Paul Graham, co-founder of Y Combinator, states that the most damaging thing we learned in school was learning to get good grades. In his essay, The Lesson to Unlearn, he explains that tests have become something to hack instead of measuring what they’re supposed to, how much we have learned. This encourages students to concern themselves with what could possibly turn up as a test question and trains them into thinking that the way to win is by hacking bad tests.
Consequently, we can start to believe that this is how the world works. We look for tricks on how and what to do instead of taking in new information and simple lessons that are right in front of us. It wasn’t until I attended Lambda School that I understood how entrenched this was in me. This realization prompted me to reflect on my educational experiences as a whole. In doing so, I discovered how conditioned I was to memorize facts for grades, the antidote to active learning.
“The end goal is not to find flaws in the things you’re told, but to find the new ideas that had been concealed by the broken ones.” —Paul Graham
You Don’t Know What You Can’t See
Within the domain of data science, models are used to approximate relationships for prediction and classification. Mental models aren’t drastically different. They are used to help us prepare for a future event by offering different lenses in which to view the world. When applied from different disciplines, mental models enhance the resolution of how the world works.
There are three specific insights I acquired from studying data science that have impacted me in ways that transcend the discipline I discovered them in. They have been useful for helping me undo my conditioning to hack bad tests and led me to expanding my mental toolbox with thinking concepts like mental models. To highlight these insights, I’ve selected a mental model to illustrate each below.
1. Questions are more important than answers.
Asking questions helps us distill problems into their most essential pieces or first principles. First principles as a form of thinking prove to be valuable for problem solving and decision making. Questions enable us to explore depth and dimension, and by using them to deconstruct a problem, we’re able to expose the roots that create a foundation for applying other models.
Mental model: First Principles Thinking
2. “All models are wrong, but some are useful.” — George Box
A model is not reality just like a map is not the territory. For example, let’s say you’re visiting Paris, and you refer to a map to help you navigate around the city. The map you’re holding is representative of Paris at a particular moment in time. Therefore, the map excludes any details of the city as it changes. This idea is essential to keep in mind as we use abstraction and reduction to help guide us through the complexity of our world. Understanding what models and maps tell us allows us to better question and update them as we go.
Mental model: The Map is not the Territory
3. You become what you measure.
Your output is an expression of the thinking and strategy going on behind the scenes. You are falling prey to proxies and predetermined targets if you don’t know what you’re trying to optimize for. What are the metrics that will help you get to where you want to go? It’s easier to illuminate a path to what you want when you know what’s going to help get you there. By asking if what you are doing now is going to get you the results you want in the future, you can weigh the effects of different decisions. Considering “the effects of the effects” is second-order thinking in action.
Mental model: Second-Order Thinking
Each of the ideas above are takeaways that have helped me morph learning into just as much of an artistic and personal endeavor as an intellectual and professional one. As a result, I’ve collided with other cool, useful topics like Personal Knowledge Management, a practice for capturing ideas and insights, and sharing in public, which involves releasing your work for feedback before it’s finished; they are how and why this essay is in front of you now.
Win by Doing Good Work not by Hacking Bad Tests
I thought my success was dependent on how well I was able to hack bad tests until I realized it didn’t have to be. In fact, as the future continues to enable non-routine cognitive and creative work, the reward for being proficient at existing problems over the discovery of new ones is less and less. The ability to look around, see what’s in front of you, and imagine different ways of doing things becomes more valuable than the next tactic or trick. Your relationship with learning is the differentiator.
As my relationship with learning has morphed, so to, has my mindset, worldview, and approach to solving problems. With this, how I think about the world becomes a decision I make every day. By empowering myself to be an active producer of knowledge and ideas, I now know that learning is the job of a lifetime and a path to possibility.
Taylor Bickell 