Learning

The Power of Sorting Algorithms

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

This is part of my 5 Minute Concepts series, which is designed to help you understand fundamental concepts about subjects like learning, memory and competition in the shortest time possible. Each episode is available in video format on my YouTube channel and audio via my podcast. If you prefer to read, the transcript is below.

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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.

Pattern Recognition

Ace Eddleman

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Transcript:

What separates experts from novices? More than anything, it’s pattern recognition. The expert has the knowledge and experience to recognize the most important patterns in any given situation.

While it is possible to leverage pattern recognition in solitary pursuits, and there’s quite a bit of literature on the role of pattern recognition in overall intelligence, my focus today is on how it impacts competition.

Pattern recognition provides two important advantages. First of all, it makes processing your environment much more efficient.

When you’ve seen a pattern a million times and know it’s significant, you automatically tune out other, less relevant cues in your environment in order to focus on that pattern.

This is a mental shortcut, because it means your brain doesn’t have to work hard to find signals within the noise that surrounds you — it just happens by default.

Novices struggle in large part because they lack that ability. They get overwhelmed by the cues around them and try to address all of them at once. The expert knows what to focus on, and therefore can allocate their mental and physical resources on what matters the most.

In this context, you can view pattern recognition as a sort of built-in risk management mechanism. When we see things we recognize, we can more easily classify them as “safe” or “dangerous.” This is true not just in competition, but in life in general.

Secondly, pattern recognition gives you the ability to anticipate what’s going to happen in the future. If you’ve spotted a pattern in an opponent and they don’t show signs of changing it, you can exploit that.

Much of competition is this dance of figuring out patterns, exploiting them, and then adapting to replacement patterns.

This dance happens in donkeyspace, with both sides trying to figure out how to get maximum exploitation out of their opponent’s patterns without creating too many exploitable patterns themselves.

Poker is an excellent example of how pattern recognition can dominate an entire game’s competitive spaces. Everyone sitting at the table is watching and learning, trying to spot patterns (which are referred to as “tells”) that can be used for profit.

Maybe one player always scratches their nose when they’ve got a good hand, so you choose to fold rather than bet against them whenever that pattern shows up. Or maybe they start playing with their chips in a certain way when they’re bluffing.

Top-level professionals are watching those physical cues, plus they’re paying even closer attention to betting patterns. They watch how every player pushes their chips around at every stage of the game, looking to spot ways they can infer an overall player “personality.”

There’s even a common language players use to describe specific types of players, which is itself a mechanism for aiding pattern recognition. For example, those who only play the best cards in a hyper-conservative manner are labeled “Rocks,” and the players who are intensely aggressive are called “Maniacs.”

It’s a general principle that the skill of spotting meaningful patterns in opponents is one of the most sustainable competitive advantages you can create for yourself. Being able to see and exploit the holes in your opponent’s game better than they can means you’re always a step ahead of them.

But there are dangers to be found in our pattern recognition capabilities. To start with, there’s always the risk that we’re focusing on the wrong patterns. There’s a whole metagame of creating the patterns designed to deceive opponents, and if you aren’t aware of that metagame then you can find yourself in hot water very fast.

On top of that, you might see the right pattern but come up with the wrong method for exploiting it. It might be because you lack the experience or execution skill to take advantage of it, or it might be because your analysis of that hole was just off the mark. Just because you can take advantage of a pattern doesn’t mean you will.

To use the poker example again, another player might be aware of their own tells and use that against you. They might know they have a tendency to scratch their nose when they bluff, and deliberately scratch it when they have a strong hand just to bait you into playing against them.

Anyone can (within reason) get better at pattern recognition with practice. But not everyone is capable of getting good enough at it to hijack the pattern recognition habits of other people. This is, in my opinion, the mark of the master competitor.

Think about this next time you have to square off against an opponent in some competitive arena. It’s easy to spot patterns for yourself, but what are you doing to set traps with the patterns you give off? Practicing this will pay gigantic dividends in the long-run if you stick with it.

Specialization

Ace Eddleman

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A real-world manifestation of the exploration-exploitation dilemma can be found in the choice we all have to make between being a specialist, which is a path dominated by exploitation, or a generalist, which involves a focus on exploration.

In my course on competition, I tell students that specialists tend to win in competitive games. I still stand by that, but only because I was talking about the specific context of formal games. The real world is much more complex and therefore the answer is murkier.

The problem is that the world is a fascinating place, and there’s so much to learn. Even if you spend all day learning and probing around, you’ll continue to uncover knowledge that makes you feel like you’ve been wandering in ignorance your whole life. This tends to pull us towards becoming generalists.

On the other hand, endless exploration is not a feasible choice for those of us living within a capitalist framework. Nobody is going to pay you to read and think all day, unless you already have a pool of resources to work with.

This is why people who spend their whole lives immersed in creative activities tend to either be rich already, or benefit from some kind of patronage (the Medicis financed many of the great Italian Renaissance artists, for example).

That means most people are expected to become specialists, which is why there’s such an emphasis on professional training.

Going to college and spending time climbing a corporate ladder is the common path for demonstrating expertise within a given domain. Kids in school aren’t encouraged to explore much, and instead are told it’s best to pick a pathway that’ll guarantee a healthy income in the future.

There are some problems with this. First of all, a contradiction lives at the very core of all this: you can’t find the best exploitation paths without exploring first. I’ve covered this before in great detail, but it’s worth reiterating that exploration and exploitation feed off of one another.

By telling people to simply take the path of least resistance — to exploit a known payoff schedule, like going into investment banking — employers, teachers and other authority figures are nipping potential in the bud.

As a society, we’ve decided that securing an income in an established industry matters more than developing new ways forward.

Secondly, there’s a serious risk associated with deep levels of specialization: path dependence. If you go far enough down a path, it becomes near-impossible to turn around and reorient yourself in the world.

For example, if you’d spent 20 years becoming an expert in typewriters right as the personal computer boom began, you’d be in trouble. The payoff for your expertise would have evaporated, and adjusting to this new world would be a tall order (to put it lightly).

This obsession with specialization helps people who are already part of the status quo, and hurts those who don’t fit into the molds those with power hold sacred. It’ll get you a job, but it might also shut off your capacity to live the way you want to.

I remember one time I saw a job ad for a company in the credit card industry, and one of the “Nice to Haves” was, no kidding, “A deep passion for credit cards.” All I could think when I read that was, “What kind of psychopath has a deep passion for credit cards?”

The answer is simple: people who want to get jobs in the many modern industries can’t just be knowledgeable about credit cards (or whatever domain they want to work in), they have to be so specialized that it seeps deep into the fabric of their personal lives.

To put it another way: the world wants you to not only know something about what you do for a living, it wants you to be dominated by that subject until it consumes your whole identity. I can’t help but see this as a problem.

But, again, we come to a quandary. This is the world we live in, and it won’t change overnight. What can a single person do to give themselves a chance at success?

My suggestion here — and it is only a suggestion, because I don’t think this can be fully fleshed out and resolved in a single 5 Minute Concepts episode — is to think about the intersections of your interests. Where do different skills and ideas overlap to create something that could be viewed as “useful” to others?

It’s not an easy task to accomplish, as I can tell you from experience. But if you can come up with some kind of mix that’s both unique and valuable, you’ll find that you’ve flipped the whole formula on its head.

You will be specialized because of your generalist tendencies, and not specialized in spite of them.

Failure & Learning

Ace Eddleman

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Transcript:

Failure is a subject that I have a love-hate relationship with. On the one hand, failure is an important feedback mechanism that can drive learning. On the other hand, it’s been fetishized by the self-help crowd and turned into some glorious path to success.

Yes, it is true failures are inevitable and cannot be divorced from the expertise-gathering process. Everyone, even the best in the world in any given field, has experienced failure at one point or another along the way.

Sometimes a particular type of failure — usually some kind of dramatic departure from what you wanted to happen — can act as a beacon for a beneficial change that leads to a big win. That is a very real side of failure, and I don’t want to discount the value of failure in certain situations.

And yet there are all kinds of memes that lead people to view failure in the wrong light, or get used as a means of not doing the hard (but necessary) work of examining why they failed.

The most common meme around failure is the “Man in the arena” quote by Teddy Roosevelt. Now, I’m not knocking the quote — there’s a lot to be said for giving something a shot when others aren’t willing to — but I am knocking how it’s used as a means of hand-waving away failures.

Every time I see or hear someone using that quote, I want to ask that person, “Is failure always meaningful?” The hard truth is that it usually isn’t, and sometimes critical failures completely derail us. Sometimes that derailing is permanent.

It’s important to confront this uncomfortable truth head-on. There are, in fact, events in life that have serious consequences. There are situations where it’s not possible to recover from a failure, as much as we might want to play the role of the eternal optimist.

Why do we fail? More often than not, it’s because we underestimate the problem we’re trying to solve, and overestimate our ability to solve it. Or we don’t even understand the problem in the first place, and as a result we apply an inappropriate solution.

It’s common to lean too much on our own psychology as well. We think that belief or motivation alone are enough, when more often than not they’re only small, insignificant pieces of the puzzle.

We tend to have high opinions of ourselves, so it’s not so shocking that we step on landmines which could have been spotted if we had our egos a little more in check. When it becomes clear that failure is inevitable, we’re shocked at our own hubris — but alas, it’s too late.

After the fact, we’re prone to pointing fingers in the wrong directions. Not to say that there aren’t ever external factors to blame (I’m personally not a fan of the “absolutely everything is the fault of the individual” school of thought), but there are usually signs that mistakes were made ahead of time.

So, now that we have that dark side out of the way, when does failure help us instead of hurt us? What are the heuristics we can all use to figure out if we’re failing in the right direction, or just failing?

While there isn’t a simple algorithm for all situations, there are some guidelines you can use to make an educated guess.

First of all, ask yourself if said failure was unexpected. If it was, what kind of probability did you assign to it? If it wasn’t, what could you have done to stop it? Asking these questions will force you to confront your own estimations of yourself and what you were trying to accomplish.

Second, was something meaningful learned from the failure? It’s critical to be honest here — don’t just make up some sorry, generic phrase to describe the failure like “I learned that I have a long way to go.” That may be true, but you need to be specific — what actions led to this outcome? What could you have done differently?

Lastly, what’s your experience level in this domain? If you’re brand new, it’s normal to fail a ton, and it’s equally normal to learn quite a bit from each failure.

As your experience increases, you’ll hit diminishing returns on each failure because you should be collecting enough data to fix the biggest mistakes over time.

If you possess a deep level of expertise and you’re making big mistakes, then it’s time to go back to the drawing board. The world you’re in may have changed in some fundamental way, and you might have to rethink how you approach solving that set of problems.

You’ll never escape failure entirely — even the best still screw up sometimes — but you should see a decreasing frequency in the biggest failures if you’re learning the right way.

In general, it’s worth pondering the overall value of failure. In many cases it’s not helpful, despite what the gurus would lead you to believe. But it’s also a crucial part of any successful learning plan, especially for beginners.

You could say that failure is both underrated and overrated, it just depends on the nature of the failure itself and who it’s affecting. Think about that next time you hit a snag in your learning.