A simple way to build a formula for your life and work decisions
When we think of the butterfly effect in pop culture, it gives us the impression that any little decision we make now can have massive implications for the future. Thinking about this inherently makes us imagine how many millions of variables might be at play at any given moment of our lives.
We see a car accident on the way to work, and we think, “Wow, if my alarm didn’t go off late, and I didn’t struggle to find my keys, I would be 5 minutes early, and that might have been me!”
We create these little stories after an event has occurred. We wrongly assume that variables like what time our alarm goes off and whether we have a minor setback in our morning routine affect something significant.
In reality, it just doesn’t.
Sure there probably was a perfect combination of words, body language, and gestures that you could have displayed, that would have made you ace that job interview — but the data suggests it was 80% about your qualifications, how genuine you came across as, and your cultural fit within the company.
There may be a million variables out there that dictate the occurrence of any single event, so understandably this gets out of hand when we’re trying to make ‘correct’ predictions or decisions.
So what do we end up doing?
We use our imperfect intuition. Because we’re not statistical masterminds, and we’re not prepared to sample countless data points and use multiple linear regression to predict things.
Fortunately for us, we don’t need to rely on intuition all the time, and we certainly don’t need to apply statistical models to everything. In fact, we can use second-grade math to predict things for us.
Robyn Dawes, an American psychologist, reveals in his famous article “The Robust Beauty of Improper Linear Models in Decision Making” that complex formulas add little value to decision-making. A simple formula that combines several predictors that are equally weighted can predict the outcome all the same.
An example he uses is as simple as a formula to predict marital stability.
frequency of lovemaking minus frequency of quarrels
It’s entertaining as it is simple and as simple as it is likely true. Still don’t believe me? It’s too simple, right? Well, let’s look at organizations that have it in their best interest to predict the future.
There are many insurance companies out there, and they make their money by analyzing if things are going to go more right than wrong for each person. This risk comes to you in the form of insurance premiums. The higher the premium, the more likely something might go wrong.
But to get to this premium, in a world of millions and millions of variables, they really don’t ask you much, and they really don’t have a ton of data points. Why is that?
I had travel insurance on my recent backpacking trip, and the questions they asked me were something along the lines of;
- The dates of travel
- Age of traveler
- If I’m doing winter sports
- If I have electronics worth over $1500
- Pre-existing medical conditions
This company is very profitable, as a lot of insurance companies are. And yet, for something like travel that seems to have millions of unknown variables and a thousand and one ways things could go wrong, they asked me all of 6 questions before giving me insurance. The reason is simple.
You can extrapolate or predict almost anything if you find 5 or 6 significant but uncorrelated variables, weigh them equally, and add them up to find a score.
Before you head off and lose all your money on your local stock exchange, you should know that advanced algorithms, complex models, and information availability play a huge role in predictive analysis in 2019. Some organizations make their money with tiny differences in arbitrage. So hold your metaphorical horses. 🐴
Because we’re not trying to conquer the world, we’re not competing with everyone; we’re trying to learn how to make a formula for recurring decisions in our life and work. We’re trying to conquer our own world and predict how things will play out. We’re creating our own little decision making unicorn. 🦄
Like I mentioned before, I have recently been traveling, for about 10 months actually. There were places I thoroughly enjoyed, and places that I felt were lacking. Picking places to go was becoming a chore because I had no idea if it would be a place that I would enjoy.
It seemed like others' recommendations were a hit or miss, and how popular a place was didn’t correlate to how much I enjoyed it. So in a moment of frustration, I decided to use Dawes’ work and a simple formula laid out by Nobel Prize winner Daniel Kahneman in Thinking Fast and Slow to help me.
Based on the work of clinical psychologist Paul Meehl, Kahneman mentioned a story that seemed very applicable to my travel destination problem and many day-to-day issues for that matter.
Kahneman, then a lieutenant in the Israeli military, was tasked with predicting soldiers' future success through an interview. With Meehl’s work in mind, he had decided on 6 traits to rank the soldiers on during the interview.
“I instructed the interviewers to go through 6 traits in a fixed sequence, rating each trait on a five-point scale before going to the next…The results made us happy. As Meehl’s book had suggested, the new interview procedure was a substantial improvement over the old one.”
With my travel frustrations and my new-found knowledge, I started writing.
I asked myself some questions and assembled 5–6 uncorrelated variables that I guessed my enjoyment hinged on. And just by considering what made certain places better and getting these variables out there, I learned a lot about myself in the process.
The uncorrelated and equally waited variables that were significant to me in deciding if I would enjoy a particular destination were as follows.
- If I’m staying in a social place
- How cheap the food, accommodation, and transport costs are
- If there’s a wide variety of activities I can do
- If the average traveler is younger
- Whether it’s peak season, offseason, or somewhere in the middle
Considering these 5 variables, ranking them out of 10 gave me what I called a Backpacker Score—all with some second-grade math.
The higher the score for a place, the more likely it would be “backpacker-friendly,” and I would like it.
I did this in retrospect, where I realized that I didn’t like certain places because I wasn’t staying in social environments; maybe there were families everywhere. Other places were just so expensive and restricted freedom of movement.
Some real examples were destination such as;
Bangkok: 10, 9, 8, 9, 10 — BP Score is 46 (9.2/10)
Budapest: 10, 8, 8, 9, 10 — BP Score is 45 (9/10)
Paris: 6, 4, 8, 5, 10 — BP Score is 33 (6.6/10)
It became effortless to do some research, score destinations, and then decide whether to go there or not. What to look for in terms of accommodation and activities, and much more.
And like we mentioned before, there are still many variables out there that I haven’t considered. Pollution, crime, quality of internet — but that doesn’t matter as much for me. And the beauty of it is that if it matters to you to the extent, you’d include it in your top 5–6 variables, you absolutely can. It’s entirely subjective.
It doesn’t stop at my travel example. You can do this for almost anything when it comes to decision making, assessing a body of work, or predicting a certain outcome.
- An ideal relationship
- An ideal job
- If a business venture is on its way to success
- If an article, illustration, or piece of creative work will gain popularity
The possibilities are endless, and the process is simple.
- Find a few previous instances where you’ve had to make a similar decision.
- Inspect and deconstruct them to find why they were good or bad.
- Make an educated guess as to what variables/predictors you can take from them.
- Assess new decisions using the ranking system mentioned above on 5–6 variables
- Assess the accuracy of new decisions and adjust variables accordingly for future decisions.
And you have now learned how to predict your future practically, make better decisions, and be right more often than not. All with a simple formula, looking at some smart people's work, and of course, some second-grade math.
I hope you enjoyed it,