Essays

The Making of a Self Educated Child Genius

At the age of 14, Taylor Wilson built a nuclear reactor in his parent’s basement and achieved nuclear fusion, becoming the 32nd person in history to do so, and the first person to do it at his age. He then went on to become proficient in at least 20 other fields of science and engineering, like biology, chemistry, physics, astronomy, etc.

He’s currently focusing on using nuclear science to solve problems in several fields. He’s building a new, safer (and cheaper) nuclear reactor, he’s invented the cheapest neutron detector designed to stop terrorists from smuggling dirty bombs, and in the field of nuclear medicine he designed a device which lowered the cost of cancer detection.

The really interesting part, is that he did all this through self education, deep passion and an insatiable curiosity. He found all the relevant materials online and exchanged emails with physicists, engineers and nuclear scientists. In fact, he credits the internet for enabling him to do this project in record time.

I stumbled upon Taylor in a podcast interview with Tom Bilyeu called Impact Theory where Taylor talks about how to managed to achieve all his accomplishments while still in high school.

In this essay, I’ll deconstruct Taylor’s methods for building a remarkable career. We’ll talk about:

How Taylor managed to escape the woeful fate of many child prodigies who never amount to anything in adulthood

How he found and nurtured his passion for nuclear science

How he got scientists and engineers from around the world to mentor him, teach him and later collaborate with him

And finally how he taught himself, his secret method for radical self education.

Wasted Child Prodigies

Taylor credits his obsessive passion, insatiable curiosity and unrelenting drive to achieve for his success. “That’s always been my personality” he says in the interview, “If I decided I wanted to do something, I was going to do it” but he’s careful about calling himself a genius.

In the interview, Taylor states that he doesn’t believe that the secret to success is pure aptitude: “aptitude is important but not that important” he says. What matters more is if you’ve taken the time to really learn about a subject.

You can have a very high aptitude for a field, like science or mathematics, yet be unable to achieve greatness In fact, his younger brother Joey scores higher than him in all the aptitude tests but has yet to find what he wants to use that aptitude for.

Sounds pretty reasonable if you think about it, but what if you wanted to replicate it?

How can you achieve what he’s achieved?

How can you have such an amazingly impactful career?

We need to delve a little deeper.

The first clue comes from a very unlikely source.

Eliezer Yudkowsky wrote a fascinating Harry Potter fan fiction book called HPMOR (Harry Potter and the Methods of Rationality) where Harry is a child prodigy and his step father is a professor. The book is very long, and very fascinating but this quote early in Chapter 6 gives us a key to Taylor’s success

“Harry had always been frightened of ending up as one of those child prodigies that never amounted to anything and spent the rest of their lives boasting about how far ahead they’d been at age ten […] because those other geniuses hadn’t gotten their hands on the one thing you absolutely needed to achieve greatness. They’d never found an important problem

Finding an Important Problem

By focusing on energy Taylor found a very important, highly impactful problem to study. By further focusing on nuclear energy, as opposed to other energy sources (like fossil fuels, wind, solar, etc) Taylor has also found a branch of science that has applications beyond energy (e.g. nuclear medicine, homeland security, etc.)

He realized that the amount of energy stored in 1g of Uranium was far denser than the energy stored in much larger amounts of fossil fuels. He also realized that energy is a powerful key to human progress because it touches everything in our lives, especially with the advent of global climate change. The nuclear reactor he’s building is safer, cheaper, easier to build and much smaller than the current reactors.

Ok we now have the first key to Taylor’s success. But is it enough?

In the podcast, Taylor mentions the word “passion” in every other sentence. Passion is one of those esoteric subjects that everyone talks about how important it is, but very few have any idea how to find it or nurture it. Even those who claim to be passionate about something have no clue.

So how do you find something you’re passionate about it? Or how do you become passionate about something?

Deconstructing the Secret Code of Passion

When I was about 9-10 years old, I remember very vividly finding my mom’s high school books on Physics in the attic of my grandparent’s home. These were books meant for 16 and 17 year-olds but I had no problem understanding them.

I remember being fascinated and later obsessed with electricity and electromagnetism and I build several electromagnets with stuff I found lying around (batteries, wires, old toys, etc.) You could say I was passionate about physics.

Taylor recalls a similar experience with nuclear science “I was passionate about it, I was obsessed with this stuff” he says.

Passion is the emotion you feel when you’re deeply motivated to pursue something and you’re fully immersed in the experience. But how does it work?

We get our second clue in professor Cal Newport’s 2012 book So Good They Can’t Ignore You

In the book, Prof. Newport sets forth a very compelling argument that passion is not important, what’s important is to what he calls “the craftsman mindset.”

He argues that passion comes after you’ve mastered something and that the most important thing to look for in a job is autonomy. This is based on a scientific theory of intrinsic motivation known as SDT (Self Determination Theory)

Later in the podcast, Taylor says:

“You have to stick with something long enough to get a good enough grip on it, before you find that passion”

This seems to validate Prof. Newport’s thesis that you need mastery before you feel passion.

SDT explains a lot about what we consider to be the feeling of passion, especially that unrelenting motivation to keep digging into something. There is also a second theory that adds an important missing nuance to SDT. This is the theory of Flow states which has been studied extensively by Mihaly Csikszentmihalyi.

Let’s dive in and see how.

You can find thousands of web articles, academic journals and books on intrinsic motivation, but a far more accessible discussion is detailed in the book Drive: The Surprising Truth About What Motivates Us by Dan Pink

In this book Dan Pink details the three elements that make up our drive to pursue anything:

  • Autonomy: The desire to be self-directed in everything we do
  • Mastery: The desire to get better at something
  • Purpose: The desire to do something important, meaningful and impactful

Let’s now look at the second theory.

In positive psychology, a flow state, also known colloquially as being in the zone, is the mental state of operation in which a person performing an activity is fully immersed in a feeling of energized focus, full involvement, and enjoyment in the process of the activity. In essence, flow is characterized by the complete absorption in what one does, and a resulting loss in one’s sense of space and time.

https://en.wikipedia.org/wiki/Flow_(psychology)
File:Challenge vs skill.svg
Source: https://upload.wikimedia.org/wikipedia/commons/f/f6/Challenge_vs_skill.svg

The above chart explains quite clearly how we achieve a flow state. It’s essentially the perfect combination of a challenging task and our skill level

If a task is too challenging and our skill level is low, we feel Anxiety (if we need to complete the task say for work or for school)

If a task is too easy and our skill level is high, we feel Boredom

However, if the level of difficulty is perfectly matched to our skill level, we experience a state of Flow, characterized by feeling “in the zone”, full immersed in the task and where we lose all sense of space and time.

There were moments during his project where Taylor would be so immersed in what he was doing that he’d forget to eat. It’s very easy to explain these feelings as “passion” however having a more scientific explanation allows us to replicate it for ourselves.

We now have the formula for passion:

Passion = Autonomy + Mastery + Purpose + Flow

So how does this apply to Taylor Wilson?

First, Taylor was interested in rocket science. In a special segment for ABC News there’s a video of him as a 9 year old explaining rockets during his science project (at the 2:24 mark)

Interest in rockets led to interest about energy sources that power rockets and though that’s usually solar energy, sometimes nuclear batteries are used. Upon landing on energy as a field of study, I believe that Taylor found his main Purpose in life.

Because his project to build a nuclear reactor in the basement was a self-directed learning project (SDLP) it encompassed both Autonomy and Mastery. Powered by his insatiable curiosity, Taylor embarked on a journey of self education, and as he progressed through his project, he learned more. The more he learned, the better his skills got.

This way both the level of skill and the level of the challenge were both perfectly balanced setting the conditions for him to experience a state of Flow which he interprets as passion.

I’ll write more about how to set up self-directed learning projects (SDLPs) for yourself in the future.

How to Acquire the Knowledge

We’ve just deconstructed how Taylor became passionate about nuclear science; how did he then go about acquiring the knowledge required to become a nuclear scientist?

Taylor started with a project. He decided he would build a nuclear reactor, saw that it was possible to do and decided to acquire the knowledge as he went about finding the materials and schematics of the reactor.

“First, getting the overarching themes of a new field and second getting into the personalities of the people who made the discoveries, what were their motivations, what questions did they seek to answer, what was their training/background. A lot of Nobel prize winners were right at the edge of their fields or made their discoveries outside of their fields.”

“Even something as complicated as quantum mechanics, which is very unintuitive can be easily understood if you study the motivations of the people who came up with the theory. You can then see the logical stepping from concept to concept. You get to see guys who weren’t really that much more intelligent from you but who were able to make the logical leaps from one concept to the next.”

Then Taylor mentions his core belief and main technique he followed to teach himself everything he could about science and engineering. He states:

“I really believe the best way to learn about a topic is to study its history.

This is a very interesting approach to learning; one I’ve never seen discussed anywhere. The main reason it’s successful is because our minds tend to really like information when it’s presented in narrative form. Before the advent of the written word and the invention of the printing press, the only way knowledge was passed down from the elders was in the form of stories.

There isn’t much space here to get into the depths of narrative rationality and the power of stories, but suffice it to say that organizing your knowledge acquisition around the stories of the main characters in any field makes for far more effective and interesting learning experience than reading a dry academic textbook.

Now that he’s very established in his field, Taylor continues to learn about new subjects but the strategy has changed. Using his field as an anchor, he then dives into adjacent fields which connect tangentially to nuclear science.

He tries to reach out to people who are working in fields that are radically different from his own and he tries to see if something from his field can help them in their work or vice versa.

I used to be quite intimidated of people who knew things that I didn’t know. My ego got in the way and made me feel stupid instead of allowing me to learn.

Here’s Taylor’s attitude in his own words:

“Being in a room with someone who has knowledge I don’t have is the best feeling in the world. Being able to absorb their knowledge is the best feeling in the world”

This also allows him to get unstuck in situations when he feels that his thinking is in a rut.

Achieving Credibility and Finding Mentors

At some point in your learning project you might need to ask for help from someone more knowledgeable. This is the most difficult part of the project.

When you go to college, the idea is that you have access to the brightest minds in any field either directly or through connections. When you’re doing a self-directed learning project however this is nearly impossible to do. So how did Taylor do it?

Not only did he do something that’s extremely rare, namely building a working fusion reactor, but he did it at the very young age of 14. The level of impressiveness is off the charts. Can anyone do impressive things? If so, how?

The clue for this question comes from another article by Prof. Cal Newport. In a post from 2008 he introduces the Failed Simulation Effect

The Failed Simulation Effect states that when presented with anyone’s accomplishments, you tend to mentally try and simulate the path they took to get there. If you can easily simulate this path no matter how arduous or hard it’s not that much of a mystery.

If however you find that you cannot simulate their path, you feel a sense of wonder and curiosity which you interpret as impressiveness.

Now if you hear that a 14 year old kid built a working nuclear reactor in his parent’s basement, and you run that through a simulator you can’t help but wonder how he did it, while feeling very impressed.

This is very important if you want other people to help you. The article above goes into some details on how to achieve this, so I will not cover that here.

Now that Taylor has earned national recognition for his work, he has the necessary credibility to easily find mentors and collaborators.

In conclusion, I believe that it’s even easier these days to use the myriad tools available online to design a self directed learning curriculum that suits you and that can help you accelerate your career without the need to go to college or get additional degrees. In fact your future depends on it.

Why unstructured learning is the only way to build a lucrative career quickly

Photo by Wes Hicks on Unsplash

Data science as a term was coined back in 2008 by DJ Patil and Leff Hammerbacher. In 2012, D.J. Patil and Thomas Davenport called “data scientist” the sexiest job of the 21st century in an article they wrote for HBR

This new dream job, combined the coding skills of a programmer with a statistician’s ability to build mathematical models in order to create massive value for companies. The case of LinkedIn’s “People you may know” provides a striking example of the power of data science.

The speed of innovation far outpaces college curriculum’s ability to keep up with it

There were no college courses on data science or machine learning when that article came out, there were not even any online courses. You could of course take programming courses in college, and you could take statistics courses, but there was no curriculum for a degree combining the two and nothing that was designed to teach data science specifically.

It was a new emerging field and if you wanted to get into it you had to study obscure books on data mining, and knowledge discovery (which is what data science used to be called) You had to be willing to deal with a lot of ambiguity, uncertainty, lack of feedback and you had to figure a lot of things out by yourself. This was around the time that I got into data science. I wrote about my experience here

Although I didn’t want to become a data scientist, I prefer to be a more of a generalist, the knowledge I learned through my project still allows me to talk intelligently to data scientists as well as explain things to non-data scientists in a way they can understand.

Companies care more about your experience than your degrees

My company was hiring for a senior business data analyst position recently. This career has become increasingly more lucrative as businesses continue to accumulate data and the need for analysts to extract insights out of that data is skyrocketing. As such, many colleges have begun offering masters degrees in business data analytics as a way to fill the skill gap. The success of these degrees however is still lacking.

The hiring managers reviewed hundreds of applicants for the position, many of them recent graduates with one of these new masters degrees in the field of analytics. But, they didn’t hire any of them. Instead they hired a junior analyst (who by the way had an unrelated degree) because he had experience doing data analytics in a business setting and solving real world problems.

This may be a single data point but I bet If a company had a choice between a fresh graduate with a masters degree but no experience in the field and a person without a related degree but with a few years experience, they will hire the person with experience 9 times out of 10. Experience is a more direct (and thus better) way to demonstrate that you can create value.

You can only get that experience through unstructured learning and solving real world problems in a the appropriate context. That’s why whenever people ask me for advice on how to advance their careers in a new, unfamiliar area, I always suggest they take on a project in that area as part of their job, or even on their own time.

Many lucrative careers require skills you cannot learn in college

YouTube millionaires is a regular feature of many business magazines and websites (see here, here, here or here)

These people managed to leverage a new platform to build a following and a steady stream of income. Talk about a lucrative career!

How did they manage to build careers without a college education? They figured it out on their own, through trial and error. There’s no college curriculum on how to become a millionaire YouTuber, how to become an Instagram Influencer or a Social Media marketer.

Maybe becoming a top Instagram Influencer is not what you want in your life, maybe you don’t want to be a YouTube millionaire, but if you think about how these people learned the skills that garnered them all this attention (and all the money), you can’t help but realize the power of unstructured learning.

Not only did the YouTubers and Instagramers build a following, made a lot of money, but they also learned incredibly valuable evergreen skills; skills that will serve them for life. Because if tomorrow one of these platforms disappears, the skills will still be valuable and can easily be transferred onto new and as of yet undiscovered platforms.

They learned skills in building a following, making full use of a the tools that platforms afforded them, skills in creating valuable content, ancillary skills such as video and audio editing, presenting information in interesting ways, speaking in front of a camera or microphone, connecting with others, etc.

More importantly they learned how to learn in unstructured ways, the most powerful, evergreen skill of all.

If there’s a college curriculum, someone younger, hungrier and cheaper than you can be trained to do your job

I love this tweet by Naval Ravikant:

“Specific knowledge is knowledge that you cannot be trained for. If society can train you, it can train someone else, and replace you.”

This quote provides the most compelling reason for unstructured learning.

Because this “specific knowledge” that Naval is referring to cannot be trained for, you have to learn it yourself, by solving real world problems through projects. College simply cannot cover everything, despite what the term “university” seems to imply. Some knowledge has to be acquired through self learning.

And of course, if there’s a curriculum, then the knowledge has already been commoditized, which means that your job can be shipped “offshore” or be given to younger, hungrier and cheaper workers. In order to prevent that, your best bet is to build a rare and valuable skill stack which cannot easily be replaced, and the only way to do that is through unstructured learning.

How I taught myself data science in 90 days

Photo by Avel Chuklanov on Unsplash

I first encountered data science (aka data mining, machine leaning, artificial intelligence, etc) back in 2014. I was a newly minted analyst trying to expand my expertise beyond the basics and my company had just hired its first data scientist.

I got curious about what she did, so after talking with her for a while, I was sobered to realize that I would need two more years of education and a graduate degree in statistics if I wanted to properly to call myself a “data scientist.”

The only problem was that after my experience getting an MBA, I had sworn off any kind of academic style learning. While I made a number of connections and friends, the degree itself had not taught me anything useful. In fact, I have learned more about business by reading books in my own time than I ever did in college.

If I was going to learn data science quickly, I was going to learn it on my own. I also realized that with the exception of a few select fields, companies don’t care much about your degrees, they care about what you can do for them, what kinds of value you can provide, and in many cases, you can provide incredible value without needing another college degree.

What I really wanted was to understand enough key concepts of data science that I could apply them in the real world to produce something useful.

So I thought “hey, I’m a smart guy, I can probably figure this out.”

Yes, I was quite conceited back then.

The challenge for me was that I absolutely hated statistics, and when I took the class in college I found it incredibly hard to understand, nonetheless I began my quest.

I started by looking for a few books that taught the key concepts of data mining with a bend towards applicability. They were quite hard to find and the few books I did find were dull and boring. But, I did my best to get through them and I managed to learn a few theoretical concepts.

Next, I searched for videos on YouTube. There weren’t many, but what I did find was very interesting. There were some videos that demonstrated the use of a free visual data mining tool called RapidMiner.

The author had many examples with the code and data easily downloadable so I could get them and try them on my own. It was exactly what I needed because it allowed me to see the concepts I was learning applied in the real world.

JIC vs JIT learning

I believe that the best way to learn is to solve a problem that you care deeply about or are strongly motivated to solve. It could be a personal problem, or a professional one and it should allow you to apply theoretical concepts to a concrete problem.

But why?

Almost all colleges apply the same framework towards learning. I call it Just In Case (JIC) learning.

You start by learning all the fundamental concepts first, you then apply these concepts to artificial problems in the book (which you don’t really care about) that are very clearly laid out and where there’s usually only one correct solution.

You then continue to learn more concepts that build on the fundamental ones you learned previously, you continue to apply them to even more artificial problems you don’t care about, in the hopes that some day you’ll need this knowledge to solve problems.

This theory of learning believes that knowledge builds on top of itself like a pyramid. In fact, many text books are set up this way. Fundamental concepts first, more specific knowledge later.

In real life, however, you start with a very specific problem you’re trying to solve, you search for the solution and once you find it, you can generalize that solution to other similar problems. I call that Just In Time (JIT) learning. You only learn things just before you actually need them, which maximizes both usefulness and retention.

That’s why I chose a specific project at work to apply data science on, that would both benefit the company, and teach me how to do data science in practice. As I struggled with the project, I learned another dark secret nobody tells you about in college.

The problems you solve during your classes are artificially set up to be easily gradable not to maximize learning.

For example in pretty much all data science books and courses, the data you work with has already been selected, cleaned and staged in order to make it easy for you to build the model. In real life it’s never that easy.

The hardest problem I struggled with during my project was figuring out what data to choose for my model in such a way that made sense, and I had no one to ask. No articles or online courses to follow. I happen to find the answer in an obscure book written by a practitioner vs theoretical books written by professors.

That’s how I managed to teach myself data science in just three months without having to go back to school and get a statistics degree. As a side benefit, I now understand and enjoy statistics. It makes sense to me because I have seen it applied in the real world.

Years later in my new job, I repeated the same process to learn another aspect of data science/ machine learning this time by doing a hackathon project at work. It cemented the lessons I had learned and taught me even more valuable skills.

It is because of these, and other similar experiences that I believe that unstructured learning is the key to an amazing career. Many valuable things can only be learned the hard way through experience not by going to school.

Obliquity – Why some problems cannot be solved directly

On March 30, 2017 a large portion of the Interstate 85 (I85) highway in Atlanta, GA collapsed after a massive fire that raged underneath it.

Being a key piece of infrastructure that carries thousands of cars every day, experts predicted severe traffic congestion and delays. Yet, none of this materialized. People simply changes their behavior; in fact Atlanta’s public transportation (MARTA) reported a 25% spike in ridership following the incident.

On the other hand, adding a lane to an existing highway usually makes congestion worse. This is known as Braess’s Paradox. Traffic congestion is one of those complex problems that simply cannot be solved with a direct solution of building more roads.

Have there been times when you tried to tackle a problem head on and failed? Some problems are best tackled indirectly. Why?

In order to understand why this happens we have to first understand a few things about complex systems. As explained in my previous article, there are three types of systems categorized by the level of constraint on both the system and the agents operating in it.

While ordered systems are transparent (simple systems are transparent to everyone and complicated systems are transparent to experts) complex systems seem transparent but are in fact opaque. We simply cannot know everything that happens in these systems. We think we know, but we usually have a very limited understanding of the complexity inherent in these domains.

John Kay calls this phenomenon Obliquity and explains it in detail in his book by the same title. He writes:

The environment—social, commercial, natural—in which we operate changes over time and as we interact with it. Our knowledge of that complex environment is necessarily piecemeal and imperfect.

The human mind is programmed to look for patterns and to seek causes, and this approach is often valuable. But that programming leads us to see patterns in random events and to attribute intentions where none existed. We believe we observe directness in obliquity

Because of this, direct solutions almost never work as intended and usually have unforeseen consequences or adverse effects, like the increase in congestion when more roads are open. 

A good example of this is the so called cobra effect which is based on an anecdote about a bounty program created in British colonial India where the government tried to fix the problem of venomous cobras by offering a bounty for every dead cobra. 

This worked initially but then people started breeding cobras for income. When the government found out, the scrapped the program causing the breeders to release their worthless cobras making the problem worse.

It is because of this that I believe the first step in tackling any problem is to get a sense for the type of environment we’re dealing with. 

If the environment or domain is simple, the solution should be self evident. We simply sense what’s happening, we categorize, prioritize and solve the problem. 

If the domain or system is complicated, like a car’s engine or a software system. we hire experts to analyze the issue, get a sense for what the problem is and solve it.

 If we’re dealing with a complex domain or environment, we cannot solve the problem by analyzing. We have to adopt a more experimental, discovery based approach. We have to try things and see how they work; we have to probe, sense and then respond accordingly.

You assess the situation quickly, form a hypothesis, design and carry out a small-scale, safe-to fail experiment and analyze the results, Then you reassess your hypothesis and figure out if you’ve solved the problem. Other times you can leverage what’s already there, what you sense and see that’s already working.

Why plans are useless but planning indispensable

“Plans are worthless, but planning is everything”
-Dwight D. Eisenhower

“Failure to plan is planning to fail”
-Benjamin Franklin

We all have the fantasy of the perfect plan that goes without a hitch. Heist movies, like Ocean’s Eleven (Twelve and Thirteen), The Italian Job, The Bank Job, etc. all fuel that fantasy that you can be a mastermind capable of seeing all the angles, predicting everyone’s behavior several moves ahead, getting the timing right down to the second and achieving your goal exactly as you planned. In the real world however this is rarely the case. Why?

We live in a complex, interconnected world. Every action we take can cause ripples of unpredictability in the system. Complex systems are by their very nature unpredictable because there are no universal laws that govern them. Even if every agent in the system were to have simple rules by which they make decisions, the overall system behavior that emerges is unpredictable.

To account for all the possible scenarios quickly exceeds the capacity of even the most powerful of today’s computers. Just look at the weather patterns. Despite all the advances in computational power and simulation capabilities, we still can only forecast the weather with any level of accuracy a few days in advance. The complex behavior of the water molecules, the air temperature, atmospheric pressure, initial conditions and other factors make it nearly impossible to analyze and predict what will happen.

There are however systems which are highly predictable even if they seem very complex. A computer program’s behavior for example is very predictable (most of the time anyway) A car’s various systems: the engine, transmission, brakes, electrical systems, etc. are also very predictable even if they are interconnected and interdependent.

So what’s the difference?

David Snowden’s Cynefin framework (pronounced kun-ev-in) recognizes three types of systems: Ordered, Complex and Chaotic. The difference between them is the level of constraint in each system.

Ordered systems are highly constrained and as such their behavior is very deterministic and predictable. You can easily determine cause and effect and the patterns you find are very likely to repeat in the future. Ordered systems are further divided into Simple and Complicated. A highly structured business process for example (like getting a loan) is a Simple system. It’s highly constrained and relatively easy to fix or optimize. Cause and effect relationships are clearly visible and you can predict with very high accuracy what will happen.

A car is an example of a Complicated system. It’s still Ordered because it’s highly constrained (there’s little to no variation beyond what’s been specified by the system designer) but the level of detail in the design makes it much harder to understand and notice cause and effect relationships. This is why you need highly trained professionals (experts) to analyze the system and figure out cause and effect relationships.

Complex systems on the other hand are only partially constrained. Complexity science is still actively being studied and discovered but we do know a few things that can help us understand how these systems work. Complex systems are made up of agents that interact with each other and with the system based on their own rules and strategies and the constraints imposed by the system.

In the example above we saw that cars were Ordered systems because of the high level of constraint in every aspect of their design; traffic on the other hand is only partially constrained and as such it’s a Complex system. There are rules in the form of laws and guidelines such as speed limits, traffic signs, traffic lights, highways, ramps, paved roads, direction of driving, etc. but these rules do not fully constrain driving. You can choose to dive fast or slow, change lanes frequently or not at all, slow down or speed up, turn left or right, etc.

This creates unpredictable emergent patterns such as accidents, traffic jams, traffic congestion or sparsity, etc. On top of that, the traffic patterns from moment to moment, from day to day are completely novel and unique. There’s no way to know for sure when an accident will occur or when the traffic will become congested. Even though you may know exactly why an accident happened, it doesn’t help you fully predict future accidents.

Chaotic systems are highly unconstrained. Imagine for a second that one day none of the rules of driving applied. You could drive in the middle of the road if you wanted, drive backwards, go through red lights and stop signs, drive on the opposite side of the road, cut through lanes at will, make sudden u-turns, break and accelerate as you wished, etc. What would happen? Complete and utter chaos. It would be impossible to predict anything.

Side Note: Temporarily removing constrains in a system is an excellent way to unclog bureaucratic gridlock in an organization and spur innovation. Dave Snowden calls this “shallow dive into chaos” but that’s a topic for another day.

So how does this relate to planning?

Most planning is done under the assumption of Ordered systems. We assume that the future is predictable from past events so making plans is easy. Planning comes naturally to us as our brains function like cybernetic (goal seeking) systems. We set a goal and immediately our brain provides ways to achieve it.

Now if the system you’re dealing with is highly constrained, these plans are very likely to succeed. For example if you wanted to buy a house you’d need a bank loan and since getting a loan is an Ordered system, given certain criteria, you can predict with very high accuracy if you will succeed or fail.

If we’re dealing with a complex system however, or a chaotic system, we would be unable to account for all the possible future scenarios and contingencies and our plans would be at best incomplete. Before the advent of GPS and turn-by-turn navigation systems with up the the minute traffic data, it was impossible to plan a route down to the minute and be very confident you would arrive at a particular time.

So the reason why plans are useless is that more often than not they are incomplete and don’t account for all the possible contingencies in the complexity of today’s systems.

Why then is planning indispensable?

The process of planning gets us to think through many of the possible futures and scenarios that can unfold and help us be better prepared if any of those futures scenarios were to happen by creating contingencies. Of course we can’t cover every single scenario and we need to be agile and capable of course correction. The measure of true agility is the ability to ditch your plans halfway through when the situation has changed and made your plans obsolete even if the sunk cost might be high.

Always have multiple theories for explaining and understanding things

When trying to understand or explain something that’s happening, like a certain behavior pattern in your friends or significant other or a trend in fashion, technology, etc, it helps to have more than one hypothesis (theory), (even better if it’s more than two) and assign each one a probability of being right.

Then as you get more evidence for any one of your multiple theories, you adjust the probabilities of what the correct explanation could be. You might also run multiple experiments to cover all your theories. This will lead you to a more accurate understanding of people or the world around you which then leads to more accurate forecasts, better decisions, more confidence and decreased levels of stress.

I believe that there’s always more than one way to explain things, there’s always more than one theory that fits a situation and I’m not attached to any one of them at first. This doesn’t mean that I like being wrong, in fact this means that I want to be even more accurate so I want to cover all my bases. As I gather more data, I eventually converge on a single theory, while still keeping an open mind that it could still change in the future.

As humans we’re addicted to being right, it’s a compulsion that threatens to derail our friendships and our relationships. We want our intuition to be the correct one. It’s very easy to get emotionally attached to certain explanations that benefit us, make us feel smarter, more confident and more proud, or that ensure that we keep our jobs.

When you have multiple competing theories for why something is happening you keep yourself open to possibility, and as a result you understand the world better. You might not look as smart or as confident or as self assured as the person with a single theory, but more often than not, you will end up having more accurate predictions and be more confident than them in the end.

The Dangers of Optimization

“We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil” -Donald Knuth

In the field of Computer Science (CS) optimization is the process of changing the algorithm (the logic) of a program in order to improve its efficiency (i.e. make it run faster or consume fewer resources.) In order to do this, certain assumptions need to be put in place and my argument is that these assumptions make optimization dangerous and not just in the context of programming.

First of all in order to optimize a process you need to understand it very well. You need to know how it behaves in different circumstances or under various boundary conditions. If we were to look at an organically (necessity) grown business process that we want to optimize, it’s usually necessary to see where the inefficiencies or bottlenecks are in the process and remove them.

Second, when you optimize you may need to restrict boundaries, make certain assumptions, use special cases, tricks and complex trade-offs which will achieve the required result but at the expense of potentially over-complicating a process or over-specializing it. This causes a loss of agility in the long run or over-adaptation (as may be the case in over-optimized diets).

Let’s use a simple example from manufacturing to explain this. Let’s assume that you have a retool-able machine that produces widgets at 95% defect rate. This means that 5 out of every 100 widgets are defective. When you’re looking to optimize the defect rate, you’re looking to produce fewer defective widgets without slowing down the machine or the manufacturing process. Suppose also that you know the process of making this widget very well, since you make millions of them every year.

Since you know the process well you know that there are just a few ways to optimize the defect rate, you can for example utilize the machine better by redesigning the overall process or you can install much more specialized machines that make just this one widget. Now you have an optimized defect rate but it has come at the expense of over-specialization and a huge loss of flexibility or agility. Imagine what can happen after you’ve replaced your retoolable machines with specialized machines and the market changes to where it no longer needs those widgets. You’re practically out of business.

The danger is that not all processes are fully known. Even in a precise field such as computer science there’s always some level of unknown, certain unexpected conditions or special circumstances where the program will fail. If you were to optimize the process without knowing all these cases you run the risk of having an incorrect program that no longer solves the original problem.

Nowhere is this more prevalent than in digital marketing. First let’s get one obvious thing out of the way. SEO or search engine optimization is not really optimization per se. You’re not optimizing your website to work better or faster, you’re adapting it to fit the search engine’s constraints.

The whole idea behind the concept of targeting is really about optimization. When you’re trying to target a certain segment of your audience, you’re trying to optimize the marketing dollars so you increase the efficiency. After all why would you want to spend money on leads that will not respond to your offer?

There is nothing wrong with targeting (although Roy H. Williams aka The Wizard of Ads would beg to differ) The problems arise when you start to over-optimize while assuming that you really understand your audience and what offers they actually respond to. The truth is you really don’t, so all this optimization will really hurt sales. There are many other things in a business you can optimize and improve, such as your close ratio.

A good example of this is a recent story I read in Roy H. William’s Monday Morning Memo (a highly recommended business newsletter by the way) The story is about two lawyers who take different approaches to marketing. One believes in targeting, and converts about 10% of inquiries while the other believes in casting a wider net via radio advertising and ends up converting 60% of inquiries proving once again that marketing is primarily about the messaging and the offer.