How to resolve stubborn business and personal problems Part 3 - Working with unpredictable reality.

This series of posts covers a range of topics. The first post explores new concepts to deal with stubborn problems, while the second provides an in-depth look at queues. The third post is dedicated to understanding and working with unpredictable systems and how queues shape their behavior.

What is an unpredictable system?

There are two types of systems - predictable and unpredictable. Predictable systems have a clear cause-and-effect relationship, which means that one can predict specific outcomes or behavior of the system after taking particular actions. For instance, if you turn on a faucet into a sink and block the sinkhole, eventually, water will spill from the sink.

On the other hand, unpredictable systems have unclear causes and effects. For example, it is impossible to predict how much rain will fall in a specific location, even if we know all the conditions of that particular environment. Even if we try to take action to influence the outcome, no one can predict accurate participation.

Despite starting as predictable, most systems can become unpredictable with changes over time. For instance, a sink with a faucet that is not continuously running can be unpredictable if the flow changes, as it becomes impossible to predict when the water will spill out of the sink.

what distinct unpredictable systems from predictable systems

Humans tend to prefer systems that are predictable and stable. However, most of the systems we encounter daily are unpredictable and inconsistent. The critical difference between predictable and unpredictable systems lies in their behavior. Predictable systems are linear, progressing smoothly from one stage to another. In contrast, unpredictable systems are non-linear, and their behavior can be erratic and uneven.?

Furthermore, systems can be classified as deterministic or nondeterministic. A deterministic system will always produce the same results if provided with the same starting conditions. On the other hand, a nondeterministic system may generate different outcomes even when the starting conditions are identical. This is because a nondeterministic system is dynamic and subject to changes in its operations.

Therefore, unpredictable systems are characterized by their dynamic and inconsistent nature.?

Take a moment to reflect on the systems you work with. Are they predictable or unpredictable? If you find that most are unpredictable, read on.

How do queues contribute to the unpredictability?

Systems consist of actors, queues, and actors' actions to manipulate the queues. An actor can be something like a faucet, a queue could be the water level in a sink, and the action would be the water flow from the faucet.

In predictable systems, actors have a predicted influence on the queue, which will have known results. Queues, on the other hand, hardly impact one another in a predictable system. A change in a queue is usually a result of an action taken by an actor, not a result of a change in another queue. The same applies to Actors in predictable systems; they rarely impact each other.

The dynamic is entirely different in unpredictable systems. The actions actors take to influence a queue are negligible compared to the impact that queues have on queues and actors on actors.?

It's not very useful to spend a lot of time trying to understand how an actor affects a queue, as it does not really help to understand the system as a whole. Instead, it is a better investment of time to focus on understanding the dynamics between queues and between actors when dealing with unpredictable systems.

Are unpredictable systems introducing different problems than predictable systems?

Unpredictable systems pose much more complex problems than predictable ones. These problems are not only tricky but also recurring. That is why we call them stubborn problems.

When people try to solve stubborn problems using the same logic they use to resolve predictable system issues, they end up with recurring problems. Using tools created to deal with predictable systems leads to a shallow understanding of unpredictable systems. This shallow understanding only identifies one of the symptoms of the system as the root cause of the problem. However, resolving or removing the symptom will not fix the issue, and it will recur.

Most people oversimplify an unpredictable system and use familiar toolsets to make it look predictable. Oversimplify is the wrong approach because it assumes that the problems are the same regardless of the system context. Unpredictable systems' challenges are exponentially more complex than predictable systems' problems. They require a different approach, not just different toolsets.

How to deal with unpredictable systems

We will discuss various toolsets that can be used to deal with unpredictable systems. However, before delving into that, it's important to touch on other aspects.

The first and foremost point to remember is that accepting the nature of unpredictable systems is better. Assuming that an unpredictable system is predictable would only lead to frustration. Taking reality as it is will make it easier to deal with.

Secondly, it's essential to understand that you cannot influence or impact the dynamic between queues and actors. Instead of manipulating queues by actions, try to understand the dynamic between queues and leverage them. This dynamic can be observed as patterns between queues and patterns between actors. As humans, we cannot influence or change queues to achieve the desired results. Clinging to the belief that we can manipulate everything will only result in frustration.

So, how do we understand the dynamics between queues and agents? The answer is systems thinking, which is currently the leading tool available. Systems thinking focuses on the interactions between queues and agents and introduces visual language to depict queues and their impact on each other, as well as how agents affect each other. Available tools can translate these visual languages into a working simulation that will give us a better understanding of the dynamics of the system we are exploring.

We will explore in detail all the systems thinking tools available to us, using examples to make them clear and easy to use.

In the following post, we will take a deep dive into how we can depict queues and their interaction and how we can use this data to gain different insights about the system we are exploring.