Study Hacks Blog

A Focused Digression

David Brooks’s most recent column ends up on the subject of geopolitics, but it begins, in a tenuous but entertaining fashion, with a long digression on the routines of famous creatives (which Brooks draws from Mason Currey). For example…

• Maya Angelou, we learn, was up by 5:30 and writing by 6:30 in a small hotel room she kept just for this purpose.
• John Cheever would write every day in the storage unit of his apartment. (In his boxer shorts, it turns out.)
• Anthony Trollope would write 250 words every 15 minutes for two and a half hours while his servant brought coffee at precise times.

To summarize these observations, Brooks quotes Henry Miller: “I know that to sustain these true moments of insight, one has to be highly disciplined, lead a disciplined life.”

He then offers his own more bluntly accurate summary: “[Great creative minds] think like artists but work like accountants.”

Or, to put it in Study Hacks lingo: “deep insight requires a disciplined commitment to deep work.”

Keeping these insights in mind, now consider the following article posted on Time.com the day before Brooks’s column: 9 Rules for Emailing From Google Exec Eric Schmidt.

Read more

Obsessing About Selection

I’m currently trying to solve a fun problem that’s captured my attention and refuses to relent. Here’s the basic setup:

• A collection of k devices arrive at a shared channel. Each device has a message to send.
• Time proceeds in synchronized rounds. If more than one device tries to send a message on the channel during the same round, there’s a collision and all devices receive a collision notification instead of a message.
• The devices do not know k.

In this setup, a classic problem (sometimes called k-selection) is devising a distributed algorithm that allows all k devices to successfully broadcast in a minimum number of rounds. The best known randomized solutions to this problem require a*k rounds (plus some lower order factors), for a small constant a > 2.

What I am trying to show is that such a constant is necessary. That is: all distributed algorithms require at least b*k rounds for some constant b bounded away from 1 (and hopefully close to 2).

The Dash Method

What I’ve noticed in my thinking about this problem over the past week or two is that at the beginning of each deep work session, I’ll typically come up with a novel approach to attempt. As I persist in the session, however, the rate of novelty decreases. After thirty minutes or so of work I tend to devolve into a cycle where I’m rehashing the same old ideas again and again.

I’m starting to wonder, therefore, if this specific type of deep work, where you’re trying to find a creative insight needed to unlock a problem, is best served by multiple small dashes of deep work as oppose to a small number of longer sessions.

That is, given five free hours during a given week, it might be better to do ten 30-minute dashes as oppose to one 5 hour slog.

Read more

Lounging in Lauinger

Today I spent the morning in the library. As often happens, I arrived with a specific book in mind, but soon a long trail of diverting citations lured me in new directions.

I’m a sucker for libraries.

One such happy discovery was the book, The New Faculty Member, by Robert Boice, a now emeritus professor of psychology at Stony Brook. This book summarizes the findings of a multi-year longitudinal study in which Boice followed multiple cohorts of junior professors, at multiple types of higher education institutions, from their arrival on campus until their tenure fate seemed clear.

(He also wrote a non-academic version of this book called Advice for New Faculty Members, which I haven’t read, but assume is similar in its conclusions.)

I was particularly drawn to his chapter on research productivity. It turns out that Boice hounded his subjects on this topic year after year. He didn’t trust self-estimates of work accomplished and instead required the young professors to produce newly written pages to verify progress.

After four years, only 13% of these professors had produced enough (and had good enough teaching evaluations) to make tenure seem highly probable. Here are some of the main differences Boice identified in the research habits of these “exemplary young faculty” as compared to their peers:

Read more

The Temporary Plan

As I’ve revealed in recent blog posts, there are two types of planning I swear by. The first is daily planning, in which I give every hour of my day a job. The second is weekly planning, where I figure out how to extract the most work from each week.

These are the only two levels of planning that I consistently deploy.

But there’s a third level that I turn to maybe two or three times a year, during periods where multiple deadlines crowd into the same short period. I call it (somewhat blandly, I now realize) a temporary plan.

A temporary plan is a plan that operates on the scale of weeks. That is, a single plan of this type might describe my objectives for a collection of many weeks.

When a lot of deadlines loom, I find it’s necessary to retreat to this scale to ensure things get started early enough that I can coast up to the due dates with the needed pieces falling easily into place. If I instead planned each week as it arose, there is too much risk that I would find myself suddenly facing a lot of uncompleted work all due in the next few days!

Logistically speaking, I typically e-mail myself the temporary plan and leave it in my inbox. My general rule is that if a temporary plan is in my inbox while I’m building my weekly plan, I read it first to make sure my weekly plan aligns with the bigger picture vision.

A Temporary Plan Case Study

To help make this strategy more concrete, let’s consider a temporary plan I developed last spring to make sure that the papers I was working on for a May deadline would come together in time while I still made progress on some other efforts that also had looming deadlines. I replicated this plan below. (I added my commentary in square brackets):

Read more

Shallow September

I track my deep work hours using a weekly tally, so I have a good sense of how my commitment to depth varies over time. A trend I’ve noticed is that my deep work rate hits a low point around this time of year.

The obvious explanation is that the start of the fall semester adds extra time constraints. But I don’t think that’s the whole story. My deep work tends to increase as the fall continues, even though my teaching commitments also increase during this period (i.e., once there are problem sets and exams to grade).

In thinking about this mystery I’ve begun to better understand a crucial but often ignored aspect of working deeply on important things: the necessity of clarity.

Read more