There’s a great line from Heather McGaw toward the end of the latest episode of 99 Percent Invisible:
Sometimes … we can start to solve problems that we wish were problems because they’re easy to solve.
Reminds me of an excerpt from Richard Weaver’s book Ideas Have Consequences:
Obsession, according to the canons of psychology, occurs when an innocuous idea is substituted for a painful one. The victim simply avoids recognizing the thing which will hurt. We have seen that the most painful confession for the modern egoist to make is that there is a center or responsibility. He has escaped it by taking his direction with reference to the smallest points. … The obsession, however, is a source of great comfort to the obsessed.
The pinned tweet on my Twitter account at the moment says “Productivity tip: work hard.” It’s gotten a lot of positive feedback, so I assume it has resonated with a few people.
I don’t know how people take it, but here’s what I meant by it. Sometimes you can find a smarter way to work, and if you can, I assume you’re doing that. Don’t drive nails with your shoe if you can find a hammer. But ultimately the way to get things done is hard work. You might see some marginal increase in productivity from using some app or another, but there’s nothing that’s going to magically make you 10x more productive without extra effort.
Many people have replied on Twitter “I think you mean ‘work smart.'” At some point “work smarter” wasn’t a cliché, but now it is. The problem of our time isn’t people brute-forcing their way with hard, thoughtless work. We’re more likely to wish for a silver bullet. We’re gnostics.
Smart work is a kind of hard work. It may take less physical work but more mental work. Or less mental work and more emotional work. It’s hard work to try to find a new perspective and take risks.
One last thought: hard work is not necessarily long work. Sometimes it is, but often not. Hard creative work requires bursts of mental or emotional effort that cannot be sustained for long.
I heard of someone who had a commitment to get rid of something every Thursday. I don’t know anything about how they carried that out. It could mean throwing out or donating to charity a physical object each Thursday. Or maybe it could be handing over a responsibility or letting go of an ambition. It could be a combination, such as getting rid of an object that is a reminder of something intangible that you want to let go of.
This may mean reducing your total inventory of objects or obligations, or it could be simply turnover, making room for new things.
Getting rid of an obligation is not necessarily irresponsible, nor is letting go of an ambition necessarily lazy. Letting go of one obligation to take on another could be very responsible. Letting go of one ambition to pursue another could be a lot of work.
Automation doesn’t always save as much time or effort as we expect.
The xkcd cartoon above is looking at automation as an investment. Does the work I put in now eventually save more work than I put into it? Automation may be well worth it even if the answer is “no.”
Automation can be like a battery as well as an investment. Putting energy into batteries is a bad investment; you’ll never get out as much energy as you put in. But that’s not why you put energy into batteries. You put energy in while you can so you can use some of that energy later when you need it.
Write automation scripts when you have the time, energy, and motivation to do so and when nothing else is more important. (Or nothing is more interesting, if you’re looking for a way to procrastinate without feeling too guilty. This is called “moral compensation.”) You may indeed save more work than you put into writing the scripts. But you also may save mental energy just when you need it.
Suppose it takes you an hour to write a script that only saves you two minutes later. If that two minutes would have derailed your concentration at a critical moment, but it didn’t because you had the script, writing the script may have paid for itself, even though you invested 60 minutes to save 2 minutes.
If your goal is to save mental energy, not time, you have a different strategy for automation. If a script executes faster than a manual process, but it takes a long time to remember where to find the script and how to run it, it may be a net loss. The less often you run a script, the chattier the interface should be.
The same considerations apply to learning third party software. I suspect the time I’ve put into learning some features of Emacs, for example, will not pay for itself in terms of time invested versus time saved. But I’ve invested leisure time to save time when I’m working hard, not to save keystrokes but to save mental energy for the project at hand.
Related post: How much does typing speed matter?
This is a post about letting go of something you think you need. It starts with an illustration from programming, but it’s not about programming.
Bob Martin published a dialog yesterday about the origin of structured programming, the idea that programs should not be written with
goto statements but should use less powerful, more specialized ways to transfer control. Edsgar Dijkstra championed this idea, most famously in his letter Go-to statement considered harmful. Since then there have been countless “considered harmful” articles that humorously allude to Dijkstra’s letter.
Toward the end of the dialog, Uncle Bob’s interlocutor says “Hurray for Dijkstra” for inventing the new technology of structured programming. Uncle Bob corrects him
New Technology? No, no, you misunderstand. … He didn’t invent anything. What he did was to identify something we shouldn’t do. That’s not a technology. That’s a discipline.
Huh? I thought Structured Programming made things better.
Oh, it did. But not by giving us some new tools or technologies. It made things better by taking away a damaging tool.
The money quote is the last line above: It made things better by taking away a damaging tool.
Creating new tools gets far more attention than removing old tools. How might we be better off by letting go of a tool? When our first impulse is that we need a new technology, might we need a new discipline instead?
Few people have ever been able to convince an entire profession to let go of a tool they assumed was essential. If we’re to have any impact, most of us will need to aim much, much lower. It’s enough to improve our personal productivity and possibly that of a few peers. Maybe you personally would be better off without something that is beneficial to most people.
What are some technologies you’ve found that you’re better off not using?
Negative space blog posts
When people sneer at a technology for being too easy to use, it’s worth trying out.
If the only criticism is that something is too easy or “OK for beginners” then maybe it’s a threat to people who invested a lot of work learning to do things the old way.
The problem with the “OK for beginners” put-down is that everyone is a beginner sometimes. Professionals are often beginners because they’re routinely trying out new things. And being easier for beginners doesn’t exclude the possibility of being easier for professionals too.
Sometimes we assume that harder must be better. I know I do. For example, when I first used Windows, it was so much easier than Unix that I assumed Unix must be better for reasons I couldn’t articulate. I had invested so much work learning to use the Unix command line, it must have been worth it. (There are indeed advantages to doing some things from the command line, but not the work I was doing at the time.)
There often are advantages to doing things the hard way, but something isn’t necessary better because it’s hard. The easiest tool to pick up may not be best tool for long-term use, but then again it might be.
Most of the time you want to add the easy tool to your toolbox, not take the old one out. Just because you can use specialized professional tools doesn’t mean that you always have to.
Related post: Don’t be a technical masochist
A uniformitarian view is that everything is equally important. For example, there are 118 elements in the periodic table, so all 118 are equally important to know about.
The Pareto principle would say that importance is usually very unevenly distributed. The universe is essentially hydrogen and helium, with a few other elements sprinkled in. From an earthly perspective things aren’t quite so extreme, but still a handful of elements make up the large majority of the planet. The most common elements are orders of magnitude more abundant than the least.
The uniformitarian view is a sort of default, not often a view someone consciously chooses. It’s a lazy option. No need to think. Just trudge ahead with no particular priorities.
The uniformitarian view is common in academia. You’re given a list of things to learn, and they all count the same. For example, maybe you have 100 vocabulary words in your Spanish class. Each word contributes one point to your grade on a quiz. The quiz measures what portion of the list you’ve learned, not what portion of that language you’ve learned. A quiz designed to test the latter would weigh words according to their frequency.
It’s easy to slip into a uniformitarian mindset, or a milder version of the same, underestimating how unevenly things are distributed. I’ve often fallen into the latter. I expect things to be unevenly distributed, but then I’m surprised just how uneven they are once I look at some data.
Here’s an internal dialog I’ve had several times.
“What will happen when you’re done with this project?”
“I don’t know. Maybe not much. Maybe great things.”
“How great? What’s the best outcome you could reasonably expect?”
“Hmm … Not that great. Maybe I should be doing something else.”
It’s a little paradoxical to think that asking an optimistic question — What’s the best thing that could happen? — could discourage us from continuing to work on a project, but it’s not too hard to see why this is so. As long as the outcome is unexamined, we can implicitly exaggerate the upside potential. When we look closer, reality may come shining through.
Gaussian elimination is systematic way to solve systems of linear equations in a finite number of steps. Iterative methods for solving linear systems require an infinite number of steps in theory, but may find solutions faster in practice.
Gaussian elimination tells you nothing about the final solution until it’s almost done. The first phase, factorization, takes O(n^3) steps, where n is the number of unknowns. This is followed by the back-substitution phase which takes O(n^2) steps. The factorization phase tells you nothing about the solution. The back-substitution phase starts filling in the components of the solution one at a time. In application n is often so large that the time required for back-substitution is negligible compared to factorization.
Iterative methods start by taking a guess at the final solution. In some contexts, this guess may be fairly good. For example, when solving differential equations, the solution from one time step gives a good initial guess at the solution for the next time step. Similarly, in sequential Bayesian analysis the posterior distribution mode doesn’t move much as each observation arrives. Iterative methods can take advantage of a good starting guess while methods like Gaussian elimination cannot.
Iterative methods take an initial guess and refine it to a better approximation to the solution. This sequence of approximations converges to the exact solution. In theory, Gaussian elimination produces an exact answer in a finite number of steps, but iterative methods never produce an exact solution after any finite number of steps. But in actual computation with finite precision arithmetic, no method, iterative or not, ever produces an exact answer. The question is not which method is exact but which method produces an acceptably accurate answer first. Often the iterative method wins.
Successful projects often work like iterative numerical methods. They start with an approximation solution and iteratively refine it. All along the way they provide a useful approximation to the final product. Even if, in theory, there is a more direct approach to a final product, the iterative approach may work better in practice.
Algorithms iterate toward a solution because that approach may reach a sufficiently accurate result sooner. That may apply to people, but more important for people is the psychological benefit of having something to show for yourself along the way. Also, iterative methods, whether for linear systems or human projects, are robust to changes in requirements because they are able to take advantage of progress made toward a slightly different goal.
More linear algebra posts
In describing writing his second book, Tom Leinster says
… I’m older and, I hope, more able to cope with stress: just as carpenters get calloused hands that make them insensitive to small abrasions, I like to imagine that academics get calloused minds that allow them not to be bothered by small stresses and strains.
Mental callouses are an interesting metaphor. Without the context above, “calloused minds” would have a negative connotation. We say people are calloused or insensitive if they are unconcerned for other people, but Leinster is writing of people unperturbed by distractions.
You could read the quote above as implying that only academics develop mental discipline, though I’m sure that’s not what was intended. Leinster is writing a personal post about the process of writing books. He’s an academic, and so he speaks of academics.
Not only do carpenters become more tolerant of minor abrasions, they also become better at avoiding them. I’m not sure that I’m becoming more tolerant of stress and distractions as I get older, but I do think I’m getting a little better at anticipating and avoiding stress and distractions.