systems?
Once we understand linearity, we can build on it to explain complex systems. There is no universal definition of a complex system. However, it is reasonable to consider that a complex system is composed of multiple diverse elements that are independently connected and enable some functionality. A system is made up of parts or elements that perform some common function. Examples of systems include the human body, a city, a nation, and the internet.
Complexity has many moving parts that interact with each other. It’s a measure of the number of elements within a system, the degree of interconnectivity, and diversity. The internet consists of trillions of web pages and can be used for numerous applications and functions that are highly interconnected. Similarly, we could consider a business as having different subsystems: suppliers, people, customers, competitors, etc. — all of which interact and are somewhat interdependent.
How to Work with Complex Systems
Complex systems cannot be reduced in complexity. By simply existing, components of complex systems behave unpredictably, making studying the individual components ineffective through reductionism. The interactions of the components are more relevant than the unit itself. To comprehend how a complex system operates, you must understand the system as a whole (holism). For example, to assess an organization made of people, you must understand the entire organization as one. For example, to study a city, instead of studying in chunks or as a collection of neighborhoods or buildings, you need to study the entire city. This is what complexity theorists call the emergent property of the system.
They need to be studied and observed in order to differentiate between the parts and the whole. You can create a map of the interactions between the parts, which I wrote about in a nature article. The article talks about how a flock of birds and a school of fish obey simple rules, which can simplify your understanding of the system but not reduce its complexity.
Complex systems are the products of multiple interacting elements. You can study complex systems, but it requires a paradigm shift in thinking, referred to as “systems thinking.” It is different from a component-based and process-based set of methods. As a result, because complex systems are naturally nonlinear, you can not influence or reduce their complexity.
What Are Complicated Systems?
Complicated systems are not always complex. They are like the mechanics of a clock or the flow of an assembly line. A clock is complicated and has lots of moving parts. However, we can dissect it, disassemble it, and figure it out.
A complicated linear system is typically characterized as follows:
- It is predictable.
- It has clear cause-and-effect relationships.
- It has been linearly optimized for efficiency.
- It has constraints that can be reduced or eliminated.
- It can be standardized.
- It is process-driven.
A complicated linear system can most likely be taken apart, with each part studied individually. It can then be reassembled, reducing complicatedness, improving performance, implementing best practices, and ultimately simulating a cause-and-effect relationship.
While you can not reduce complexity, you can reduce the amount of complicatedness. In everything you encounter and every decision you make, every minute of every day, ensure you determine and distinguish between the linear and the nonlinear. This will help you improve your decision-making skills. From here, you can apply standard management tools, and best practices to linear systems as well as identify and address complex systems using systems thinking.
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