Get Comfortable Starting With An Unfinished Map
(we don't reach our goals through planning, but movement)
Imagine yourself standing in a forest.
You've never been here before. It's the middle of the day, and you need to make it out before it gets dark.
What do you do?
Do you take out a piece of paper and start planning? Do you try detailing exactly where the paths lead, which landmarks you'll run into, what turns you’ll need to make 2 miles away from your current spot?
Absolutely not.
You'd pick a direction that seems most sensible to you, and you'd start walking. You need to get out ASAP. You don't have time to sit around and plan.
You might scribble out a crude map to document where you've been and update as you move, but you wouldn't spend more than 5 minutes on it, because you simply can't see what’s ahead of you until you get there.
This is pretty common sense. When we're faced with an urgent situation, our first instinct is to get moving.
This situation is exactly like the goal you’re trying to tackle.
Like the forest, the path to achieving your goal involves uncertainty. You know you need to get to the end, but you have no idea what it looks like - you haven't been there yet! But instead of moving, like you would in the woods, you spend all of your time making a map.
We draw maps so we don’t get lost, but no matter how many times we re-draw it, a plan isn’t movement.
Imagine if you just stood and stared at your map all day. You’d never expect to get to your destination. At some point, you have to start walking, even if it’s in the wrong direction (at first).
It Stems From Uncertainty
As humans, we crave certainty. We want a guarantee that if we travel down a path, it’s going to take us exactly where we want to be. We’ll only commit to that path if we know every step.
But that’s not how life works.
In the forest, we know that we cannot have certainty. We can’t anticipate every obstacle or bad outcome. So we just start moving.
If we were to wait until we see all the routes, we limit our learning and remain stuck in the middle of the trees.
Gradient Descent (nerd moment!)
As a data scientist by trade, I would be remiss not to reference at least one concept I learned from my classes. To that end - please bear with me here - I introduce the concept of gradient descent.
Gradient descent is an algorithm used in machine learning to minimize an equation.
In other words, we want to find our way down a hill (“descending the gradient”). Our goal is to get to the very bottom, but we can’t see the hill because it’s foggy.
Because of this uncertainty, we can’t plan our path directly, so we’re going to take small steps towards the direction that feels most downhill from where we’re currently standing. If we end up going in a direction that eventually leads uphill, that’s ok! We’ll redirect.
Through this process of taking small steps and adjusting, we reach the bottom of the hill.
What gradient descent doesn’t do is plan out the entire path, then start moving. To do so would be fruitless, because the algorithm knows it’s bound to make some mistake, and any path it maps would just have to be redrawn later. So it doesn’t waste energy excessively planning that path. It channels that energy into moving.
It’s the same for you.
Like the algorithm, you don’t know where the end point is. You know the general direction you want to head, but you don’t know every obstacle you could run into, or every turn you need to take.
You’ll need to learn as you go.
The algorithm wouldn’t work if it stayed in place. It collects data as it takes action, which informs its next action, and that back and forth process is how it reaches the end.
That’s how you need to approach your next goal, your project, or whatever you can’t see the clear path towards. Stop letting uncertainty prevent you from moving. Simply act, and collect data as you go.
“But Mia, isn’t the algorithm worried about making a mistake?”
No. In fact, the power of gradient descent is that it doesn’t shy away from mistakes.
Like your own path, gradient descent is not a linear process. It’s iterative. You change directions thousands of times along the way.
This is far better than trying to plan out an entire path, getting it wrong, and being so far off course by the time you get to the end. Mistakes are part of the process.
And if this fancy computer program can reach its goal through a thousand tiny rounds of trial and error, you can too.
My Dare For You:
Accept the uncertainty.
Just like if you were standing in the middle of a forest (or a very foggy hill), you need to accept that you cannot see the path to your final destination. Once you let your need for certainty go, you’ll be less likely to compensate through planning.
Draw a crude map (less than 5 minutes).
Briefly document where you are and then choose a direction. This is where you’re going to take a small step (like the gradient descent algorithm would).
Example:
My end goal is to pay my water bill by selling digital products. I don’t know which path to take to get there (start an Etsy store? Create a pdf? Design a website?), but I’m going to choose a direction to step towards.
The direction? I’ll create one printable pdf and post it on Etsy.
Take the step, and re-evaluate.
Update your map based on where you’ve now been. How did it go? Does it still feel like the right direction? What can you now see in this new position that you couldn’t before?
Example:
I put my first download on Etsy and realize customers don’t want to buy printable pdfs, but they are interested in online versions.
Adjust your direction if needed, and take another small step. Repeat.
Repeat and repeat. Until you’ve reached your destination.
Example:
Knowing that customers prefer strictly digital content, I’ll create a Notion template instead.
The truth is, you don’t need a perfect plan. You just need to start moving. Embrace the uncertainty. Take a step, re-evaluate, adjust your direction if you need to.
That's how you'd get out of the forest.
That's how the gradient descent algorithm finds the bottom of the hill.
And that's how you're going to achieve your next goal.
Good luck!
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