Pattern Language

Last updated: Wed Nov 06 2024

These are various “patterns” that I tend to use and reuse in my thinking. This page is inspired in no small part by Jacky Zhao’s “A Pattern Language” and her list of patterns. Is this really accurate to Christopher Alexander’s A Pattern Language? No idea, but I find this useful.

Table of Contents

Open Table of Contents

Chesterton’s Fence

Before you remove a fence, figure out what it’s for.

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In the matter of reforming things, as distinct from deforming them, there is one plain and simple principle; a principle which will probably be called a paradox. There exists in such a case a certain institution or law; let us say, for the sake of simplicity, a fence or gate erected across a road. The more modern type of reformer goes gaily up to it and says, ‘I don’t see the use of this; let us clear it away.’ To which the more intelligent type of reformer will do well to answer: ‘If you don’t see the use of it, I certainly won’t let you clear it away. Go away and think. Then, when you can come back and tell me that you do see the use of it, I may allow you to destroy it.

G.K. Chesterton, The Drift from Domesticity

Thanks to cultural evolution, many perplexing human behaviors are actually adaptive, even if participants can’t actively explain themselves. If you suddenly change that behavior, you might find that it was load-bearing — it was completely necessary for surrounding systems to function! So, it’s often worthwhile to investigate before changing, especially if a behavior seems confusing.

Because humans occupy the cultural niche and many of our behaviors can be looked at through a cultural lens, Chesterton’s Fence can be applied to many different fields. Programming is a particularly fruitful field — whenever dealing with legacy code, it’s always useful to ask why the code behaves the way it does, even if it’s strange (for instance, being written in COBOL!).

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Cultural Niche

Humans are uniquely successful due to our social learning ability.

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Humans are not the only intelligent animal; humans are not the only tool-using animal; humans are not the only social learning animal. However, humans are almost unique in their ability to socially learn across generations, slowly accumulating more effective tools, processes, thinking patterns, and organizations of relationships to solve day-to-day challenges, the sum total of which we call “culture.” Our biology is in fact evolved to promote just this ability, which is adaptable to nearly every environment; in a very real sense, our “niche” as a species is “anything we can learn to do from others.”

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Cultural Evolution

Culture is subject to evolutionary pressures.

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The logic of Darwin’s theory of natural selection only requires three preconditions:

If a trait meets these three preconditions, we should expect to see evolution — a directed change in the proportions of a trait across a population across time. Notably, as Mesoudi’s Cultural Evolution points out, Darwin’s logic does not rule out Lamarckian (within-generation) evolution or require particulate inheritance, both of which are parts of biology’s modern synthesis. As a result, human cultural behaviors can also be considered as subject to evolutionary pressures, albeit following a different model than the biological synthesis.

This has all kinds of implications; for instance:

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Deliberate Practice

Deliberately practicing skills can be the most effective way to improve them.

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Setting aside time (the famous “10,000 hours”) to deliberately practice individual skills can be the most effective way to improve a skillset. Indeed, as Andy Matuschak explores in “Implicit practice,” relying on the “implicit practice” provided by a discipline’s usual work can lead to skill gaps we’re not even aware of!

We should be careful taking this too far. David Epstein’s Range argues that breadth is a depth all its own — in many disciplines, the top performers are actually people that explored a range of topics or disciplines and didn’t get 10,000 hours of practice. In particular, he argues that deliberate practice doesn’t work for “wicked problems”, where feedback is delayed or hard to interpret.

Still, for many disciplines, from art to writing to programming to athletics, there are individual skills that are amenable to deliberate practice.

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Digital Garden

Treat a personal website as a work-in-progress wiki, not a reverse-chronological blog.

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“Digital gardening” is a recent movement to treat personal websites more like always-in-progress personal wikis instead of a traditional reverse-chronological blog. That might involve publishing unpolished notes, updating pages when you learn more, linking heavily between pages, distinguishing between different classes of recency, and so on. This site is inspired by the principles of digital gardening, since I intend most pages to be “evergreen” and updated over time, although I don’t take the concept as far as many others do.

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Dunbar’s Number

Stable human social groups are limited to about 150 people.

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Robin Dunbar is a fairly prominent anthropologist — I highly recommend his books Friends and Why Religion Evolved — but he is most famous for his eponymous number, introduced in “Neocortex size as a constraint on group size in primates”. He studied the average group sizes of other social primates and discovered a striking relationship between group size and neocortex size. If you plug humans into the resulting equation, you get “Dunbar’s number” of about 150, which is the maximum size of a stable social group, or how many “close” interpersonal relationships we can have. This has been borne out by repeated studies of human social groups, from militaries to churches to Discord channels. Social groups that grow much bigger than Dunbar’s number need more complicated organizational structures to make up for the fact that most members will not, and indeed cannot, know most other members.

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Eternal September

Communities struggle to assimilate large numbers of new people arriving at the same time.

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Before 1993, the proto-web message boards of Usenet saw a yearly influx of new users around September, when new university students got access to Usenet for the first time. At first, these new arrivals would have poor etiquette, but through a mixture of shaming, mockery, trolling, and simple observation, they would socialize to the standards of Usenet. Around 1993, service providers began providing much larger numbers of users with access to Usenet, leading the forums to become overwhelmed with badly-behaved users (at least, from the perspective of old-timers). That led some to refer to this period as an “Eternal September.”

The broader lesson here is that communities, if they are to remain communities, need time to socialize new arrivals into the culture of the community. Otherwise, they risk becoming a place where strangers mostly interact with strangers, where there is no sense of shared norms and few repeated interactions that would encourage politeness.

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Eyes on the Street

A feeling of safety in urban environments is provided by having many “eyes on the street”.

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What makes a neighborhood feel safe or unsafe? Why did so many of the housing projects of the mid-twentieth century fail so spectacularly?

One of the major arguments of Jane Jacobs’ Death and Life is that neighborhoods feel safe when they are more lively — when there are a wide variety of people going about many different activities at all times of day, providing many “eyes on the street” to deter wrongdoing. After all, most humans are naturally cooperative and willing to step in if something untoward is happening.

Good neighborhoods — like, say, San Francisco’s North Beach — support many uses, from children going to school in the morning to workers in the office during the day to diners eating out in the evening to revelers partying at night. Bad neighborhoods — like, say, San Francisco’s Civic Center — are oriented around a single purpose, like civic administration or corporate offices, and remain desolate at “abnormal” times. The mid-twentieth century housing projects of high modernism, like Le Corbusier’s “tower in a park” idea, were ineffective because they segregated different purposes.

When in a new neighborhood, pay attention to how many “eyes on the street” you can notice.

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Goodhart’s Law

When a measure becomes a target, it ceases to be a good measure.

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We have some property of the world we want to change. It’s difficult, or impossible, to measure, so we instead focus on a proxy that we can measure. But then we’re optimizing exactly the wrong thing, and we end up with nonsensical results!

We should, however, be careful of taking this idea too far. As Cedric Chin points out in “Goodhart’s Law Isn’t as Useful as You Might Think”, more limited instances of Goodhart’s law are solvable via the techniques of Deming’s statistical process control. Additionally, picking some measure has benefits — otherwise, how would we know we’re changing anything at all?

Still, we should be careful which measures we pick and how seriously we take them. We should never pursue optimization for its own sake.

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Illusion of Explanatory Depth

Humans think they understand familiar systems better than they actually do.

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How does a toilet work? A car? A computer? The economy?

For systems we’re familiar with, we tend to rate our understanding highly, but in practice most people can barely explain the basics of how a toilet works. This isn’t necessarily a bad thing — humans are great at compartmentalizing what we need to know and what we don’t. However, it can backfire when we forget that we don’t really understand things that we think we do.

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Magical Number Seven, Plus or Minus Two

Human short-term memory is limited to seven (plus or minus two) “chunks”.

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Extensive research has shown that human short-term memory is limited to about seven items. Importantly, this doesn’t mean seven bits — experts are able to construct higher-level representations or “chunks” that allow them to remember more.

This principle can be applied widely anywhere human cognition is important. For instance, as Loup Vallaint argues, “readability” in software engineering is simply a function of limited short-term memory. Global variables are hard to understand because they take up precious memory space. Composition is better than inheritance because inheritance inherently requires keeping track of multiple interacting parent and children classes.

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Principal-Agent Problems

When an agent takes actions on behalf of a principal, care must be taken to align incentives.

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In social life, it is often the case that one party (the principal) has access to capital or other resources and uses these to hire the services of another party (the agent). However, how does the principal know the agent will actually do as they wish, and how does the agent know the principal will likewise maintain their commitments? This is a fundamental issue that requires careful mechanism design.

One of the most famous examples is stock-option-based compensation for early startup employees in the tech industry. Venture capitalists invest millions of dollars in startups. Why don’t the founders take the money and run? Why do early employees put up with harsh working conditions, when the company can barely afford to pay them? Their primary compensation comes in the form of stock options, which are only valuable insofar as the company itself is valuable, aligning incentives between the funders and the founders. (Tom Nicholas points out in VC: An American History that early-19th-century American whaling expeditions settled on an identical solution.)

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Programming as Theory Building

Programming is the process of building theory of how a problem is solved, not the production of a program.

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Why is it difficult for a new team to take over development of a program? Why does documentation sometimes prove useless for understanding a system?

Peter Naur argues in this paper that programming is often misunderstood — it is not the process of producing an artifact that we call a “program”, but rather it is the process of the programmer developing a “theory” of how a particular problem can be solved using a program. In this view, the activity of “programming” just is developing this theory and then applying it, with the corollaries that:

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Rubber Duck Problem Solving

Talking through a problem in as much detail as possible can help solve it, even if you’re just talking to a rubber ducky.

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One of the best problem-solving techniques from software engineering is to explain the problem and attempted solutions in as much detail as possible. It doesn’t matter whether you’re talking to another software engineer or even another person; you could even use a literal rubber ducky! The important part is to explain the problem in as much detail as possible, because it helps clarify your assumptions and will often reveal overlooked details.

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Scenius

”Scenes” are often more inventive than individuals.

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Scenius is a concept coined by Brian Eno to describe the fact that groups of creative individuals in the same time and place — a “scene”, if you will — are often much more inventive than the individuals themselves. This is arguably the “micro” version of cultural evolution as described above. There are many examples, from literary groups like the Inklings or the Bloomsbury Group, to the invention of modern climbing at Camp 4 in Yosemite, to the flow of conversation at a really good dinner party.

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Spaced Repetition

Memorize anything efficiently by reviewing at spaced intervals.

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Ebbinghaus’ forgetting curves show that the most efficient way to remember something is to test yourself at increasingly-spaced intervals. As a result, with the help of spaced-repetition software and careful choice of flashcards, you can efficiently remember virtually anything you choose to. Moreover, you can use memorized details as building blocks for more complex thoughts, allowing you to learn arbitrarily-complex topics with greater effectiveness.

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Speed Matters

Working quickly is important.

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Working fast means you can get more done per unit time. But as James Somers points out in his essay, there’s two other benefits to working fast:

  1. You lower the activation energy of starting a new project — you’re more likely to start a project if you think it will be quick and effortless.
  2. Because you can get more done in the same time, you get more deliberate practice.

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