Connecting the Dots: Astrology and Data Science
Maybe astrology is mumbo-jumbo or maybe it’s aligning with the spiritual energy of the universe. But does it even matter?
Astrology is the study of patterns and relationships — of planets in motion, our birth chart, synastry with others, the make-up of elements — and using that knowledge as a tool to find meaning. Data science is the field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data.
“I’m a Sagittarius sun, Taurus moon, and a Libra rising… and what is your birth chart?” I would usually say to friends or acquaintances when getting to know someone better. If they aren’t immediately turned off by Astrology, I’ll go into more detail. If they are even more curious, I’ll evaluate their planetary Natal Chart.
My close friends may think I bring up astrology way too often and take it way to seriously… While part of me agrees with them, through the last 4 years I’ve found Astrology to be surprisingly useful.
Here’s how I use Astrology in my daily life:
- Interpret different aspects of mine and peoples’ behavior, natural tendencies, and personalities
- Foster mindfulness, self-awarenasess, and reflection through periodic horoscope readings
- Predict/anticipate energy levels & moods
- Connect me to others and the world
Some think it’s a little silly, even nonsensical. I’ve had people express anger towards me for being someone who utilizes pseudo-science like Astrology for insight and advice. Even I initially thought astrology was absurd.
Astrology argues this: that at the precise moment you separate from the womb and manifest into your own living body, the arrangement of the planetary bodies around you correlate with the aspects of your self. Sounds far out. Hocus-pocus even!
But once I started looking at astrology through the lens of data science, my entire perspective changed. Here’s how.
We are all (kinda) data scientists
Data science is an emerging hot career field, synthesizing the power of statistics and modern computer science. For instance, IBM predicts the “demand for data scientists will soar 28% by 2020” with an average pay of $105,000. There’s a lot of talk about data science without any understanding of what it really is. You might not see it at first, but we as human beings are wired to use principles of data science every day.
Data science: statistics on steroids
In order to understand data science, you need to know a little of basic statistics. Actually, we are all practicing statisticians! According to Leo Breiman’s “Statistical Modeling: The Two Cultures”, statistics start with data which is information you measure, observe, and feel.
Every second you are alive, your body is processing an enormous amount of data — what you see, feel your emotions, the position of your body, etc — and making sense of it!
Let’s take — for example — birth. When you are born under the celestial heavens and gain consciousness you enter a world that you had no understanding of. You have zero to little data in your hard drive so to speak. So once you breathe your first breath and see your first ray of light you begin to absorb the data around you. Picture yourself back to when you were a newborn baby. Forced into a brand new world with no familiarity with all the data you are taking in. Naturally, you are overwhelmed by the amount of data you are processing so you cry. Mom likely gave you your first meal to shut you up. You’ve been satisfied with mom’s milk. Hours pass, you take a nap, and you wake up start to cry again until Mom feeds you.
A couple more times of crying then feeding, you begin to notice that when you cry, someone like your mom is likely to cater to you — with mom’s touch, attention, and most importantly, food. Eventually, you come to understand this process: that when you input the action of crying, you expect the outcome of being fed by mom.
Congratulations! You have created your very first statistical model!
A statistical model is an algorithmic way to predict what happens in your life. Just like with our first model, our mind has been continuously analyzing the data we receive every day. With each data point, the mind creates more associations between inputs and what we observe. Eventually, we create models that explain the phenomena we experience or the goals we want to achieve. The baby has a model for call for food from its mom: by crying.
Data science takes the predictive modeling ability and creativity of the human brain to make connections and exponentiates the scale and speed through modern computer science and emerging types of digital data.
Changing your model means changing your worldview
We use our own personal models every day to predictably accomplish anything and everything! Here are some examples: going to school, preparing for a test or job, figuring out how much you will spend this weekend, or wooing a potential partner. We have our own personal models on how to likely guarantee success in all of your endeavors. How I prepare for a test is different than yours. Your model for getting dates on Tinder is different than mine as a gay man. Our models for predictably accomplishing what we want are personal and individualized models on our unique life experiences and identities.
But sometimes we don’t get the results we predict from our models. Can’t get an A on any test? Can’t get a date on Tinder? Maybe its not the other people or ourselves who are the problem.
Ideally, we consciously and subconsciously refine and change our models as we evaluate the results of our actions. If our actions produce unfavorable results that don’t agree with the way we see the world, we can change the model and the behavior to produce different results. However, this only happens if we allow our ego to get hurt by admitting we were using the wrong model.
“Insanity is doing the same thing over and over again and expecting different results.” — Rita Mae Brown
It’s not easy, but we have to be receptive to the data and make changes accordingly. The aggregate of all of your models of the world and how they interact manifest as your unique worldview. We use our worldview to do the following:
- Make predictions of what will happen in the world. For example, you may predict which team will win your favorite sport. Or you might predict whether or not mom and dad will get mad at you if you come home past 11 pm or before 11 pm.
- Extract meaning and understanding about the world. We use our worldview to explain why some relationships work while others do not. We justify why homelessness is allowed exists or why people are ultimately good are evil. We observe and interpret data to either challenge or underscore our predominant worldview.
With our predominant worldview in the face of new data, we choose to accept it as truth to challenge our assumptions and adjust our worldview or ignore it and remain blissfully ignorant.
The Trade-Off Between Predictability and Inference, Bias and Variance
“all models are wrong, but some are useful” — George Box
Every day we are using our particular worldview to navigate our world. Ultimately, our human understanding of the world will always be a simplification of reality. In other words, what you know and how you know it is ultimately wrong.
You may have a basic understanding of how gravity works: you know enough about it not to jump from high heights. But do you understand the science behind law of gravity: why matter attracts other matter?
However much we learn and understand, our models of anything will be by human-nature, incomplete and wrong.
Despite being all wrong, the models within our worldview help us survive. Even though you do not fully understand gravity — for example — you still know enough not to jump off a cliff because your model will predict that you have a high possibility of dying. Our wrong models are still useful.
Our models can vary by 1) predictive power and 2) ease of interpretation. The best models are usually both highly predictive and understandable. In other words, our worldview correctly explains the phenomena we observe, and we have an intuitive way of understanding the relationship between the input variables and the results.
Despite being useful for accomplishing our goals throughout the day there is inherently always a degree of bias and imperfection in our models: they only capture a portion of the truth. As we refine our models in life and across generations we get closer and closer to the real truth.
If we listen to the data, our worldview can become more refined, nuanced, and closer to the truth.
Modeling human behavior: Myers Briggs, Sorting Hats, True Colors, Strengths-finders or Astrology?!?
We also have models for predicting and explaining our behavior and personality. Some companies use the models in their leadership development and communication pieces of training. Others casually use models to predict love compatibility or describe a person’s personality.
If you’ve ever taken a psychology course, you might be familiar with the Myers-Briggs Personality Test, which categorizes you with a four-letter acronym (ie. ENTJ, INFP, etc.). Most of these models are 1) somewhat predictive, and 2) easy to understand. The tests ask you to input responses to a variety of behavioral questions and an algorithm calculates a specific personality profile according to the model you use.
I’ve listed my results for several famous behavioral models below:
Valuing utility over understanding
Most behavioral models tend to value simple, understandable models. When I get my results, they really make sense with how I answered the questions. Taking the Myers-Briggs for example, I got extroverted because I answered in favor of extroversion in the test.
Something like Astrology, however, is completely non-intuitive — the inputs for the model are the exact time and location that you are born and the output is your astrological natal chart with a framework that assess’s your personality and provides periodic predictions for how you may act or interact with others.
All of these predictions are determined by which zodiac each planet was residing in at your time of birth. Each planet corresponds to a different facet of your self. My natal chart for example predicts that my outward self is adventurous and zealous for truth while my emotional self can be stubborn and jealous.
This interpretation is derived from the fact that the Sun and Moon were residing in the Sagittarius and Taurus in the sky when I was born.
So between Myers-Briggs’s and Astrology or any other personality model, which one do you use? The answer lies in your personal values and the assumption that all models are wrong, but some are useful.
For me, I decide to use whichever model seems the most useful for the situation. At work, I rely on the DISC or Strengthfinders to help navigate my relationship with others. Generally, however I like Astrology for reflection, assessment, and spiritual connection with myself, others, and the world.
So what now?
At the end of the day, I am not here to argue that astrology is right any more than Myers-Briggs, Strengthfinders, etc. After all — every model is wrong — so we have to be critical of the models we are using to interpret the world.
I am just one 24-year-old human of billions of humans on this planet. It would be arrogant to think with 100% certainty that astrology is infallible. I do not have the scientific explanation for why humanity communes and connects with the heavenly bodies.
But scientific worldview transforms all the time. From a flat to a spherical earth, or explanation of evolution, and even now, discovering the importance of the bacterial microbiome that controls and affects our mood, behavior health. So who knows!
Maybe in the future scientists will discover electromagnetic links between us and the other planets in the same way that we sync with the moon’s lunar cycles or the sun’s circadian rhythm. Maybe we will uncover the reason we feel connected and alone, important yet insignificant when we look up to the heavens.
But for now, I’ll continue to use whatever tools I have in my tool-belt to try and navigate this world. And if the tools stop working, if I’m no longer receiving the results I expect, I’ll choose another tool, another model, another worldview. I’ll live each day with the understanding that in an instant my whole perspective can shift. I’ll look up to the heavens, thank God I’m alive and give gratitude to the Universe for another day to discover more truth.
My name’s Kevin. I’m trying to find more ways to progress interesting ideas forward and writing is a new way I’m doing that.
I like to think about how we can design living, learning, and working communities that optimize how people learn and actualize their potential. Let’s talk on Twitter.
References
- Breiman, Leo. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author). Statist. Sci. 16 (2001), no. 3, 199 — 231. doi:10.1214/ss/1009213726. https://projecteuclid.org/euclid.ss/1009213726
- Columbus, Louis. “IBM Predicts Demand For Data Scientists Will Soar 28% By 2020.” Forbes, Forbes Magazine, 14 May 2017, www.forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data-scientists-will-soar-28-by-2020/.
- Stephens-Davidowitz, Seth, and Steven Pinker. Everybody Lies: Big Data, New Data, and What the Internet Reveals about Who We Really Are. Dey St./William Morrow, 2018.
- Nicholas, Chani. “Horoscopes.” Chani Nicholas, 2 June 2019, chaninicholas.com/horoscopes/.
- Stewart, Benjamin, director. Kymatica. Kymatica, YouTube , youtu.be/14Bn3uYqaXA.
