Roger Martin observed in The Opposable Mind that our stances inform our tools, and our tools inform our experiences. For those who take a stance of continuous improvement and risk management, choosing simulation as a tool is a good course of action. This post unpacks that statement.


What are you? You decide your stance. Are you a scientist? Are you an artist? Are you a software engineer? An entrepreneur? You get to decide.


Your stance has a powerful impact on which tools you pick up. A scientist picks up the scientific method. An artist may pick up a paint brush. A software engineer pick up python. An entrepreneur may choose the lean canvas and the pitch deck.


The tools you use impact the experiences you have. Because nature doesn’t give up her secrets willingly, the scientist experiences long hours and a lot of frustration, and maybe, elation. The artist may experience acceptance and praise, and often, rejection. The software engineer experiences mounting complexity. The entrepreneur only ever experiences two emotions and nothing in between: sheer elation and sheer terror.

Continuous Improvement and Risk Management

I choose to believe that things can be made to be better and together, we can conquer entropy. Nature is knowable. It can managed to create better outcomes for people and by extension, the biological systems that keep us all alive. This is stance isn’t original. Its orthodox classical liberal thought with a w-shaped ecological risk perspective. The whole system relies on optimism.

The heuristic “if it ain’t broke, don’t fix it” can be experienced in one of three ways. It can signal complacency, pessimism, stasis and decline. It can signal satisfaction and with a prioritization – that a 0.0001% improvement in a small process is a lemon that just isn’t worth the squeeze, and there are way larger fish to fry. And it can signal that it is just too risky to try to make it better.

Let’s assume the best and argue that there’s an unacceptable risk involved. As a Canadian, I think of risk as a dark force that needs to be managed and mitigated. It lurks everywhere. The worst place for it to lurk is in the dark corners of the mind, stabbing at the light of optimism from the shadows. On the other side, there’s dissatisfaction with the status quo and the sense that equilibrium must be broken. Often, situations have to become very bad before dissatisfaction reaches the threshold hold for change.

Simulation As A Tool

Mister Rogers once said that play is the work of childhood, and imagination was a core part of the job. It can be the work of clever adults, too.

When we look at the equation, Y = mx + b, those in the west read it left to right, “Y is m times x, plus b”. The equal sign is read as the word is. We’re taught from a very early age to execute this equation mechanically, by rote. Given m, x, and b, Y becomes known, mechanically. It’s in part why, when mathematics is introduced to children, it feels oppressive. There’s no room for imagination. The outcome is pre-destined. You just have to execute the operations to discover what is already there.

Sometimes x gets a special friend above it, an exponent, which makes Y so much more than it is, and non-linear. And then we’re taught to optimize for Y, given some constraint imposed on m. I’m always curious as to why people think they can introduce more Y’s into the equation, as though that somehow they can evade the constraints placed on m. If you don’t like what the equation is predicting about the future, change what’s to the right of Y.

Optimism springs from the infinity to the right of the b. I really like to imagine is what’s beyond the b. What about z? Doesn’t z get to come and play? And t? What of time? And all the other variables we assign a special m and a special x? And what about all our operator friends? The subtraction operator. The divisor. Maybe even the root? What else could Y become? Could it become a system of equations? What’s out there that explains Y, and could Y become something much more?

Simulation, as a tool, offers those who dare the experience of imagining more outcomes and more what if’s. Moreover, it offers a pathway for narrow machine intelligence to help you think wider and explore further. The bridge between the two worlds is you. And you should be aware of it as you’re exploring a space. A narrow machine intelligence can be trained to understand the Bellman Equation, and how to satisfy it. And your preferences are trained using it all the time. No, really. You’re often subject to it.

Ever notice how sometimes a sales person will show you things that you don’t like, or things that are outside of your price range, to start? They’re really just executing a Bellman search. They may not be aware that that is what it’s called. But nature has taught them that if they want to make a sale, they have to execute such an algorithm.

Simulation can help us understand what we want by showing us what we don’t want. It can teach us the consequences or benefits of choice. If you price your ego at zero, the cost of failure within a simulation is zero. What narrow machine intelligence enables is a strengthening of the tool.

Which is why I argue that for those who take a stance of continuous improvement and risk management, choosing simulation as a tool is a good course of action. It can prepare your mind to be open to more possibilities.