I’ve been reading Range by David Epstein, a book I will expound upon once I have finished it. But a key concept that he introduces early in the book is that in order to become a truly transformational thinker in your field of expertise, you have to have a wide range of experiences that you can bring to bear on given problems.
You’ve likely heard the common expression “If all you have is a hammer, everything looks like a nail.” This conveys the same idea of the limitations of your approaches to problem-solving. If you were always trained in a specific approach to solving certain kinds of problems, you will come to rely heavily on that approach. And by always using the same approach, you will likely always come up with the same sorts of answers. Your skill set will be in getting to those same solutions relatively faster than others in your field.
However, in your profession and in your life, the problems are not always the same. And the answers should not always be the same. Or, more importantly, the approach to finding answers should not always be the same. By trying different approaches to solving problems, new answers arise, and better questions are asked.
You should always be looking for a different perspective on your problems at work and in life.
By learning about and using new mental models, we can expand our toolkit. This way we have the answer if the problem is a nail. But we also have it if the problem is a screw, a fuse, a light bulb, or a toothache.
One book I found helpful in learning about problem-solving was The Great Mental Models (Volume 3), by Rhiannon Beaubien and Rosie Leizrowice. In this book, the authors explain multiple mental models and show show how these models from systems thinking can help you take innovative action to solve the problems you face.
The more mental models you have at your disposal, the better you will be at solving problems. Some of those systems models are explained below.
Feedback Loops
Systems theory relies heavily on feedback loops. The concept of a feedback loop is simple. If you flip this switch, that light turns on. If you smile at your friend, they smile back. An action gets specific feedback.
There are two kinds of feedback. Balancing, and reinforcing.
Balancing feedback seeks equilibrium – in just the way your thermostat constantly checks the air temperature to keep it at a constant and comfortable level.
Reinforcing feedback leads to continuous change in one direction, such as when a person is constantly working on learning a new instrument and getting better based on what they hear.
In order for these kinds of feedback to be helpful in your life, you need to make sure that the information you are getting is reliable, and is the correct measure for what you want to accomplish.
For example, if you are trying to become more physically fit and healthy, the primary feedback you will seek is NOT “does this food taste really good?” If you are guided only by the feedback from what tastes good, your efforts to become fit could well end in diabetes, unless you have an unusually strong fondness for vegetables.
Perhaps you have this problem: When I ask my teenager “how was your day?” she just says “fine” and walks away.
You have feedback in this situation. “How was your day” does NOT start a conversation with your daughter. It would be easy to simply decide that the problem lies with your daughter, or with teenagers in general. But that leaves us in a state of helplessness.
What if the problem is not with your teenager, but with your question and your approach? You don’t know that the problem is intractible until you’ve failed using all available solutions, right?
You can make this balancing feedback if you’d like. You can keep asking the same question and change nothing. Or you can make it a reinforcing feedback situation, and change your approach.
Bottlenecks
Another important systems concept is “bottlenecks” or sticking points. These are specific places or events that slow down the entire process from moving forward. A recent example of this came as the US economy was emerging from the pandemic shutdown. Americans were experiencing delays in receiving items they had ordered online. There were several bottlenecks involved. The most prominent was a long line of cargo ships waiting to dock at the largest seaports on the west coast, because there were not enough healthy workers to unload them, or enough healthy drivers to carry the products away and make room for more.
Similarly, a shortage in computer chips led to a spike in car prices as demand far outpaced the supply of new cars.
Thinking about your day in terms of feedback loops and bottlenecks might help you approach problems differently.
Again, with the non-conversational teen, perhaps your question “how was your day” is creating a bottleneck. Maybe the question itself is the problem.
Margin of Safety
Another useful systems mental model is introducing a margin of safety. In systems thinking, it is common to test something to find its breaking point. How much weight can it hold? How far can it bend before it snaps? How hot before it catches fire?
Then, once that level is found, the system is designed to stay well away from that extreme. The difference between the level where a system should work normally, and where the most that a system can handle before it breaks.
In addition to a margin of safety, systems thinkers create backups. When you anticipate and plan for the worst, you are ready even if the worst thing happens.
This is an idea that can be applied to situations in your life. Once you know when and how far you can push your teen, you also know to stay away from those boundaries and red lines.
That doesn’t mean that we, as humans, won’t occasionally behave differently than expected. We are not, after all, closed systems. However, being aware of boundaries and thinking of ourselves as systems helps expand our problem-solving repertoire.
A margin of safety can be helpful when driving a car or managing money, just like it can be in designing an electric grid for a building.
Algorithms
Algorithms are sets of rules that, when followed, create a certain result. While we may not always label them as algorithms, we encounter them routinely every day. They can be simple like a recipe, or complicated like predicting weather. There is likely an algorithm involved in requesting vacation time if you work for a large company.
However, for an algorithm to work correctly, you have to be using the right data.
Back to the example of talking to your teenager. Perhaps the prompt “How was your day?” sometimes works and sometimes doesn’t. Your algorithm is currently lacking important information. There are clearly other variables at play in this situation. You must seek further information in order to improve your algorithm and get to a prompt, or perhaps a series of behaviors on your part, that get your teen to open up and share about their day.
Or perhaps you need to examine what time of day you ask the question, and whether your teen has recently eaten. Food is often part of the solution to any problems involving teenagers.
By examining the entire situation as if it was a system instead of a complex human interaction, you might gain insights for how to get past common roadblocks.
What are some examples from your life of how one of these systems thinking approaches helped – or could have helped – you solve a problem?