If You Can Apply All You Read, You’re Simply Not Reading Enough
And you’ll make very needless, costly mistakes.
A piece of common advice hammers on applying what you read. As the advice goes, don’t read another book or article until you apply the lessons of the last one.
The theory is that you’ll learn more by applying what you’ve read than picking up the next book or article.
Which is all fine and wonderful. Only that, even for very practical advice, not every practical information you read must be practised by you.
In fact, I’ll go as far as saying if you can apply everything you read, then you’re simply not reading enough.
As a rule of thumb, you’ll only be able to practice maybe 5% or less of what you read, if you read enough. That’s about one in every twenty practical idea or information you consume that make it to the application stage.
What about the other 95%? What’s their point? They make the other 5% possible.
The truth is, if you’re consuming enough, you simply can’t act on every information or idea you get. You simply don’t have enough time, energy, focus to pursue every single idea.
But should that stop you from consuming them? No, not necessarily. Why? Because the 95% per cent that doesn’t get acted upon helps you assess which 5% deserve your limited resources of time and energy.
The 95% is there as fodder to make a reasoned, informed and wise decision on which 5% of idea or information deserves going all-in on.
There’s great wisdom in “just get your hands dirty, try stuff and fail a lot.” Yet, while the virtue of failing a lot, failing forward etc has been trumpeted lately, failure itself has not ceased to hurt.
When your best effort blows up in your face, the raw emotions can be so real, so impactful it might preclude any further try, especially if you’ve invested significant amount of time, money etc.
Also, whereas learning by trial and error is great, it isn’t the best kind of learning there is. Learning by throwing things to the wall and seeing what sticks can be very costly — sometimes too costly if what you were throwing at the wall is really valuable and you’re now running out of stock.
That’s why learning from the experience of others, whenever possible, is better. I say whenever possible, because there are instances where directly experiencing something is the only way to learn it.
The Secretary Problem
So, let’s say you want to achieve a particular result — say, buy a house, pursue a career etc — that you need to research about, should you read a lot about it, or just jump in straight away and get your hands dirty?
You can guess either extreme, just reading without taking action, or merely acting without doing the required research can prove costly. Which suggests there’s probably, then, a middle ground, an optimal point that balances research and action; that balances looking and leaping.
This optimal point is suggested by an algorithm that comes from a mathematical problem called the secretary problem,
This problem imagines you’re trying to hire a secretary. You can interview each applicant, and after each person, you must decide immediately whether to hire the person or look at the next. No going back.
The question is, what process should you use to pick the best secretary, given that you don’t know what they will be like until you interview them?
It turns out there’s a mathematically optimal way to handle this problem. You start out by rejecting the first third of applicants. It doesn’t matter how good they are, you just reject them and move on to the next.
Then, after getting through this phase, you enter a new, ‘choosing’ phase where you pick the first applicant who is better than all the other applicants that you’ve seen before.
With this algorithm, you will select the best possible secretary roughly 40% of the time, regardless of whether there are a million applicants or ten.
The most important implication of this algorithm is not its accuracy but the underlying premise that the optimum that gives you the best result when you want to achieve a particular result there combines both looking and leaping.
The fact of decision-making and getting results is, look all you want without leaping and you won’t achieve anything tangible, leap all you want without looking and you’ll likely make very costly avoidable mistakes.
Optimal Algorithm of Looking And Leaping
This balance is what the algorithm secretary problem describes. It’s a split between looking and leaping.
Looking, when you lack enough experience to know what to choose, what course of action to take, and leaping when you have enough information to make a reasoned choice and run with it.
The most instructive part of the algorithm that proposes an optimal solution to the secretary problem is that its divided neatly into two phases. A phase where you look, search and research, then switching after a while to the other phase of leaping: making a decision and running with it.
When does Looking become Leaping?
The algorithm offers the optimal point of ~40% to switch from looking to leaping but real life is not as neat.
That’s why the greatest lesson of the algorithm presented by the secretary problem is that optimal decision will involve two phases: one phase where you look, followed by the other phase where you leap.
But the problem remains: at what point do you switch from one to another?
Let’s say you want to decide on buying a house, choosing a mate or career, or any of the significant life or career decisions? How do you avoid analysis paralysis that looking often harbour or the costly mistakes that blind leaping often harbour?
Based on the lesson of the secretary problem algorithm, there are two tactics that help:
1. Ask if your problem is a lack of information?
Do you lack information? Would more information help you make a better decision?
If so, you’re best served to go through a ‘looking’ phase where you simply just explore your options without making any real commitment yet. In this phase, you just experimentally try out different options without committing to any of them. Spend some time in different jobs before picking a career. Spend some time with different groups of people before finding a tribe.
What if you have the opposite problem? You have a lot of information, but can’t make a choice?
Stop the analysis paralysis. Pick something and start working on it. Make a choice and commit to it. Commit to one of the many options you have for a minimum period of time and evaluate afterwards.
2. Set a deadline for action
Another application of the algorithm is to set a deadline after which you go from looking to leaping. More research will more likely help you pick the best option, just like if you interview more applicants for a job, you’ll be much more likely to find the ideal candidate than if you hire the first person through the door.
However, more research can easily devolve into analysis paralysis where you spend so much time “thinking” about and “researching” options, that you never actually do anything about it.
Therefore, a way to counteract this is to set a deadline beyond which you start to take action, regardless of the information you have or have not.
To Wrap Up
When making important decisions, there are two costly extremes you want to avoid: reading/researching without taking action, or merely acting without doing the required research can prove costly.
The optimal point is one that balances research and action; that balances looking and leaping.