The most consequential decision we make about our career is how we spend our time. And, I personally believe that the most fundamental time management question is this….

How should we split the time between learning & immediate productivity?

For example, if you spend two hours today on learning, your productivity for the day will plummet. However, if you spend your entire career just doing and not learning, you will have a lower long-term productivity.

I call this situation the Discover-Develop Dilemma (named by my mentor Eben Pagan). Although, it is academically known as the Explore-Exploit Tradeoff.

The tradeoff in a nutshell is this…

• Time is limited.
• If you explore, you may get insights that help you be more effective. But, you also risk getting no new valuable information. Or you risk not giving yourself enough time to leverage what you’ve learned.
• If you exploit what you already know works, you’re likely to get good, but not optimal results.
• Doing one means that you’re taking away time from the other.

Once you see the Discover-Develop Dilemma in one place. You start to see it everywhere...

• Friendships. Should you spend time with your closest friends or spend time making new friends?
• Books.  Lydia Davis brilliantly captures the dilemma with reading: “I had reached a juncture in my reading life that is familiar to those who have been there: in the allotted time left to me on earth, should I read more and more new books, or should I cease with that vain consumption—vain because it is endless—and begin to reread those books that had given me the intensest pleasure in my past.”
• Business. Should you spend time optimizing your existing products, systems, and marketing or should you try something completely new?
• Food. Should you search for a new restaurant you’ve never been to or eat at your favorite one?

You even see it in this famous quote from Abraham Lincoln:

“Give me six hours to chop down a tree and I will spend the first four sharpening the ax.” —Abraham Lincoln

And in many famous proverbs:

A bird in the hand is worth two in the bush. Better an egg today than a hen tomorrow. The grass is always greener on the other side.

In fact, the earliest known usage of the proverb dates all the way back to 600 BC making the Discover-Develop Dilemma an age-old human concern:

A sparrow in thy hand is better than a thousand sparrows flying.

Given that there is NOT a widely agreed upon rule of thumb for how to manage the Discover-Develop Dilemma, I decided to take things into my own hands.

Over the past few years, I have…

• Explored the 20% Rule for optimum learning from companies like Google, 3M, and Genentech along with leaders like entrepreneur Garyvee and Navy Seal Jocko Willink.
• Written about the 5-Hour Rule as the minimum recommended dosage of learning per week.
• Looked into the relevant psychology research (Marshmallow Experiment) and business research (Innovator's Dilemma).
• Looked at the surprising habits of luminary leaders and innovators and found that they leave a lot of slack time in their schedule.

In this article, I want to take a different angle. I wanted to see what the field of mathematics had to say on the matter.

For nearly 100 years mathematicians have been attempting to find an answer to the Discover-Develop Dilemma. The best explanation of that history comes in the book, Algorithms To Live By.

Many algorithms have been created and tested by probability theorists and AI researchers, and these experiments give insight into what works and what doesn’t.

Ultimately, there are a few simple rules of thumb we can all apply to our life now according to the authors (one of whom directs the Computational Cognitive Science Lab at UC Berkeley)

5 Rules Of Thumb For The Discover-Develop Dilemma

Rule #1: Discover early, develop later

If you are investing in your education and you are learning, you should do that as early as you possibly can, because then it will have time to compound over the longest period. And that the things you do learn and invest in should be knowledge that is cumulative, so that the knowledge builds on itself. So instead of learning something that might become obsolete tomorrow, like some particular type of software [that no one even uses two years later], choose things that will make you smarter in 10 or 20 years. — Warren Buffett Biographer

The authors recommend that you “explore when you will have time to use the resulting knowledge, exploit when you’re ready to cash in.” In other words, this rule of thumb suggests that we should focus our earlier years on learning and our later years on savoring…

“Being sensitive to how much time you have left is exactly what the computer science of the explore/exploit dilemma suggests. We think of the young as stereotypically fickle; the old, stereotypically set in their ways. In fact, both are behaving completely appropriately with respect to their intervals. The deliberate honing of a social network down to the most meaningful relationships is the rational response to having less time to enjoy them.”

Bottom line: When thinking about whether to learn something or not, think about the interval of time over which you’ll be able to use it.

Rule #2: Win-stay, lose-shift

Choose an approach at random. If it works better than the control, stay with the approach. If it doesn’t work as well, then try another approach. Keep repeating. This is known as the win-stay, lose-shift algorithm.

Rule #3: Lean toward discovery over development

Research suggests that the conventional wisdom of focusing on the bird in the hand is wrong. Most people over-focus on developing what already works...

We naturally favor exploitation with its greater certainty of short-term success. Exploration, however, is by its nature inefficient, risky, and maybe even downright scary. Yet without some effort toward exploration, firms, in the face of change, are likely to fail. —Charles O’Reilly & Michael Tushman, authors of Lead and Disrupt: How to Solve the Innovator's Dilemma

The most interesting research on the value of leaning toward discovery over development comes from the Gittins Index. The authors explain…

“A record of 0–0—an arm that’s a complete unknown [imagine a slot machine]—has an expected value of 0.5000 but a Gittins index of 0.7029. In other words, something you have no experience with whatsoever is more attractive than a machine that you know pays out 70% of the time! The index does ultimately converge on 0.5000, as experience confirms that the machine is indeed nothing special and takes away the “bonus” that spurs further exploration. But the convergence happens fairly slowly; the exploration bonus is a powerful force. Indeed, note that even a failure on the very first pull, producing a record of 0–1, makes for a Gittins index that’s still above 50%.”

Later in the chapter, the authors explain the logic behind why we should focus on exploration more than we intuitively feel we should...

“More generally, our intuitions about rationality are too often informed by exploitation rather than exploration. When we talk about decision-making, we usually focus just on the immediate payoff of a single decision—and if you treat every decision as if it were your last, then indeed only exploitation makes sense. But over a lifetime, you’re going to make a lot of decisions. And it’s actually rational to emphasize exploration—the new rather than the best, the exciting rather than the safe, the random rather than the considered—for many of those choices, particularly earlier in life. What we take to be the caprice of children may be wiser than we know.”

Rule #4: Never bet the farm

As a bonus, I’ll throw in two rules of thumb from one of my favorite authors and a world-expert on the topic, Nassim Taleb.

In Taleb's Skin In The Game, he points out the risk of making bets based off of expected return and not on risk of ruin...

Consider a more extreme example than the casino experiment. Assume a collection of people play Russian roulette a single time for a million dollars—this is the central story in Fooled by Randomness . About five out of six will make money. If someone used a standard cost-benefit analysis, he would have claimed that one has an 83.33 percent chance of gains, for an “expected” average return per shot of $833,333. But if you keep playing Russian roulette, you will end up in the cemetery.

Bottom line: Avoid risk of ruin at all costs. It is much better to take lower returns in the short-term if it means surviving in the long-term.

Rule #5: Do small experiments with huge potential pay-offs

Nassim Taleb calls this tinkering: “Let us call trial and error tinkering when it presents small errors and large gains. [This was] voiced by Steve Jobs at a famous speech: 'Stay hungry, stay foolish.' He probably meant 'Be crazy but retain the rationality of choosing the upper bound when you see it.'”

The authors of Algorithms To Live By explain further and recommend having optimism in the face of uncertainty: “By focusing on the best that an option could be, given the evidence obtained so far, these algorithms give a boost to possibilities we know less about. As a consequence, they naturally inject a dose of exploration into the decision-making process, leaping at new options with enthusiasm because any one of them could be the next big thing.”

Conclusion

If you spend your entire day doing things that are proven to work, you're probably over-investing in development. If you keep this up over time, you eventually risk disruption or hitting a major plateau.

If you spend your entire day on discovery, you're likely to never have success as you try something new before your idea or project has time to blossom.

So, clearly there must be a mix.

Most people and companies tend to over-focus on development. So, it's likely a wise bet to put more time into discovery than you currently are. I still stand behind the 20% Rule as a good sweet spot for knowledge works who primarily perform complex, non-routine activities throughout the day.