Computational thinking is an approach to solving problems and making decisions that allows you to leverage data and technology to augment your capabilities. Although the concept was popularized by Jeannette Wing, former head of computer science at Carnegie Mellon University, it is really just a form of “first principles thinking,” a technique that has been around since the time of Aristotle.
Computational thinking is a structured approach to problem-solving, which ultimately allows you to come up with a solution that can be effectively carried out by computers, people, or—typically—a combination of both.
A good starting point if you want to learn how to be a more effective computational thinker is to understand the difference between reasoning by analogy and reasoning from first principles.
Story vs. Statistics
Analogies are a particular type of inductive argument in which perceived similarities are used to imply some further similarity. They can be a powerful way to teach a lesson because they engage the narrative, story-driven part of our brain. But that also means they are potentially limiting, as the perceived similarity between two situations can hide more fundamental truths, which when thoroughly explored might lead you to radically different conclusions.
Algorithmic leaders take a different approach to evaluating problems and making decisions. They tend to approach strategic issues in a more structured way that allows them to use data and computation to augment their problem-solving capabilities. That’s where a traditional Hollywood studio executive might differ from someone who works on the content production team at Netflix. Analogies are not enough, and they can be misleading if you don’t have the data to support the purported similarities. As simple as that sounds, it goes against much of 20th century management training, which traditionally coached leaders to reason by analogy rather than first principles.
Management students analyze business cases and prepare arguments based on what other companies or leaders have done in similar situations. For example, fans of Harvard Business School professor Clayton Christensen take case studies—like the rise of minimills in the 1970s, which disrupted the steel business—as proof of larger strategic trends. Minimills initially made cheap concrete-reinforcing bars known as rebar. Larger competitors, like U.S. Steel, were not fazed by this development—until the minimills used their success to navigate their way into the production of higher-value products.
Intel’s CEO at the time, Andy Grove, interpreted the minimill analogy as a warning to not cede the bottom of the market, and so he started promoting Intel’s low-end Celeron processor more aggressively to buyers of cheaper computers. But the analogy failed Grove and Intel in an important way: They completely missed the rise of the smartphone market. Intel’s real threat was in fact Arm, a tiny British chip design company with a market cap that, for most of the pre-iPhone era, was smaller than Intel’s marketing budget. When Steve Jobs asked Intel if they wanted to fabricate a chip based on a design licensed from Arm for his new iPhone, Intel refused. It didn’t want to be in the low-margin business of providing phone CPUs, and it simply didn’t realize just how big the smartphone revolution would be.
Building a Better Rocket
Reasoning by analogy alone not only is dangerous when it comes to strategy, but also can create confusion when it comes to culture and leadership. All too often, we try to teach leaders concepts like team-building by making them take part in a tug-of-war or a “trust- fall” (a trust-building game in which a person deliberately allows themselves to fall, relying on someone else to catch them).
Don’t get me wrong. Whether it be paintball or making houses with playing cards, company social activities can be fun and an excellent opportunity to interact and get to know each other. But do these activities, built on analogous thinking as they are, also reinforce a way of thinking that is counterproductive?
If you are trying to secure support for a breakthrough product, how can you obtain funding when your superiors demand examples of similar successful products in the market? When a marketing team develops a campaign that replicates the approach of the No. 1 player in the market, is this good work or poor judgment?
When your engineering team tells you that the brilliant design for a new device can’t be built at a reasonable cost, do you merely accept what they say? That last question is particularly relevant, as it is part of the Space Exploration Technologies (SpaceX) story.
When Elon Musk set out to acquire his first rockets with a view to taking people to Mars one day, he faced a seemingly insurmountable problem: cost. The cheapest U.S. rockets that could do the job cost $65 million each, and he would need two. He therefore went to Russia to find out whether he could buy some repurposed intercontinental ballistic missiles that the Russians were apparently selling to any interested buyer. Even without nuclear warheads attached, the price of the Russian rockets was between $15 million and $20 million each. So how did Musk, six years after starting SpaceX, manage to put his first rocket, Falcon 1, into orbit at a price to his customers—not his cost—of $7 million?
Musk used first principles thinking. Aristotle defined a first principle as “the first basis from which a thing is known.” First principles thinking is therefore the art of breaking a problem down to the fundamental parts that you know are true and building up from there.
As he flew back from his meeting with the Russians, Musk started to wonder what a rocket was actually made of. If you were to break a rocket down into all its constituent pieces, how much would those cost?
After some research, he discovered rockets were principally composed of aerospace-grade aluminum alloys, plus some titanium, copper, and carbon fiber. When he investigated the value of those materials on the commodities market, he realized that the actual cost of materials was only around 2% of the typical price of a rocket. Musk decided that by assembling the right team and applying the latest technology in design and manufacturing, he could make a much cheaper rocket from scratch. That was the beginning of not only SpaceX but also a new era in commercial spaceflight.
This is not the only example of Musk’s using first principles thinking. When he was advised that it was impossible to cost-effectively use batteries to store energy for homes and cars, he once again broke the problem down into smaller parts. He reasoned that the material constituents of batteries might differ from their assembled cost. If you were to buy carbon, nickel, aluminum, polymers, and a steel housing on a metals exchange, what would that cost? Much less, it turned out, than people assumed.
To successfully reason from first principles, you first need to identify your current assumptions and then break them down into their fundamental truths before exploring how you might create new solutions from scratch.
Now that you understand the basic approach of first principles thinking, it is time to use it in a way that will allow you to work more effectively with algorithms and AI. Knowing how to program a computer is not as important as knowing how to think in a way that allows computers to help you be more effective. Rather than artificial intelligence, think of it as augmented intelligence. Or simply: computational thinking.
Like reasoning from first principles, computational thinking involves taking a problem and breaking it down into a series of smaller, more manageable problems (decomposition). These problems can then be considered in the context of how similar problems might have been tackled in the past (pattern recognition). Next, you can identify simple steps or rules to solve each of the smaller problems (algorithms), before considering what the bigger picture might be (abstraction).
You can express these principles as a series of steps, applicable to any problem:
1. Break a problem into parts or steps
2. Recognize and find patterns or trends
3. Develop instructions to solve a problem or steps for a task
4. Generalize patterns and trends into rules, principles, or insights
What makes computational thinking, as opposed to mathematical or theoretical thinking, useful in the real world is that it incorporates practical constraints. When facing a particular challenge, an algorithmic leader might consider how difficult a problem is to solve, the best way to solve it, how long the available computing resources might take to do it, and whether an approximate solution might be good enough. From this perspective, computational thinking is about reformulating seemingly intractable problems into ones that we know how to solve by reducing or transforming them in some way.
You might use computational thinking to work out where your best employees come from, to determine the real reason your customers don’t renew their contracts, why there are constant breakdowns on your production line, or even which of your marketing strategies is actually working. Computational thinking is simply a structured, iterative approach that takes into account all the data now available for us to hone our judgment calls.
However, aside from making you a smarter leader, computational thinking has the potential to change the way we do things at a bigger scale, and in doing so, transform entire industries.
Mike Walsh is the author of the forthcoming The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You, from which this article is excerpted. Walsh is the CEO of Tomorrow, a global consultancy on designing companies for the 21st century.
