Starting a Not-So-Nerdy Comp Sci Journey
If you're like most people I know, your days are a whirlwind of back-to-back Zoom calls, overflowing inboxes, OCD inducing red notification circles on too many apps, that nagging "what if" loop that jars you awake at 2 a.m. wondering if you researched that report enough, constant context-switching when you need a solid block of time, or perhaps you wonder if you should've hired that other candidate instead. Sound familiar? You're not alone. In a world that's equal parts opportunity and overload, we’re always looking for tools that cut through the noise—reliable hacks for sharper calls under pressure. What if the tools aren’t what you think they are?
Enter Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian and Tom Griffiths (2016). This isn't your dusty Comp Sci textbook; it's a well laid out, informative bridge between silicon smarts and our squishy human reality. Brian Christian (a tech journalist) and Tom Griffiths (a computational cognitive scientist) unpack numerous algorithms from computer science—think sorting routines and probability updates—and show how they have been used to address our messiest real-life dilemmas. We, as a people, have often had to use our “gut feeling” to make decisions; sometimes our experience helped, sometimes it didn’t. Sometimes it really didn’t! When the right questions were asked and computer science got involved, it became clear that life is an algorithm after all! And why fight it when you can hack it?
As we dive into this series, there are several things I’d like us to keep in mind: the implications and risks of “life is an algorithm”, if you will.
Empowerment Over Paralysis: If life is an algorithm, chaos isn't random. This makes it a “solvable” routine with inputs (your choices), outputs (outcomes), and loops (habits, regrets). Implication? You can stop fighting the "what ifs" and start debugging. Combat FOMO (fear of missing out), update hunches mid-pitch, engage in efficient searching, reclaiming time for your actual priorities, and spend your resources well.
Uncertainty as Feature, Not Bug: Algorithms thrive in the unknown (think Monte Carlo simulations sampling chaos). We are invited to lean into the tension that life's "maybe" is a feature, not a bug; it's the space for iteration. How do we make predictions without all the details? When is “good enough” good enough? How to ensure your message is being communicated in the noise?
Human + Machine Harmony: We are “squishy”. As Algorithms to Live By seems to appreciate, we need to acknowledge that beyond fact and “simple” logic, emotions create edge cases, biases generate off-by-one errors, and our experience overrides the evidence. The implication? We're not robots optimizing blindly; we're hackers tweaking everyday situations for joy (e.g., a spontaneous date night).
Acknowledge areas of Improvement: Algorithms also highlight incremental growth. Every algorithm was only formalized and developed with years of study, experimentation, and practice. They started with an hypothesis, it was tried, tested, revised, debated, and eventually "proved" scientifically -- where possible! The process continues with old and new problems!
Don’t lose the humanity: We do have to be very careful, though. It’s easy to run the risk of over-simplifying—life's not purely code; serendipity and soul defy loops. But that's the beauty. The fact that life is an algorithm humbles us, reminding us to hack with humanity, not replace it.
“May the odds be ever in your favour”: The implication is that they may not be – life doesn't always seem fair. Even the best algorithm doesn’t guarantee the outcomes we want, only that it was the best chance to have the best outcome. Even a 95% success rate, still means that you can expect that 1 out of 20 attempts won’t be successful over the long term.
Those implications have sparked my interest in pursuing this series. Over the next several weeks, we'll dive into the book's chapters, distilling these concepts into actionable intel tailored for life at both work and home. The goal? Grasping the key algorithms allows us to refine decisions amid uncertainty, lean into the tension of "maybe" without paralysis, and trace how ideas evolve. We can move confidently from raw hypothesis to a polished heuristic, from science into "real life". We'll ground it all in realistic applications, beyond the bare facts: the regrets that linger, biases that blind, and the quiet momentum of "good enough" that actually moves the needle.
The Road Ahead: Our Path to Algorithmic Wins
This series unfolds from solo survival hacks to scaled-up synergy. We’ll work on building our toolkit step by step. We'll keep the main post concept-focused but round out each post with a "Bringing it Home" section where we’ll attach a bite-sized application for family dinners, date nights, or that elusive downtime, ensuring there’s a way to make use of the concept in “life”, not just work or in theory.
Here's the proposed map (light spoilers ahead and subject to change):
Post 1: The Search for the "One"
(Chapters 1 & 2: Optimal Stopping & Explore/Exploit)
When to stop hunting and start committing
Post 2: Taming the Chaos
(Ch. 3-5: Sorting, Caching, Scheduling)
Sorting your inbox apocalypse and scheduling sanity
Post 3: Seeing Through the Fog
(Ch. 6-7: Bayes & Overfitting)
Gut-check upgrades via probability and patterns.
Post 4: The Art of Approximation
(Ch. 8-9: Relaxation & Randomness)
When “good enough" is good enough
Post 5: Connections & Complexities
(Ch. 10-13: Networking, Game Theory, Linear Time, Dynamic Programming)
Scaling through networks and negotiations
Conclusion: Using Algorithms for Incremental Wins
A handy cheat sheet, plus experiments to keep the momentum rolling.
As we go through each post we’ll bring in some gems from the book, some real-life examples, mine included (spoiler: I've botched my share of Bayesian hunches), and maybe even drop in some quick challenges into your week, as a test-drive .
These algorithms aren't blueprints for flawless execution and perfected outcomes, but are gentle nudges toward the next iteration. We’re seeking progress, not perfection. It’s a reminder that even in the code of chaos, every tweak counts, every loop refines, and every "good enough" opens the door to something sharper. We’re moving in the right direction. One hack at a time, we keep Aiming Up!
