The One Virtue Every Polymath is Required to Embrace

Research suggests it can foster peace

Image by DALL-E

March 2025.

I was anticipating a response from Simon Squibb, a popular and rising entrepreneur known for his approach of asking people, “What’s Your Dream?” He was informing his followers that he was about to release his serious take on a Diary of a CEO podcast episode discussing how common people should respond to economic challenges.

Simon gave a taster of his response to the first 5 seconds of the episode:

“This guy’s wrong”

“This clip is EXTREMELY dangerous!”

Days later, Simon posts an extended response unlike the clip - he starts by appreciating Gary Stevenson (the trader) and his content on the economy. But Simon gives a different kind of criticism to Gary’s remarks on the podcast, and this was the most striking:

“The problem is it’s [taxing the rich] is too simple.”

Daniel Priestley, the other person on the episode, even followed on by mentioning:

“I’m sceptical about simple answers to complex problems.”

I started thinking about how we approach these complex problems, what they involve, and how humans yearn to solve them. But most importantly:

Are we, as a society, truly embracing complexity in the age of social media and AI?

This essay is a new style I’m experimenting with - writing like a philosopher. This fundamentally involves giving clarity on research and arguments, and finding counterarguments where it sees fit. After being inspired by ParkNotes (highly recommend you check him out for philosophy), I thought I might give it a go, because pretty much all polymaths select philosophy as one of their treasured pursuits. Please feel free to give feedback on this amateur attempt 😉

Reductionism Disguised as Simplicity

You and I want simplicity.

Imagine the world being devoid of simplicity. Buying a coffee, for example where I’m writing now, will have to go through more than one step towards fulfilment. Opening the door will take tiresome steps.

All these simple problems take unnecessary investment towards solutions, a compounding detriment to the limited daily energy of humans. However, thanks to various innovations, the chances of this hypothetical scenario occurring are fortunately slim to none. We have achieved simplicity to solve daily minuscule problems like opening a door or buying a coffee.

But if the problem is more intricate and have a greater effect size like solving the economic crisis, then simple solutions may not be a wise approach. Our desire for simplicity is eternal and a relaxing vision for the future. You may have heard of the mental model Occam’s Razor, where the simplest solution is typically the correct solution. The blog, Farnum Street, expands on how simplicity is achieved by “thoughtful reduction.”

“Everything should be made as simple as possible, but not simpler.” — Einstein

After a bit of reflection, on Twitter, I wrote: “You don’t fully understand something until you can simplify it. But if all you do is simplify it, you don’t understand it.”

The complexity of “but not simpler” is where we struggle.

This is where our brain struggles and tries to avoid work. The struggle is where the real learning comes from.

We are pattern-matching creatures — once we find a solution that works, we close our minds to alternative ideas.

A key element of simplicity and understanding is the thoughtful reduction of the unnecessary.

Referencing the design scientist, John Maeda, and his book The Laws of Simplicity, it provides a three-part framework of simplicity by thoughtful reduction:

1. SHRINK size - to lower expectations.

2. HIDE features - as you see fit.

3. EMBODY quality - look for real quality.

Let’s see where this blog is coming from:

The goal of simplicity is to eliminate what is cumbersome for humans to process and embody - i.e. building cognitive load. We want to create the most compact version, provide the autonomy to use extra features if needed, and obsess over quality (producing significant results with fewer resources). The result is using less cognitive load. If you’re a productivity enthusiast, you may find that this intersects with Cal Newport’s Slow Productivity philosophy - doing fewer things at once and obsessing over quality.

This is essentially a thoughtful reductionist approach:

When in doubt, just remove. But be careful of what you remove.⁠

Now, can this address complexity, especially in issues like the economy?

It has potential and can be a great start. But here’s where it falls short:

Any kind of reductionist approach has the risk of building the wrong kind of cognitive load in terms of feature and information processing. It considers information as isolated parts that are added together to create the complex whole, and by the process of elimination, some components are discarded to create a simpler whole.

Go back to the Einstein quote.

By equating treating the whole as a sum of parts and simplifying by elimination, alternative perspectives to complex issues can be disregarded and may not be a wise approach to engage actively.

Complexity involves more integrating than isolating…

And even has a science of its own to be inspired by.

The Science of Complexity

It’s striking to think that a value like complexity would have its own field of research with a physical and mathematical taste.

According to the Santa Fe Institute, the leading research institution on this topic, it describes complexity as “convoluted exhibitions of the adaptive world — from cells to societies,” and “one that we can, in many senses, perceive and measure directly.”

It is an exploration of systems - how the components dynamically interact with one another to exhibit mysterious meanings that are becoming increasingly difficult to decipher over time.

All that we know - the Internet, cities, economies, and social issues - have inherent systems that innovate over time.

Although the science of complexity is extensive, three crucial concepts are particularly relevant to this essay about embracing complexity.

1. Chaos theory

2. Nonlinearity

3. Emergence

Let the chaos commence (no pun intended hahaha!)

And it isn’t what you think it is.

It doesn’t refer to the entropy and disorder that we associate it with, but rather the “underlying interconnectedness existing in random events.” It is a basic description of unpredictability and how it connects with our everyday lives.

Nonlinearity describes the output not directly proportional to the input, and where small changes can vastly change everything.

Emergence describes features of a system that do not originate from the parts on their own.

All three properties summarise how complex systems and problems perform - there is no linear interaction of the parts to provide the outcome. The outcome can be discovered or undiscovered, sensitive to change, and is more than just the sum of the parts.

Instead of isolated parts combined after a reductionist approach, the parts form connections with each other (nonlinearity), dependent on circumstances that we cannot always predict (chaos theory), producing additional meaning (emergence).

The essence of these properties lies in showing dynamics, how subtle aspects of life interact with issues over time.

To refer back to the economy example, where simple answers to complex problems may not be a good idea. In this case, the proposed solution by Gary was to “tax the rich,” but both Simon and Daniel were critical of this. Simon made a separate response, including this criticism of simply taxing the rich, mentioning that the affected people would move to another city like Dubai, a well-known tax haven. He suggests the alternative approach that these multi-millionaires would take to benefit them. To his credit, I appreciate him for making a shift from the reactionary video to a more understandable response. Now the whole economic inequality debate is way, way more complex than what it was being explained by these 3 people, but when you apply the 3 properties to an issue that matters, you can see the picture of a system, how it behaves, and how our solutions need that extra bit of creativity.

I want to let you know that I’m still learning from this complexity science, and planning to look into thinkers who commented on this, including Steven Johnson and Brian Klaas, both of whom have exciting books on this.

Can we be biased towards seeing complexity?

Now for the counterargument, as required for this philosophical take.

I’m basing this on another article from Farnum Street:

Complexity Bias: Why We Prefer Complicated to Simple.

“Life is really simple, but we insist on making it complicated.”

Confucius

This quote summarises the counterargument of treating most, if not all, as complex, or more properly put:

We find it easier to perceive something as a complex problem than a simple one.

To be honest, I’ve had a slight suspicion that we may literally complicate things in unnecessary circumstances, and in a more philosophical sense, whether we are responsible for the complexity problem (again another piece for another day).

The article references the nature of cognitive biases; mental shortcuts to reach a conclusive argument, and complexity bias is certainly no exception:

Complexity bias is interesting because the majority of cognitive biases occur in order to save mental energy. For example, confirmation bias enables us to avoid the effort associated with updating our beliefs. We stick to our existing opinions and ignore information that contradicts them. Availability bias is a means of avoiding the effort of considering everything we know about a topic. It may seem like the opposite is true, but complexity bias is, in fact, another cognitive shortcut. By opting for impenetrable solutions, we sidestep the need to understand. Of the fight-or-flight responses, complexity bias is the flight response. It is a means of turning away from a problem or concept and labeling it as too confusing. If you think something is harder than it is, you surrender your responsibility to understand it.

Know what the most energy-consuming organ in the body?

It’s the brain - consuming 20% of the nutrients at rest.

But one crucial function of the brain is that it is doing everything in its power to keep you alive.

This even applies to the most cognitively demanding of circumstances, hence why cognitive biases exist to conserve mental energy.

We can see how it fits their argument of “turning away from a problem or concept and labeling it as too confusing.”

Another process that saves mental energy and keeps us alive - pattern recognition.

“Human beings are pattern-seeking animals. It’s part of our DNA. That’s why conspiracy theories and gods are so popular: we always look for the wider, bigger explanations for things.”

Adrian McKinty, The Cold Cold Ground

The Need for Pattern Recognition in the Information Age

This leads to the blog’s second argument, conspiracy theories.

Conspiracy theories are invariably far more complex than reality. Although education does reduce the chances of someone’s believing in conspiracy theories, one in five Americans with postgraduate degrees still hold conspiratorial beliefs.

These involve a substantial amount of pattern recognition to make sense of what’s going on - connecting events, people, and action to form narratives.

So we have two impulsive scenarios against “unnecessary” complexity:

1. Labelling something as complex and avoiding understanding.

2. Finding patterns to construct a complex narrative.

One is avoiding, the other is creating.

I highly recommend you read the article in full and see how it appreciates the need for complexity as well as not falling for easy prey.

Conclusion

If you read this far, thanks for sticking along!

This essay has been a bit of mess, so please forgive me.

The nature of complexity is one we really, really need to get our heads around. It isn’t just about knowing what is complex and what isn’t.

It’s about learning how to embrace it through our learning, thinking, and habits of deep exploration.

Both monomaths and polymaths have the power of embracing complexity in problems, and at the deepest level, acknowledge that it is necessary in everyday life. The challenge is being comfortable with the non-linearity, emergence, and chaos that comes with it. This is where our basic ability to think and our methods come in, and there is one term for this:

Cognitive Flexibility

To think in multiple perspectives.

To not be afraid to use curiosity.

To explore domains outside of your speciality.

And crucially:

To find the connections.

I believe that practising this new mindset can turn complexity into a weapon to revolutionise problem-solving.

Now we can’t just see every problem as complex, otherwise we can fall into the complexity bias as discussed. But from the two scenarios against complexity, we can leverage them to further embrace complexity healthily:

1. Find non-linearity, chaos, and emergence to identify a problem as complex, and be excited and blessed that you have the potential to understand and tackle the problem.

2. Take advantage of pattern recognition in two ways:

   1. Quick, intuitive pattern recognition for hypothesis constructing

   2. Deeper pattern recognition for hypothesis testing.

This is the future.

Are you willing to step in?