Editor’s note: This post is part two of a two part series on mental models. You can read the first post by clicking here.
In part one of this series I stated “A mental model is a model that is constructed and simulated within a conscious mind.” A key part of this definition is that mental models are not static; they can be played forward or backward in your mind like a video player playing a movie. But even better than a video player, a mental model can be simulated to various outcomes, many times over, by changing the assumptions.
Remember the example from part one of the child reaching for the hot stove? One possible outcome we can simulate is that the child does not get burned. We can simulate this outcome by altering our assumptions. We could include a parent in the room who rescues the child in the nick of time. Or, we could simulate the child slipping just before reaching the stovetop because the hardwood floor appears slippery. This kind of mental simulation allows us to evaluate what may happen, given different conditions, and inform our decision making. We don’t have to make any decisions while looking at the picture, but imagine what actions you might take if the scene above was actually unfolding in front of you.
It seems effortless to mentally simulate these types of mental models. Most of the time we are not even aware that we are doing it. But other times, it becomes very obvious that our brain is working rather hard. For example, looking at the chess board below, can you determine if the configuration is a checkmate?
It is indeed. But I’ll bet it took noticeably more effort for you to mentally simulate the chess game than it did with the child near the stove scenarios. Think about the mental effort that the players make trying to simulate the positions on the board just a few moves ahead in the game.
The paper “The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information” by G.A. Miller (1956) established that people can generally hold seven objects (numbers, letters, words, etc.) simultaneously within their working memory. Think of “working memory” as you would think of memory in a computer. It’s like the amount of RAM we have available to perform computations within our mind. And it’s not very much. This means if people want to do any really complex information processing, they’ll need some help. Over the last 50 years or so, the help has come from computers. (In fact, IBM designed a computer specifically for playing chess, dubbed ‘Deep Blue’).
Digital computers have catapulted humankind’s ability to design, test, and build new technology to unbelievable levels in a relatively short period of time. Space exploration, global telecommunication, and modern health care technology would not have been possible without the aid of computers. We are able to perform the computation required to simulate complex systems using a computer instead of our minds. Running simulations with a computer is faster and more reliable.
What makes a model useful?
Models that we can simulate using computers come in many forms. For example, a model could be a financial model in a spreadsheet, an engineering design rendered with a CAD program, or a population dynamics model created with STELLA. But what makes any of these models useful? Is it the model’s results? Its predictions? I think the ability to explain the results is what makes a model truly useful.
Models are tools that can contribute to our understanding and decision making processes. To make decisions, a person needs to have some understanding of the system the model represents. A business finance model, for example, can be a useful tool if you understand how the business works.
Consider a model that does not provide any explanatory content, only results. This type of model is often referred to as a black box. It gives you all the answers, but you have no idea how it works. People rarely trust these types of models and they are often not very useful for generating understanding.
The most useful models are structured so that the model itself will provide an explanatory framework that enables someone to ask useful questions of it. Those questions may be answered by experimenting with the model (simulating) which, in turn, can help deepen a person’s understanding of the system.
This is an important feedback loop in a person’s learning process. This feedback loop can be accelerated if the model provides explanations and can be simulated with a computer.
Transforming your mental models into visual models that are easier to understand and experiment with, will deepen your understanding, and help you communicate your models more effectively.