What are Mental Models?

When I talk with people about software, I often use the term ‘mental model’ to describe what the user expects when they interact with software. I may say something like: “The interface is not aligned with most users’ mental model.” What the heck does that mean?

Well, a model is an abstraction or simplification of a system. Models can assume many different forms – from a model volcano in a high school science fair to a sophisticated astrophysical model simulated using a supercomputer. Models are simplified representations of a part of reality that we want to learn more about. George Box stated: “Essentially, all models are wrong, but some are useful”. George is saying models are wrong because they are simplifications, but they can be useful because we can learn from them.

What is a “mental model”? A mental model is a model that is constructed and simulated within a conscious mind. To be “conscious” is to be aware of the world around you and yourself in relation to the world. Let’s take a moment to think about how this process works operationally.

Imagine that you are standing outside, looking at a tree. What happens? The lenses in your eyes focus light photons onto the retinas. The photosensitive cells in your retinas respond by sending neural impulses to your brain. Your brain processes these signals and forms an image of the tree inside your mind.

So at this point, we’ve only addressed the mechanisms by which you perceive the tree. We have not addressed understanding what a tree is or considered changes over time. We are dealing with visual information only. There is nothing within this information that tells you what a tree actually is.

What makes the image of a tree in your minds click as an actual tree that exists right there in front of you? This is where mental models kick in and you start to think about the tree. The tree is actually a concept of something that exists in physical reality. The “tree concept” is a model. Understanding the concept of a tree requires more information than is available through sensory experience alone. It’s built on past experiences and knowledge.

A tree is a plant. It is a living thing that grows and changes appearance over time, often with the seasons. Trees have root systems. Trees use leaves for photosynthesis. Wood comes from trees. I can state these facts confidently because I have memories and knowledge of trees contained within my mental models. Mental models contain knowledge and help us create new knowledge.

Take a look at these images for a few moments and then think about what is happening inside your mind as you look at them.

My guess is that with each image you thought about what happens next. If so, you were actually simulating a mental model forward in time. The images don’t show you what happened next, but you can probably make a pretty good guess. This guess is the result of simulating a mental model of what is depicted. And, you can simulate different outcomes. With the last image, I like to simulate a parent rushing into the scene and scooping up the child before he gets burned.

When we think about the boy catching the ball, the blocks knocking one another over, worrying about the toddler getting burned, we are applying knowledge that we hold in our minds to help simulate a mental model of what is depicted in the image. This is all going on subconsciously, so we’re really not aware of it as it is happening.

Thinking About Systems

The human mind is very good at simulating mental models of our immediate physical reality. Things get harder when we start thinking about abstract systems.

A market is a good example of an abstract system. In a market system, price acts as a signal of aggregate demand for a commodity. You can’t “see” a market like you can “see” a tree in front of you. A market does not exists in a particular physical location. A market is an abstract concept that exists in the collective minds of all who participate in it. Even though a market does not exist physically, markets have an enormous impact on our lives nonetheless.

For example, when an economic recession hits, people tend to get worried about the economy and begin saving money instead of spending it. Retailers notice this trend, and in an effort to boost demand, begin dropping prices.

This can lead to price deflation because consumers see prices dropping rapidly and began delaying purchases as a result. The outcome of consumers simulating their mental model of the market informed their decision making: “I should wait to buy this because the price keeps dropping”.

This mental model paints a pretty picture for consumers over the short-term: low prices in a down economy. As the deflationary dynamics play out over the long term however, the picture becomes bleak. As prices spin downward, profits decline and businesses are forced to layoff workers or close up shop entirely. As unemployment increases, consumers perception of the stability of the economy decreases and they spend even less.

Economists and policy makers use sophisticated computer models to help them understand markets. On the other hand, consumers use simple mental models when making purchasing decisions. The more sophisticated models inform policy makers of the long term consequences of consumers cutting spending, so they react by trying to jump-start spending with stimulus programs.

Often, it is hard for us to define the optimal boundaries for a mental model. We tend to have a narrow focus and act on short-term dynamics within our mental models. For example, in the model above, our understanding changes when we expand the boundaries to include profits and layoffs.

However, we are not generally very good at mentally simulating complex systems with inter-dependencies, lots of variables, and delays.

Mental Simulation

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 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 stove-top 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. But we have to begin with a mental model!

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 system dynamics modeling software. 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. This is why technology can be such a useful tool and accelerate our learning.

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.

Modeling Dynamic Systems

Dynamic systems are notoriously difficult to understand. A survey of opinions and policy proposals concerning the economy, health-care reform in the US, and climate change will yield a myriad of mental models about how these systems work, and what action we can take to improve them. These systems are hard to understand because they challenge both our mental capacity to model and simulate them. Systems problems are characterized by non-linearity, delays, and competing feedback loops—all of which are challenging phenomenon to understand.

As a result of the complexity that is inherent within dynamic systems, you’ll often find a lot of debate and mistrust of proposals to change them. Think about how complex issues are presented in newspapers, blogs, on television and the workplace. Typically, projections concerning what will happen, if we adopt one proposal or another, will be presented with little explanation of how the proposal will change the system’s behavior. People will have difficulty engaging in meaningful discussion about how to actually solve problems without any common ground for understanding the dynamics of the system.

Understanding that we perceive the world through mental models, and knowing their limitations, is a good first step for overcoming the complexities we face in our modern lives. Education is key to understanding systems. We must teach children how to think more systemically, and how to use technology to increase their understanding, for them to use empathy in their decision making as global citizens.

Originally published at blog.iseesystems.com.