From Tortoise Shells to Netflix: The Surprising Ancient Roots of Recommendation Syste

If you think recommendation systems are just a modern invention, think again! While many of us associate them with Netflix suggestions and Amazon product recommendations, Recommendation Engines by Michael Schrage digs much deeper into their roots. In the first two chapters, Schrage takes us back centuries—well before the digital age—to explore how people have been seeking (and giving) recommendations in all sorts of ways. One of the most fascinating examples he shares is the story of Fu Xi from ancient China.

This is my second book of the year, and I’m excited to share it with you!

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Fu Xi and the Tortoise Shell: An Ancient Algorithmic Tradition

In Chinese mythology, Fu Xi is credited as the first emperor and one of the legendary creators of Chinese civilization. According to the tales, he noticed mysterious patterns on the back of a tortoise, which inspired him to create trigrams—the foundation of the I Ching (or Book of Changes). You might be thinking: “What does that have to do with recommendation systems?”

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Well, Schrage calls the I Ching “history’s first hexadecimal, algorithmically driven recommendation system.” Why? Because each hexagram (formed by combining two trigrams) delivers guidance or advice tailored to the seeker’s question. By throwing coins or yarrow stalks, the user produces a specific hexagram that interprets their situation and suggests a course of action.

It’s fascinating to see how these ancient methods mirror our modern quest for customized suggestions:

“The I Ching doesn’t just provide advice; it fosters reflection and self-discovery. It invites users to explore their inner worlds while navigating external uncertainties.”

Even though we’ve swapped tortoise shells and yarrow stalks for digital code and big data, the basic principle remains the same: people look for direction, and a ‘system’ (algorithm, tradition, or otherwise) provides it.

Beyond Fu Xi: Other Early Forms of ‘Recommending’

In the early chapters, Schrage reminds us that Fu Xi and the I Ching aren’t the only historical examples of recommendation systems. From oracles in ancient Greece to astrology in many cultures, people have long sought guidance outside themselves—sometimes from divine forces, sometimes from learned experts, and sometimes from systematic rules or patterns. Here are a few early forms that Schrage mentions:

Oracles and Prophecy

In ancient Greece, seekers flocked to the Oracle of Delphi for cryptic yet influential predictions. While it wasn’t exactly an algorithm, it’s another instance of people relying on external structures for decisions.

Astrology and Star Charts

Throughout history, many societies have believed that the positions of the stars could reveal truths about individuals and the decisions they should make. Think of this as a celestial “recommendation system,” guiding rulers and commoners alike.

Rituals and Runes

From Celtic runes to Viking traditions, many cultures have used symbolic systems to interpret fortunes and give direction. These might seem mystical at first, but if you look closer, they follow set patterns, not unlike a modern algorithm.

By connecting these practices with today’s recommendation engines, Schrage highlights a fundamental human need: when we’re facing uncertainty or overwhelming choices, we turn to patterns—be they technological or spiritual—to help us decide what to do next.

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From Ancient Rituals to Modern Algorithms

The leap from tossing coins in the I Ching to using AI on Netflix may feel huge, but Schrage draws parallels between the two. Both rely on a set of rules or formulas to deliver personalized guidance:

Trigrams and HexagramsI Ching hexagrams are like the “code” that interprets your question and context.

Algorithms and Data Modern recommendation systems process vast amounts of data—like what you’ve watched, listened to, or clicked—using complex algorithms to figure out what you might want next.

In both cases, there’s a give-and-take between the user and the system. With the I Ching, your throw of coins determines the hexagram’s advice; with a streaming service, your watch history and preferences guide the algorithm’s suggestions.

The Timeless Human Desire for Guidance

What really ties ancient and modern recommender approaches together is our consistent desire for reassurance, clarity, and direction in a chaotic world. Whether through mystical divination or data-driven algorithms, we’ve always sought—and valued—systems that help us answer life’s big and small questions:

What should I watch tonight?

Should I accept this job offer?

Is it time to start a new project or invest in a new market?

Just like ancient divination methods, modern recommendation engines do more than spit out advice. They spark reflection, help us narrow our options, and—at their best—encourage us to learn more about ourselves.

So, Were Recommendation Systems Born 100 Years Ago?

Nope! According to Schrage’s exploration, their origin goes way back—thousands of years, really. Our current digital platforms might be built on cutting-edge technology, but the underlying concept has been around for a very long time. In other words, recommendation systems are as old as human curiosity itself.

Why Does This Matter?

By tracing the origins of recommendation systems to ancient practices, Schrage challenges us to see modern algorithms in a broader context. Far from being just “tech tools,” they’re part of a longstanding human tradition of seeking guidance. This perspective pushes us to think about:

Ethics and Responsibility: If people grant so much trust to recommendation systems, how do we design them ethically?

Human-Centered Design: Just as oracles were meant to serve communities, modern recommenders should serve and respect users.

Future Innovations: Understanding these deep roots can inspire new ideas, blending old wisdom with new technology.

Final Thoughts: It’s in Our Nature to Seek Guidance

So the next time Netflix queues up a new series or your favorite shopping site suggests a product you never knew you needed, remember: the urge to look outside ourselves for guidance has been with us for centuries. Recommendation Engines by Michael Schrage is a fantastic read if you want to explore how we got here—and where we might be headed next.

P.S. If you enjoy exploring how technology shapes our decisions, I also recommend checking out Eli Pariser’s The Filter Bubble and Hannah Fry’s Hello World. They offer different angles on how algorithms are changing our lives—for better or for worse.

Have you come across any other ancient or unexpected origins of modern tech? Drop a comment or reach out—I’d love to compare notes and hear your thoughts!

And by the way, this is the second book I’ve read this year—I can’t wait to share more with you.