The humble calendar is one of the most powerful tools on the planet. It powers all human coordination and gives shape to time itself. No other human creation is more global. It transcends the bonds of geography. Time differs from zone to zone. It’s 8:55 pm in Japan. It’s It’s 11:55 am in the UK. But it’s Tuesday throughout.

We all share a calendar. And if you’re a knowledge worker, you share the common problem of scheduling. This has become more pressing in the past twenty years. During the industrial age, productivity was largely measured in labor and output on the factory floor. Time was measured in eight-hour shifts. No one managed their own workplace calendar. They showed up, did the work, and went home.

But productivity is measured differently now. The hallmark of the factory, performance management, has reached the office. And the gig economy. And logistics. Operations. Hospitals, retail, sales, marketing, and everywhere else. To meet our metrics, we organize our work in new and unique ways.

But it’s all just another version of the calendar. Gantt charts, task lists, kanban boards, trello cards, OKRs, etc. Some have beautiful interfaces. Some are fun to use. All are a riff on the calendar and say the same basic things: who does what by when.

Who Does What By When?

That question powers all organized work. I’ve learned that it also powers our technology. In the computer science field, there is a deep, dedicated literature on scheduling science. Because computers have to operate on a schedule. Just like us.

And also like us, computers can do a lot of things. Just not all at once.

Computers have to switch between tasks all the time. Some are “heavy” tasks, like 3D rendering. Many are “light” tasks, such as word processing. We humans do the same. We try to do a “heavy” task like cooking while engaging in a “light” task such as talking on the phone.

Computers keep certain operations active in the “background”. You can see them in a thing called the “task manager” on the PC. We keep a constant background operation going, too. It’s called the subconscious.

Between the two of us, the computer and the human, there is a constant tension between our ever-expanding abilities and the constant limitation of resources. Neither man nor machine will ever be able to do it all at the same time.

So schedules matter. Which gets us back to our question: who does what by when?

A Few Imperfect Answers.

Computer scientists have the best answers on many things, it seems. But the best answers are seldom perfect. When it comes to scheduling, for either humans or machines, the best we can devise is a series of rules or algorithms to structure our decision-making. This structure, if used properly, can be an operating system for our daily work. I’m convinced it can make us tremendously more capable thanks to the fantastic book Algorithms to Live By by Brian Christian and Tom Griffiths.

So again, there is no perfect schedule. There is no perfect example of investing our time the right way. But there is an underlying logic that remains consistent and can help:

If you have a clear goal, you can optimize your schedule. If you don’t, you can’t.

Most of us don’t have much of a clear goal when we start our day. And we certainly don’t design our calendars around it. It’s rare for me to design my schedule to determine what I will do, as an itinerary; instead I design my schedule to determine what I won’t do for others.

Specifically, I tend to use my schedule as a way of telling people when I won’t be available for their somewhat-arbitrary, slightly-overlong meeting because I’ll be in a competing meeting that is equally-arbitrary and equally-overlong.

If you think about it, the bulk of our schedules function in this way. By showing pre-arranged commitments, it helps us manage incoming requests.

There are a lot of incoming requests. The average knowledge worker is overwhelmed. Telephone calls, emails, IMs, text messages, notifications, reminders, documents in the mail, the occasional real life human.

These requests are what diminish our productivity. Never mind the other distractions we face. It is the requests of others that tax our resources.

So what’s the goal? What’s the answer to all these problems? Here is one strategy I deeply admire:

Minimum Acceptable Response

In 1851, French writer Nicolas Chamfort wrote a great line attributed to a conversation he had with one M. de Lassay, who said:

… we should swallow a toad every morning, in order to fortify ourselves against the disgust of the rest of the day.

The idea here drives a lot of successful habits. Do you have a difficult task today? Do it first thing that morning. Your willpower is probably strongest in the morning and your energy reserves are likely full. Be clear that this is your goal and the morning is when you’ll start.

So if your goal is complete a difficult task, dedicate your entire morning to it.

Or perhaps your entire day. You get the idea. Prioritization is key.

But here’s where things get tricky. Prioritization means mastering the art of “No”. This is necessary in order to defend your time and energy until the supremely important task is done. Such is the wisdom of Greg McKeown’s book Essentialism (book review here). It’s also the wisdom of computer scientist Peter Denning.

Denning is a pioneer in the field and the foremost architect behind the ways our computers multitask. This is a bigger deal than it may sound. Multitasking is essential to a computer’s utility. We wouldn’t have adopted these machines so quickly without this capability.

Just imagine it. What if your computer told you “no”. As in, “No, I can’t do that because you already have me doing all these other things.” That does happen occasionally, in the form of lag. And when it does, we hate it. Denning and others know this and they’ve done a lot of incredibly complex, innovative things to avoid that experience as much as possible. This is where scheduling science comes into play.

Thanks to their work, a computer says rarely says “no” to you. If it does, it’s usually in the form of a system crash. But again, that’s rare.

Instead, even when a computer is “eating the frog” of some difficult task, it appears to operate in true simultaneous fashion. You should know, however, that this is an illusion. Kind of.

For example, imagine you issue a computationally-intensive command to a computer and then try to switch to another program while that process “runs in the background”. There is a good chance that the computer will play with your perceptions in order to maximize its resources. Our authors explain it as such:

… operating systems programmers have turned to psychology, mining papers in psychophysics for the exact number of milliseconds of delay it takes a human brain to register a lag or flicker.

Why would programmes worry about this? So that they can determine the precise amount of precious time the computer can delay a response to your command without you knowing it. This is the machine’s way of not doing what it’s told for as long as it can. Just like a child.  

In other words, when a computer is tasked with an intensive operation (a’la “eating the frog”), it will say “no” to your other commands for as long as humanly tolerable. It will focus all resources on the intensive task within a minimum acceptable limit.

For users, the minimum acceptable limit is milliseconds. Computer scientists probably wish it were longer.

What about our interactions from a person-to-person basis? What is the minimum acceptable limit that we can delay an incoming request? It varies of course. It’s one range for our bosses; it’s another range for customers; it’s something different for friends.

Whatever the case, lowering your expected responsiveness increases your productivity.

This is where strategies like auto-reply email can help. Such tools can allow you to shift all emails to a “slow to reply” sort of message. This establishes a minimum acceptable limit for when you’ll switch from your high priorities to your low priorities. Such tools are an artful way of shifting from “no” to “not yet”.

Josh Spector has a great example of this. Here’s a link to his article.

Diana Urban has another good article on this strategy. Particularly with the distinction between “out of office” and “slow to reply” messages.

It’s a few steps closer to the technique of timeboxing. You set the minimum amount of time you’ll spend “eating the frog” which, in turn, establishes the minimum amount of responsiveness that you’ll manage for other people’s requests. For more on timeboxing, see Matthias Orgler’s article on the concept.

But our authors of Algorithms to Live By capture the idea best:

The moral is that you should try to stay on a single task as long as possible without decreasing your responsiveness below the minimum acceptable limit. Decide how responsive you need to be—and then, if you want to get things done, be no more responsive than that.

This is such a great strategy in the workplace. So much important work needs to be done and yet we seldom do it because we’re constantly attempting to switch to every request, every need, every incoming command.

It’s why some professors and managers have “office hours” where you can come in with your requests at certain discrete times. Not before. Not after. It’s also why Steve Brophy recommends in this article that you batch your emails.

It’s why I have a calendar in the first place: to block out time and show others what isn’t available to them.   

Distractions? We have them. But the ones that come from other people are no different than the ones a computer must deal with when they get our constant Alt+Tab requests. If the requests are light, and we don’t have intensive work to do, there are many ways of dealing with the volume. Getting Things Done is the best method. Along with FIFO. But when there is real work, real processing, to be done, the solution is simple:

We must lower our responsiveness.  

It’s not about getting to work early or staying late to finish that report. It’s not even about saying “no” to the incoming requests. It’s about finding and maintaining a minimum level of responsiveness (how long can I make you wait?) and saying “not yet” within those terms.

That’s milliseconds for computers.  

It’s a lot longer for us. Days, perhaps. Or so I hope. The art of doing great work is to extend that timeframe as long as possible.

I’m going to answer my emails sometime next month.

Photo by Bartosz Kwitkowski on Unsplash