Mental models
I use the term “mental models” a lot, and so I figured – hey, maybe it’s time to do some semantic disambiguation and write down everything I learned so far about them?
When I say “mental model,” I don’t just mean a clean abstraction of “how a car works” or “our strategy” – even though these are indeed examples of mental models. Instead, I expand the definition, imagining something squishy and organic and rather hard to separate from our own selves. I tend to believe that our entire human experience exists as a massive interconnected network of mental models. As I mentioned before, my guess is that our brains are predictive devices. Without our awareness, they create and maintain that massive network of models. This network is then used to generate predictions about the environment around us. Some of these models indeed describe how cars work, but others also help me find my way in a dark room, solve a math problem, or prompt the name of emotion I am feeling in a given moment. Mental models are everything.
Our memories are manifestations of mental models. The difference between remembering self and experiencing self is in the process of incorporating our experiences into our mental models. What we remember is not our experiences. Instead, we recall the reference points of the environment in that vast network of models – and then we relive the moment within that network. Our memories are playing back a story with the setting and the cast of characters defined by our mental model.
This playback experience is not always like that black-and-white flashback moment in a movie. Sometimes it shows up as the annoying earworm song, or sweat on our palms in anticipation of a stressful moment, or just a sense of intuition. Mental models are diverse. They aren’t always visual or clothed in rational thought, or even conscious. They usually include sensory experiences, but most definitely, they contain feelings. Probably more accurately, feelings are how our mental models communicate. A “gut feeling” is a mental model at work. Feelings tell us whether the prediction produced by a mental model is positive (feels good) or negative (feel bad), so that’s the most important information to be encapsulated in the model. Sometimes these feelings are so nuanced and light that we don’t even recognize them as feelings – “I like this idea!” or “Hmm, this is weird, I am not sure I buy this” – and sometimes the feelings are touching-the-hot-plate visceral. Rational thinking is us learning how to spelunk the network of mental models to understand why we are feeling what we’re feeling.
One easy way to think of this network model as of a massive, parallel computer that is always running in the background, where we’re asleep or awake. There are always predictions being made and evaluated. Unlike computers, our models aren’t set structurally. As we grow up, our models evolve, not just by getting better, but also through the means by which models are created and organized. We can see this plainly by examining our memories. I may remember a painful experience from the past as a “terrible thing that happened to me” at first, and then, after living for a while, that “terrible thing” somehow transforms into “a profound learning moment.” How did that happen? The mental model didn’t sit still. The bits and pieces that comprised the context of the past experience have grown along with me, and shifted how I see my past experience.
We can also see that if my memory hasn’t changed over time, it’s probably worth examining. Large connected networks are notoriously prone to clustering. The seemingly kooky idea of the “whole self” is probably rooted in this notion that mental models are in need of gardening and deliberate examination. When I react to something in a seemingly childish way, it is not a stretch to consider: maybe the model I was relying on in that moment indeed remained unexamined since childhood? And if so, there’s probably a cluster within my network of mental models that still operates on the environment drawn by a three-year old’s crayon. This examination is a never-ending process. Our models are always inconsistent, sometimes a little, and sometimes a lot.
When I see a leader ungraciously lose their cool in a public setting, the thought that comes to mind is not whether their behavior is “right” or “wrong,” but rather that I’ve just been witness to a usually hidden, internal struggle of inconsistent mental models.
Our models never get simpler. I may discover a framing that opens up a new space in the previously constrained space, allowing me to find new perspectives. Others around us are at first simple placeholders in our models, eventually growing into complex models themselves, models that recurse, including complexity of how these others think of us and even perhaps how they might think we think of them (nested models!) Over time, the network of models grows ever-more complex and interconnected. At the same time, our models seamlessly change their dimensionality. Fallback fluidly influences the nuance of the model complexity, and thus – the predictions that come up. Fallback is a focusing function. If my body believes I am in crisis, it will rapidly flatten the model, turning a nuanced situation into a simple “just punch this guy in the face!” directive — often without me realizing what happened.
I am guessing that every organism has a kind of a mental model network within them. Even the simplest single-cell organisms contract when poked, which indicates that there’s a — very primitive, but still — a predictive model of environment somewhere on the inside. It is somewhat of a miracle to see that humans have learned to share mental models with such efficiency. For us, sharing the mental models is no longer limited to a few behaviors. We can speak, write, sing, and dance stories. Stories are our ways to connect with each other and share our models, extending already-complex networks way beyond the boundary of an individual mind. When we say “a story went viral,” we’re describing the awe-inspiring speed at which a mental model can be shared. Astoundingly, we have also learned to crystallize shareable mental models through this phenomenon we call technology. Because that’s what all of our numerous aids and tools and fancy gadgets are: the embodiments of our mental models.
This is what I mean when I say “mental models.” It may seem a bit useless to take such a broad view. After all, if I am just talking about leadership, engineering, or decision-making, it’s very tempting to stick to some narrower definition. Yet at the same time, it is usually the squishy bits of the model where the trickiest parts of making decisions, leading, or engineering reside. Ignoring them just feels like… well, an incomplete mental model.
🔗 https://glazkov.com/2022/03/20/mental-models/
A problem
To get a more solid grounding of the newly born decision-making framework, we need to understand what a problem is. Let’s begin with a definition. A problem is an imposition of our intention on a phenomenon.
I touched on this notion of intention a bit in one of the Jank in Teams pieces, but here’s a recap. Our mental models generate a massive array of predictions, and it appears that we prefer some of these predictions to others. The union of these predictions manifests as our intention. When we observe any phenomenon, we can’t help it but impose our intention on it. The less the predicted future state of the phenomenon aligns with our intention, the more of a problem it is.
For example, suppose I am growing a small garden in my backyard. I love plants and they are amazing, but if they aren’t the ones that I intended to grow on my plot, they are a problem. Similarly, If I am shown a video of a cute bunny eating a carrot, I would not see the events, documented by the videographer as a problem. Unless, that is, I am told that this video was just recorded in my garden. At this very instant, the fluffy animal becomes a harvest-destroying pest – and a problem.
I like this definition because it places problems in the realm of subjectivity. To become problems, phenomena need to be subject to a particular perspective. A phenomenon is a problem only if we believe it is a problem. Even world-scale, cataclysmic events like climate change are only a problem if our preferred future includes a thriving humanity and life as we know it. I also like how it incorporates intention and thus, a desire to impose our will on a phenomenon. When we decide that something is problematic, we reveal our preferences to its future state.
Framing problems as a byproduct of intentionality also allows us to play with the properties of intention to see how they shift the nature of a problem. Looking at the discussion of the definition above, I can name a couple of such properties: the strength of intention and the degree of alignment. Let’s draw – you know it! – a 2x2, a tool to represent the continuous spectrum of these property values as their extremities. The vertical axis will be the degree of alignment between the current state of the phenomenon and our intention imposed on it. The horizontal axis will represent the strength of our intention.
In the top-left quadrant, we’re facing a disaster. A combination of strong intention and a poor alignment means that we view the phenomenon as something pretty terrible and looming large. The presence of a strong intention tends to have this quality. The more important it is for a phenomenon to be in a certain state, the more urgent and pressing the problem will feel for us. Another way to think of strength of intention is how existential for us is the fulfillment of this intention. If I need my garden to survive through the winter, it being overrun by a horde of ravenous bunnies will definitely fit into this quadrant.
Moving clockwise, the alignment is still poor, but our intention is not that strong. This quadrant is a mess. This is where we definitely see that things could be better, but we keep not finding time on our schedule to deal with the situation. Problems in this quadrant can still feel large in scope, indicating that the predicted future state of the phenomenon is far apart from the state we intend it to have. It’s just that we don’t experience the same existential dread when we survey them. Using that same garden as an example, I might not like how I planted the carrots in meandering, halting curves, but that would be a mess rather than a disaster.
The bottom-right quadrant is full of quirks. The degree of intention misalignment is small, and the intention is weak. Quirks aren’t necessarily problems. They can even be a source of delightful reflection, like that one carrot that seems to stick out of the row, seemingly trying to escape its kin.
The final quadrant is the bread and butter of software engineers. The phenomenon’s state is nearly aligned with our intention, but the strength of our intention makes even a tiny misalignment a problem. This is the bug quadrant. Fixing bugs is a methodical process of addressing relatively small, but important problems within our code. After all, if the bug is large enough, it is no longer a bug, but a problem from the quadrant above – a disaster.
I do wonder about the point that our models never get simpler. I find that first principle's thinking is a good way to revisit your models and build them up to the complexity they need. In many ways collaboration is about simplification of mental models - so that we can share them. Maybe that is stories - shared mental models, or even "runs" of shared mental models. Hmm. Interesting.