You open your phone to study and see a mess: 200 screenshots, 40 notes, five half-finished playlists, and a dozen tabs about “how to learn faster.” It feels random and overwhelming. Yet some parts repeat—your class schedules, the way you name files, your favorite study playlist order. Murray Gell-Mann and Seth Lloyd suggest a simple way to think about this mix of pattern and noise: separate what’s regular from what’s random, then measure both. In their view, “information” isn’t just messages or data—it’s also the uncertainty you still have. That’s why the same math that measures entropy in physics also measures surprise in messages, and in everyday choices like a coin flip. When all outcomes are equally likely, that uncertainty is highest; when you’ve seen the answer, uncertainty drops to zero.

Here’s the trick. First, describe the regular parts of your world as compactly as possible—the rules, templates, and habits you actually use. In the authors’ terms, that compact description is called effective complexity, and it’s the length of the shortest “program” that captures your recognized regularities. Think of it like the few lines you’d write to describe your note-taking system or playlist rules. Second, add a number for what’s left over—the unpredictable bits you can only label with probabilities. Add those two numbers and you get total information: “regularities length” plus “randomness left.” That sum is what it really takes to describe your situation. When you compare different ways of spotting patterns, the best choice is the one that makes the total information smallest, and then, given that, makes your regularities description as short as possible within a reasonable computing time. In plain terms: pick patterns that both explain a lot and are easy to use.

What does that look like on a busy day? Suppose your lecture notes often follow the same outline. Writing a short template (headings, quick symbols, highlight colors) encodes those regularities. That’s your effective complexity. The unexpected parts—off-syllabus examples, a surprise quiz—are the random remainder. Your goal is to choose a template that keeps the total low: simple enough to apply fast, specific enough that less is left to chance. The authors demonstrate the same logic with coin-toss sequences and even with recognizing the digits of π: a concise, insightful description can transform what initially appears random into something far easier to comprehend. In the π case, once you spot the rule, you trade randomness for a slightly richer description, and the overall effort drops. In study life, that’s like replacing “save everything and hope” with a tiny rule set that makes new material land in the right place automatically.

There’s also a helpful mindset for uncertainty itself. When you don’t know details, don’t pretend you do; assign fair weights and move on—what statisticians call “maximum entropy.” That keeps your randomness honest while you continue to refine the patterns. In practice, shrink your regularities until they’re easy to compute (templates you can apply quickly), and let the leftovers be labeled as “to triage later.” As Gell-Mann and Lloyd argue, any process that lowers total information makes a system easier to understand and control, whether it’s a physics model or your week. So next time your phone feels like chaos, write the tiniest rule that explains most of your flow, and let chance have the rest. You’ll spend fewer bits on confusion—and more on getting things done.

Reference:
Gell-Mann, M., & Lloyd, S. (1996). Information measures, effective complexity, and total information. Complexity, 2(1), 44–52. https://doi.org/10.1002/(SICI)1099-0526(199609/10)2:1<44::AID-CPLX10>3.0.CO;2-X

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