Learning

Volatility, Stability & Contradictions

To maintain an accurate or effective grasp of reality one must undergo a continuous cycle of interaction with the environment geared to assessing its constant changes.

If you’re reading this, then you’re interested in the concept of volatility. Furthermore, I’d wager that you’re here because you want to find some sort of solution to the “problem” of volatility in your own life.

I don’t blame you for feeling this way. Every passing day seems to generate more complexity, rapid change and uncertainty than at any other point in human history.

You aren’t wrong — that’s exactly what’s happening. But below the surface of this chaotic ocean of volatility lurks useful knowledge.

Knowledge that will make you uncomfortable in many cases. Knowledge that will make you realize you’ve only taken a few steps down the rabbit hole.

But it will be knowledge nonetheless. If you’re the sort of exploratory person who enjoys endless journeys and moments of insight that make you feel like you’ve just unplugged yourself from one reality and jerked your nervous system into a new one…well, you’re in the right place, my friend.

Volatility

The world we live in is intensely volatile. You know I don’t need to go down the standard laundry list of examples, because you’re living in it. Every day it feels like a new order is being shaped in one domain, while the old ways and traditions are somehow holding back progress in others.

Once again, you’re not wrong — this is exactly what’s happening.

Here’s the issue with such high levels of volatility: you weren’t designed to cope with it. Small bites of volatility are fine, you and every other human comes equipped with enough adaptive capacity to deal with some change.

We know how to respond to unexpected weather, changes in social relations, patterns of behavior in our rivals, and so on. These were all part of the deal for early humans who possessed the same brain you possess at this very moment.

What you and I were not designed to cope with were global pandemics, flash crashes, the overnight disappearance of long-standing institutions, and all the other seemingly insane changes that occur in our globalized world.

Defining Volatility

You might also be wondering what I mean when I say volatility. This is a fair line of inquiry, especially since the only people who purposefully use the word volatility on a regular basis are traders (usually options traders, but traders as a group tend to be interested in it).

For this crowd, volatility carries a specific definition (courtesy of Investopedia):

A statistical measure of the dispersion of returns for a given security or market index.

In more straightforward language: how much the price of a security deviates from some expected average price.

But what if we want to boil volatility down to its essence, right on down to the fundamental level so that it can be applied to domains beyond finance?

You’re luck! We can, in fact, do such a thing by restating the definition of volatility in more general terms:

Volatility is a high rate of change within a given environment.

Notice that this definition is free from value judgements — we’re not saying that volatility is either good or bad, it just is. We’ve reframed it from a phenomenon with specific characteristics, to a sort of measurement.

All you’re saying when you say “volatile” is “high rate of change” — nothing more, nothing less.

How You Feel

Volatility, like any other conceptual model, doesn’t care about how change makes you feel; such a concept is based entirely on our own subjective experience of volatility.

The reason I bring this up is important to focus on: most people speak about volatility in strictly negative terms. You can see it whenever the term “volatility” pops up in either the media or casual conversation.

A couple of quick examples highlights what I mean:

“Greg has a volatile personality” is not a compliment. What is implied by the use of “volatile” in this context is that Greg is unreliable, untrustworthy and unpredictable. And we hate people who fall into those buckets.
“The markets have been volatile” is, for most people with money in the markets, not a good thing. The average investor wants straightforward, predictable returns. Volatility ensures that uncertainty about those returns rules the day.

Stability

What is it that we want instead? Stability, aka safety, aka comfort.

We can define this complimentary concept of stability within our new framework of change measurement:

Stability is low rate of change in a given environment.

Stability makes us feel all warm and tingly on the inside. When we know what to expect and have removed uncertainty from a situation, we breathe easier.

It’s a natural human instinct. Knowing what comes next feeds into our intrinsic need to predict the future — and stability in an environment gives us that.

Much like volatility, the idea of stability has many affective properties built into our descriptions of it. Let’s see how stability makes our previous examples look:

“Greg has a stable personality” is definitely a compliment. Greg’s the kind of guy you know will show up when he says he will, will complete the tasks he’s been assigned, and is a generally trustworthy guy.
“The markets have been stable” is a positive for most investors. Stability (or at least our concept of it) implies a healthy, dependable economy that produces high, predictable returns.

It’s clear that we treat the idea of stability as a net positive nearly every time we utilize it. Stability translates to predictability, and predictability is what we’re all programmed to seek. Therefore stability is viewed as in intrinsically positive concept.

Contradictions

The contradictions that we generate as people are what I’m most interested in and what I spend much of my time reading and writing about. If you’re trying to understand the world you live in, you should do the same.

Contradictions are everywhere, and recognizing where they pop up is a critical skill you must develop — especially if you want to understand volatility.

This is where things start to get interesting, and now we take our first steps into the maze of complexity. Here it will start to become clear to you just how deep the rabbit hole goes.

For one, both high and low levels of volatility can be good or bad, sometimes both simultaneously.

Consider the subtexts of these statements:

“Our marriage is stable.”
“I have a stable career.”
“My schedule is stable.”

What’s your first reaction when you hear someone say these?

While the surface context is positive, it’s not difficult to dig up the contradictory downsides within each one:

A “stable” marriage can mean that two people get along very well, and it’s clear that they’ll be together for the rest of their lives. On the flip side, it can mean that they never speak to one another or have given up trying to work out difficult problems that have been festering under the surface for years.
A “stable” career can mean that your trajectory up the corporate ladder is assured and there is nothing but blue sky and stock options ahead. It can also mean that you’ve been sidelined into the same role for a decade, or that you’ve gone as far up the ladder as you can get and still aren’t where you want to be.
A “stable” schedule can mean you’ve discovered your ideal lifestyle and have the means to live it on a daily basis. It might also mean that you’ve been locked into a machine-like routine that you both hate and can’t escape.

Shooting Yourself in the Foot

The contradiction here is that when you’re always grasping for stability, you often shoot yourself in the foot and end up with more volatility.

Even though a stable environment sounds great on paper, in practice a lack of change usually provides results opposite to what you were looking for.

You could, in theory, achieve perfect predictability by locking yourself in a dark room and never leaving it. A prison represents a high degree of stability, as prisoners have mandatory routines that are defined for them.

But you have to ask yourself: what benefits come from such low levels of volatility? Is creating a prison (either mental or physical) really the best move? Does a zero-volatility lifestyle really sound so appealing if it means doing nothing?

For a certain type of personality, I suspect the answer is “yes.” The idea of being “institutionalized” by prison life is well-established, and many people leave prison-like conditions only to find themselves feeling lost as a result.

If that’s you, then you need to listen to what I have to say more than anyone else. Why? Because in a world ruled by volatility, an intolerance for change is a death sentence.

Stability requires us to make changes. This is the core of the contradiction of stability: our constant search for stability leads us into volatility.

Why do you think we call careers “the rat race”? We’re all scurrying around, desperate to latch on to stability in the form of job security, high pay checks, and a predictable path up the ladder.

Changes

But how do we accomplish this? By making changes, constant changes. We try out different clothes, take online classes, read books, join clubs — we can’t help but make dramatic shifts on a regular basis, hoping to finally hit that road to the paradise of stability.

Maybe someday, through all your racing through the maze, you’ll be able to reach that peak of zero-volatility: paid retirement, a carefree lifestyle and margaritas on an exotic beach. Then you’ll finally get what you want…when you’re old, and have spent the best years of your life drowning in volatility.

At the end of the day, you never escape volatility in a meaningful way. Change is a constant, no matter how hard you try to escape it.

What about somebody who always seeks volatility? A similar contradictory result shows up much of the time here, too.

I remember watching a show about prison a long time ago, and one of the prisoners described the reason he was always heading back to the big house in a way that I still think about:

“I just live life by the ‘balls-to-the-wall’ philosophy.”

In other words, he was always looking for excitement and that quest for peak experiences always took him down the path of prison time.

This sums up perfectly what happens to many people who live for a constant rush. Their quest for volatility at all times leads them to what they dread the most: indefinite stability.

The ultimate example is someone who puts themselves in harm’s way all the time and ends up dead.

What is death, if not the purest form of stability?

Synthesis

We’ve established at this point that there are two forces at play (stability and volatility), and that there’s a central contradiction between the two of them: seeking one out exclusively tends to give you the opposite of what you want.

Pure stability-seeking leads to high levels of volatility, and pure volatility-seeking leads to high levels of stability.

What is a fundamental, first-principles idea that can be gleaned from all of this? What is it about volatility and stability that burns deep into the very fabric of existence?

In short: what is the synthesis that bursts forth from everything you’ve just read?

This is my answer:

Volatility is a fundamental concept because it emerges from the contradictory relationships built into the complex systems and narratives we create.

Phrased differently, volatility is the unavoidable consequence of:

The systems we create, which end up being complex because of how interconnected everything is now.
The narratives we construct to explain those systems, because there is inevitably information asymmetry. The asymmetries exist between those who created the systems, those within the systems, and those who are outside of the systems.

This is why volatility is so critical to understand. Everything around us is complex, and everything around us has some narrative surrounding it. This means there will be contradictions, and in turn volatility will be present.

It’s a complete mind-fuck of an idea: we must expose ourselves to high rates of change, and we must be ready for the explanations we find to contradict each other. Conflict is a constant, there is no escape.

From all of this, it becomes clear that volatility isn’t just some concept for traders to toss around as a way to describe deviations from expected prices. It’s far more fundamental and important than that, and you should turn it into a central tenet of your thinking.

Some Semblance of Balance

Think of the relationship between volatility and stability as being like an inverted U shape. In the middle of the inverted U is the ideal mix of volatility and stability, but too much stability or volatility (either side of the inverted U) is counter-productive:

The sweet spot between volatility and stability.

By seeking out this “sweet spot,” we can at least approach the contradictions of stability and volatility in a constructive way. Instead of the living death of pure stability, or the untenable chaos of pure volatility, we can probe the world for situations where different amounts of each are appropriate.

My goal is to live somewhere near this balancing point between stability and volatility. Throughout the years, I’ve found that this is a difficult task — sometimes it’s impossible.

In some ways, the concept of equilibrium falls into the same bucket as volatility: a contradiction that forces you away from your goals. If all you ever seek is equilibrium, you’ll be disappointed in how infrequently you find it.

Such is the nature of exploring complex topics. Sometimes (most of the time), there aren’t neat, easy solutions.

In many cases, you’ll find that ideas you hold sacred need to be completely rearranged, or thrown out all together. In some cases, you might get lucky and realize you were on the right track.

Either way, be ready for a long trek.

In the meantime, here are some guidelines that I use for thinking through the problems volatility presents:

Embrace the fact that neither volatility or stability possess affective properties. They aren’t good or bad, they just are. Whether they have a positive or negative impact depends on the environment and the people involved.
Utilize the Thesis, Antithesis, Synthesis approach when looking at complex problems. TAS can be summarized as finding a concept, coupling it with a competing concept and then finding where the two antagonisms intersect. Utilizing this method will help you identify the contradictions within any given domain, recognize the crossroads between what people mean and what people practice, and see where solutions might lie.
Do your best to embrace volatility and not spend so much time looking for stability. Consider the inverted U and ensure you have a mix of volatility and stability in your life. Pure volatility and pure stability are both poisonous, so as long as you aren’t too far in either direction you should be alright..

The Power of Sorting Algorithms

Ace Eddleman

This is part of The Algorithmic Society, an ongoing series about how algorithms and algorithmic thinking have taken over the world. Want to know when new content shows up? Sign up for my newsletter here.

We’re drawn to clean solutions, no matter what form they take. Complexity is frightening to us by default, and we crave results that we can forecast in all domains of life. Consistent patterns make us feel good, chaotic patterns make us run for the hills.

Pattern recognition is just that important to us. Patterns give us a sense of safety. Even if that pattern is bad (“Lions ate Fred when he walked near that tree, I should avoid that area”), it gives us a feeling of certainty that we find satisfying.

Algorithms free us from this fear of the unknown. They take us by the hand and whisper in our ear: “Don’t be afraid, I have the answer for you right here.”

This is more true today than it’s ever been. The algorithms we care about and interact with the most are designed to provide us with a safe haven in a disorderly world. They take the noise of a complex world and hand us something clean in return.

Consider sorting, one of the most common types of problems solved by algorithms.

If you crack open a textbook on algorithms, including the famous Introduction to Algorithms (aka “CLSR” after its authors), sorting is often the first major category to be explained. The reason is made clear in that aforementioned textbook:

Because many programs use it as an intermediate step, sorting is a fundamental operation in computer science. [emphasis mine]

Defining the problem of sorting algorithms is simple: there’s some set of “disordered” items, and it is the job of the algorithm to make them “ordered.” Where things get interesting is in the definition of the solution.

Sorting algorithm in action — Isn’t this satisfying to watch? Image source

This sounds trivial at first, and in many cases it is: there aren’t many implications to explore around how to sort a collection of files on your local hard drive, for example. There’s nothing political or philosophical to explore when you’re talking about text files arranged in alphabetical order.

But when you start to look at real-world sorting algorithms, a picture of how much of an impact it can have on the world we live in starts to emerge.

Facebook, Google, Amazon, and all the other major algorithm-based tech firms out there can be described in some sense as the great sorters of our time. Facebook sorts your social ties, Google sorts the web’s massive content database, Amazon sorts products, and so on.

When you login to Netflix, for example, you aren’t looking at the entire collection of films and shows they have available. Instead, the platform automatically sorts content based on what their algorithms think you’re most interested in.

This algorithmic work is performed in a way that saves humans time and energy, and in return someone gets charged for it. That “someone” is often not the front-end users of the algorithms, but instead those (such as advertisers) who wish to insert themselves into the sorted output set in some way.

Sorting Out Incentives

What are these algorithms sorting for? Again, in trivial examples like sorting files on your local hard drive, it’s not that complicated. You’re probably sorting in alphabetical order, or last modified date, or some other mundane sorting mechanism.

You could say this kind of sorting is utility-based. Making sure your files are in alphabetical order is done not because it accomplishes external objectives for a third-party, but because it makes your file system easier to navigate. It’s the computer doing what it does best: saving your precious brain cycles for something more interesting.

That’s not the case with a platform like Netflix. While it is possible for them to sort all of their content alphabetically and then present you with that whole library, it’s unlikely anyone wants that (aside from the most fringe film buffs). Their goal is to provide you with a small slice that appeals just to your tastes.

There is some straight-ahead utility in the type of sorting they provide, because they’re presenting content in a way that allows you to avoid the process of wading through that massive library on your own. But, in a theme that pops up over and over again with valuable algorithms, there’s a trade-off being made that most users aren’t aware of.

Every minute you watch Netflix, Netflix is watching you (and the same could be said for any major content sorting algorithm, such as YouTube). Each data point it collects allows you to refine its sorting algorithm just a little bit more.

This is done under the aegis of giving you the “most relevant content” (a theme you’ll hear about over and over again from algorithmic companies), which, as I stated before, is sort of true. You don’t want to have to sort through it all yourself, but you also have to wonder where the recommendations come from.

It turns out that what they’re really sorting for is time-on-platform (also known as “engagement”). In other words, they sort based on what will keep your eyeballs on the screen the longest. The algorithm is geared towards this and sorts based on what will keep your attention focused on the screen.

That could be almost anything — Ancient Aliens, anti-vaccination conspiracy theories, Steven Seagal movies — as long as it results in additional time-on-platform. To use fundamental language, the “problem” these sorting algorithms are “solving” is lack of engagement.

It’s at this point that we start to see how our love of pattern recognition can come back to bite us. We engage with the content that’s recommended to us, mostly because they fit with patterns we find appealing. The algorithms (which are patterns themselves, albeit of a different nature) then exploit our engagement of those patterns to shape new, more potent patterns for us to engage with.

There’s also some degree of sorting that’s required in order for search algorithms (another powerful class of algorithm) to be productive. If all you got was pages of fake Viagra ads because someone figured out how to game the search algorithm (which was a problem in the early days of search), then the engine would be useless.

But there are more third-party incentives built into search as well. When we search through Google, we aren’t searching through the whole of the Web. Instead, we’re searching through their index of the web (a sorted list) that they own and control.

What do they do with that index? They present search results that play most favorably to A) Google’s advertising system, and B) Google’s indexing system (also known as Search Engine Optimization, or SEO). Their users feed data into the system, and advertisers find ways to insert themselves into the sorted index.

Sorting Out Ownership

One could argue that some price should be paid for this search/filter/sort mechanism, but that’s not the point. What I want you to focus on is the fact that this algorithm is not some unbiased machine giving us the absolute best results — it gives us a “good enough” result that panders to the incentives of the algorithm’s owners.

This means that whoever owns the algorithm owns the sorting mechanism that their users plug into. By doing this, algorithms act as powerful leverage points for controlling perceptions.

The most famous example of this is how Facebook and Twitter have impacted political discussions. Because engagement is the defined solution for both platform’s sorting algorithms to solve, the sorted outputs create an engineered reality that optimizes towards that goal. Whether that goal is good for the user (or society) is not built into the specifications of the algorithm.

But the reach of sorting algorithms goes beyond the common, borderline cliche discussions of how social media has changed the political landscape. They’re now used not just for these platforms, but for more mundane (and in some ways, powerful) tasks.

Think about how sorting impacts how companies hire people, for example. The competition for jobs is fierce in almost every field, and human relations departments are overwhelmed by the sheer volume of CVs they receive for any given opening.

This is a serious problem: companies hire because they require labor to solve specific sets of problems, and the world is filled with people who need jobs. They need some way to shortcut the process of excluding and filtering people so that they can hire the “right” person — in other words, they need a sorting algorithm.

Once again, how the solution is defined matters. If a company wants to cut out a large number of candidates, regardless of actual skill, they can set a high bar with educational requirements.

Accomplishing this is now a matter of setting the parameters for their resume sorting algorithm. If a candidate does not mention any sort of university participation on the CV, they are sorted into the “round file.” Any sort of tangential value the candidate might bring to the table isn’t explored, because the algorithm’s specifications don’t include values like that.

There’s quite a bit of granularity that can be built into this. Maybe a degree isn’t enough, maybe the employer wants specific GPAs or post-graduate credentials. Again, minor tweaks to the algorithm and a large portion of the resumes being sent in gets thrown out.

What the employer is left with is a small, exclusive pool of candidates who fit exactly what they’re looking for. Whether their hastily thrown-together, biased assessments of what the “right fit” looks like is actually what they need is simply assumed. The algorithm can’t disagree, it just sorts.

This is how we’ve created a corporate landscape built out of yes-men (and yes-women, to be fair) who can’t think for themselves. Our internal algorithms about who the “right” people are tend to be defined by easy-to-spot, surface-level features like credentials and GPAs. Then we tell our resume sorting algorithms to optimize for those flawed perceptions and they manifest into real-world consequences.

It could likewise be argued that credit scores represent a sort of societal sorting algorithm. The problem to be solved is determining what level of creditworthiness an individual fits into, and the solution is a specific number.

Those with strong credit scores tend to get sorted into the most preferable buckets by banks, employers and a variety of other institutions. People with low credit scores get sorted into pathways that leave them with limited, frustrating options.

Once again, sorting is at work and the consequences of largely computerized algorithmic processes are very real. And, of course, how we define the solutions to these sorting problems is left unquestioned for the most part. It’s taken as a given that people without college degrees or with bad credit are less worthy of society’s benefits, therefore we let these algorithms “do their thing.”

Instinctual Sorting

There are some qualifications that need to be made on this subject. For one, sorting is a fundamental feature of our cognition. We need to sort our own individual environments, people and objects into categories like safe, dangerous, and so on.

For example, our brain has a built-in sorting mechanism that determines what goes into our memory and what gets forgotten. The signal used by this algorithm is salience, and anything that doesn’t hit a certain threshold of novelty is sorted into our cognitive garbage cans.

This inclination is a common thread towards how we build our societies. We sort people by class, income, political leanings, race, and a nearly infinite number of other feature sets.

On that basis it’s clear that the sorting algorithms themselves aren’t always the problem. They are often tools generated by our own inclination to order our environments, and they are in many cases useful. But, like a hammer that can either push nails or break skulls, algorithms can also be used as weapons.

It’s possible to create an algorithm with good intentions and end up creating serious problems. This is a strange sort of irony that’s created by algorithms and all the other manifestations of our need for order: in most cases, they create more disorder.

And, in line with our general misuse of algorithmic language, we don’t often talk about sorting as sorting. We use language that sweeps the downsides under the rug, such as “optimization” and “curation.” Words like “exclusion” don’t come up much, even if that’s what we get.

Sorting Out This Post

What can we learn from all of this? To my mind, the most important point to take away from all of this is that sorting perhaps the most important type of algorithm out there. There are many different, ever-evolving ways to sort, but the end goals tend to fall into a narrow range.

Sorting is becoming more powerful and more useful as time goes on. This is because the world is becoming more complex by the day, and sorting through the noise in novel ways — especially in ways that can make money — is valuable. Efficient sorting is, and will continue to be, worth billions of dollars.

From this point forward, whoever owns and controls the sorting algorithms will win. The world is being divided into the sorters and the sorted, and it’s becoming obvious that the sorted are losing ground in every walk of life.

Recognizing how sorting grants power to certain people and organizations is therefore a key skill in today’s algorithmic society. You should be capable of recognizing when sorting is occurring and work to understand the “who” and “why” behind it in a variety of contexts.

Losing Games

Ace Eddleman

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Much of what I create revolves around the idea of competing intelligently. My overall hypothesis about competition is that most people do it haphazardly, and expect their own intuitions — mixed with a recognition of incentives — to carry them to victory.

Thinking this way is a serious error. It leads to making the same mistakes over and over again, and creates situations where meaningful learning takes much longer than it should.

What’s more important, in my opinion, is that we recognize just how competitive the world is. Competition exists at all levels of life, all the way down to single-celled organisms. Competition is a key component of evolutionary biology, and there isn’t any form of life on earth that can escape this dynamic.

There’s competition for money, competition for status, competition for relationships. There’s competition everywhere.

Life is competition and competition is life.

With all that being said, there’s an idea that’s just as important to keep in mind: sometimes, you’re playing a game you can’t win — no matter how intelligent you are or how hard you work. When you find yourself in this kind of game, what can be called a losing game, you need to exit that game as fast as you can.

Recognizing losing games like this is a skill in and of itself, one that many people find hard to develop. It’s particularly common in American culture, where we’re constantly told that hard work is the answer to all of life’s problems.

An extreme example I like to use is professional basketball. The first requirement for playing basketball at the pro level is to be very tall, which is something you can’t train for. You’re either born with tall genetics or you aren’t.

This can be a hard pill to swallow for people who don’t hit those height requirements but love the game enough to dedicate their lives to it. Someone who is only average height can spend every waking hour refining their game, doing everything they can to get better, and still come up short.

The problem in this situation isn’t that the player isn’t committed or intelligent enough. It’s just not a game they can win — and there’s nothing they can do about that.

Instead, this same player could find another way to be involved with the game. Maybe they could find work as a talent scout, or a commentator, or a sports writer, and still play for fun in recreational leagues.

Maybe they’re an exceptional programmer or mathematician, and that could allow them to build some kind of technological product that is intertwined with basketball.

Those are all winnable games for this fictional person: they offer odds with large payoffs and none of them have requirements that are impossible to train for.

What tends to drive people like this crazy is the search for glory. They want to do what’s most admired in society, like playing a sport professionally.

The irony is that wasting time on paths like this more often than not generates an excessive amount of unnecessary misery. Most people don’t really know what they want, they just think they know, so they waste their time pursuing goals that other people or society set for them.

That same player who wants to be a player more than anything might find more fulfillment in an auxiliary role than they can estimate.

Instead of wasting years pursuing a professional playing career that ends badly, they should find something else that might even end up being more fulfilling (or even lucrative).

Sometimes you can create the game yourself, and sometimes you have to go play someone else’s game. Either way, you should be doing this kind of analysis on a regular basis. Every now and then, stop and ask yourself: Is this a game I can win?

I’ve failed at this more times than I can count, and it’s cost me dearly on a few occasions. Hopefully you can heed my words and not make the same mistakes I have. Don’t play games you can’t win — find a place where you can play with favorable odds, and then throw yourself into that.

Autonomy

Ace Eddleman

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If you ask most people what they want out of their work lives, there are two answers that appear to be tattooed on the inside of their skulls by popular culture:

Money
Making a difference

The priorities might be switched, but these are the two most common replies. It’s not a surprise that people pick these two, because A) we need money to buy things like food, shelter, and pleasurable experiences, and B) working a job where you feel like you aren’t making any sort of dent in the world is a soul-crushing experience for most non-sociopaths.

Neither of these answers are wrong — they’re just incomplete.

When someone gives these answers, worth asking a follow-up question: What is the common, fundamental thread between the two of them?

A common answer to this question is “meaning.” That’s a little too whimsical for my tastes, as meaning strikes me as an ideologically-charged construct that is borderline impossible to define. Even if meaning is given a quality definition (which I haven’t seen yet, but I’m open-minded), it still misses the target.

Instead, the real answer is (in the vast majority of cases), autonomy. We want to be able to determine how we spend our limited time on this planet. In short, we don’t just want to survive — we want to survive on our terms.

Nothing makes us more miserable than having someone breathing down our necks, telling us what to do every day.

And, unfortunately, that’s what most of us end up doing with the bulk of our lives.

We give up a lot of autonomy in the pursuit of money, which is both sad and ironic given that we’re working for the thing that is enslaving us in many cases. The fantasies about freedom we feed ourselves all seem to revolve around money, and not about autonomy.

This isn’t entirely misguided: within a capitalist framework like the one we live in, money can (at certain amounts) provide a great deal of autonomy.

If you suddenly have 50 million dollars in the bank, you can tell your boss to go fuck himself and proceed to jump into a pool of champagne.

Or you could politely hand in a resignation letter and shake everyone’s hand on the way out of the office. Or you could just never show up, never respond to an email or call, and laugh about leaving your former colleagues in the dark.

That’s the power of autonomy. You get to decide what you do within any given moment, so life becomes a “choose your own adventure” story instead of a constant context switch between what you want to do and what someone else wants you to do.

Making a difference and the search for meaning both fall into the autonomy bucket as well, because both are activities that you judge on a subjective basis. Maybe your definition of making a difference is volunteering at a local animal shelter, or creating a documentary about a local endangered species.

Searching for meaning might involve spending your days reading big, complex books and then going for long walks on the beach. Or maybe it dawns on you that nothing has meaning and you turn into a shameless nihilist.

This is the power of living on your own terms. You get to decide what you want to do. The world is what you make of it.

From this point forward, you should evaluate the different opportunities you have in life based on how they’ll affect your ability to operate autonomously. But, as I’ve been alluding to, this can be a monumental task.

Quite a few people make a trade-off of money for autonomy, and discover that it makes them miserable. Investment banking is a good example of this, as bankers are paid tons of money but often work soul-crushing hours with very little autonomy.

Many of them would likely be much happier making a fraction of their incomes engaging in activities that they actually want to be engaging in.

Working your face off while you’re young in hopes of some autonomy payoff in the distant future is another landmine many people step on. These people look back when they’re older and realize their best years were spent in service of someone else’s desires — and there’s no going back.

I know it’s hard with how competitive the world is now, but try to make an effort to think less about the monetary rewards you might get from doing something. Instead, reorient your thinking towards what you can do that will give you the best chance at living an autonomous life.

You might be surprised how little it costs.

The Exploration-Exploitation Dilemma, Simplified

Ace Eddleman

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Transcript:

I’ve written about the exploration-exploitation dilemma before, but only in a long-form essay format. Since I think this is such a critical concept, and I realize that not everyone has the time to read a big essay, I’ve created this simplified explanation.

Just a warning: like any other 5 Minute Concepts piece, there’s always more to the story. I’m just trying to give you the most important parts in 5 minutes or less.

Anyway…

Let’s start with a stripped-down definition: the exploration-exploitation dilemma is the choice we all have to make between learning more or taking action with the knowledge we already possess.

Learning more is exploration, acting with current knowledge is exploitation.

With either action you’re trying to find some way to maximize what’s often referred to as “reward,” or some end-state that you find desirable.

The reason this is a dilemma is simple: you can’t explore or exploit exclusively and win in the long run.

If all you do is explore, you’ll never take action in the world — which means you get a predictable payoff of exactly zero. There isn’t much to be gained from passively gathering information until you die.

On the other hand, taking action without learning anything is also a long-term losing strategy. You do get some kind of reward by exploiting a known path, but that means you’re giving up any chance at a higher payoff that might be staring you in the face without you knowing about it.

The real kicker here is that exploring is what drives the value of exploitation, and vice versa. You need to explore in order to find good paths for exploitation, and you need to exploit in order to get a reward for your exploration. Both actions are dependent on each other.

What you’re balancing in either case is opportunity cost. You have a limited amount of resources, such as time and money, to work with over the course of your life. If you explore, you’re by default not exploiting, and vice versa.

Consider this example: Let’s say you’re scrolling through Netflix, looking for something to watch for the new couple of hours.

You notice that a movie you’ve seen a dozen times is one of the choices and consider watching it. Right next to that is a movie you’ve never seen before.

Choosing the movie you’ve seen provides a specific emotional payoff for you. You know all the best parts and you’re well aware of how the entire experience will make you feel.

Choosing the movie you’ve never seen means taking a certain amount of risk. There’s an unknown payoff for watching this new movie, and it might end up being a waste of two hours. Those two hours will be gone, never to return.

But you might also discover a new favorite movie, or genre, or director, that you never knew about.

It’s easy to get sucked into either extreme. I’ve known people who spent their whole lives reading, accumulating a veritable library worth of knowledge in their head, but never tried to do anything with it.

And, of course, I’m sure we both know people who have never read a book or stopped to think for even a moment about whether their beliefs and actions should be altered in some way.

While this is an unsolved problem (and trust me, many people have tried to figure it out), there are some good rules of thumb to run with. First of all, don’t favor a binary approach. Only exploring or only exploiting doesn’t work in the long run.

Secondly, it pays to spend a lot of time exploring early on and then shifting more and more to exploitation over time. But — this is critical — you never stop exploring completely. For a person in the real world, exploring should always be part of your strategy.

There’s always some accommodation made for learning new things. This is known as the epsilon-decreasing algorithm, and, if you just want a simple heuristic for managing this dilemma, it’s a pretty good place to start.

Third, there are always inflection points where it makes sense to shift from one to the other. Sometimes it’s a moment where you realize you’ve finally reached a level of knowledge that grants you a new level of competence and the time to utilize it has come. Passing a professional exam is a simple example of this.

Other times you might suffer a bitter defeat and receive an unfiltered signal that it’s time to explore. If a big project you’ve been working on fails, for example, you might need to go back to the drawing board and evaluate how to improve for your next attempt.

I could talk about this for hours, but in general I want you to understand this: figuring out how to spread your time between exploration and exploitation is perhaps the most important problem you’ll ever face.

Don’t push this into the background — be conscious and deliberate about it. Doing that might just change your life in ways you never saw coming.