Resilience, Algorithms, and Humane Wisdom
You know that feeling when life hits all at once? Decisions keep coming, some clear, some murky. Predictions you made last month look shaky now. Relationships strain under the weight of unspoken expectations. Notifications don't stop, and neither does the quiet question: Am I handling this right? Could there be a better way to handle this?
If your life was to be a book or movie, make it an adventure. So, be the hero! That's the decision we have to start with; and right in the middle of the storm, doing our best to stay upright.
The good news is you don't have to invent every answer from scratch. People have walked similar paths before, and they've left behind guides: algorithms that have been battle-tested in real-world situations and, in our constant quest for assurances, scientifically validated.
The book Algorithms to Live By by Brian Christian and Tom Griffiths is a great tool for this journey. It started back in Episode 73, when I invited anyone who felt buried under daily chaos to look at life through a computational lens—without turning into a robot. That invitation still stands.
These aren't shortcuts to perfection. They're reliable ways to move forward when everything feels overwhelming. The catch? Even the strongest process can't promise you'll always win. Life has too many variables. The algorithms give you the best shot at good decisions, but outcomes remain partly out of our hands. Accepting that has been one of the most freeing things I've learned from this whole series.
We began with solo struggles. In Episode 74, we were challenged to look long enough to set the standard and then jump when we have a winner. We considered shortcuts for reducing the search, and weighed the cost of extending it., too. In Episode 75 we sorted the mess at home base: closets, inboxes, priorities. We cached what mattered most so it was easy to reach, and we scheduled tasks so the important and valuable stuff actually got done. Those steps built a foundation. They didn't eliminate chaos; they made it possible to keep going.
Then came the fog. Episode 76 gave us Bayes' Rule as a way to update what we believe when new information arrives, and a reminder to watch out for overfitting—chasing every tiny detail until the model fits yesterday perfectly but fails tomorrow. Like biology, when an organism is too optimized for its environment and there’s no room to adapt to changes in that environment, overfitting doesn’t handle prediction well. Simpler often held up better.
As the path got harder, we stepped into shared territory. Episode 77 taught us to relax rigid rules and let a little randomness in—drawing from "good enough" solutions that actually work in the real world. Episode 78 showed how to handle the networking side: exponential backoff when things get heated, mechanism design to make honesty the smart move instead of the risky one.
The turning point came straight from the book's final pages. The hero realizes solo wins aren't enough. The real shift is toward, what Christian and Griffiths called, “computational kindness”: designing interactions so they don't overload the other person or create unnecessary complexity. What do I mean? Think of a venue parking lot where you can park anywhere you want versus a parking lot that has a single, directed lane you must travel that starts closest to the venue’s front doors and slowly takes you farther away. In the former, you never know if you’ve got the best spot (or even a good spot). You likely don’t, in fact, you might even circle the lot a few times looking for a decent spot. With the latter format, as you enter the lot simply take the first open spot and you’re guaranteed to have the current closest opening. Design kindly.
In Episode 78, with Vickery Auctions and “Revelation Theory” we saw that it was possible to mitigate future mental work in the design process instead of leaving it to the players, when we understand the mental burden the future players will have. This transparency turns frustration into progress.
A few algorithms stand out like trusty tools we picked up along the way:
- Optimal Stopping: Evaluate the first chunk of options, then commit to the next one that beats your benchmark. It’s a solid process; there’s no guarantee of the absolute best, but it’s the most likely to get the best.
- Explore/Exploit: Dive into new things early (new-city energy), then settle into what works (old-city loyalty). This balances curiosity and stability.
- Relaxation and Randomness: Loosen the grip on perfect rules to get a sense of possibilities, then reimpose them with some new-found starting points. Let chance bring in fresh data. "Good enough" doesn’t have to mean “mediocre” and often beats endless searching. Keep trying something until it is “good enough”.
- Game Theory: Rewrite the incentives so honesty pays off. When cooperation becomes the stronger play, everyone wins. We may not get what we want, but we might get what we need.
These tools give real strength when we use them humanely. Exponential backoff in a tense conversation gives everyone breathing room. Relaxing constraints in group decisions invites more voices; randomness in the same conversation provides a starting point for discussion.
In recreation, I love competition of all sorts. In life, though, I love competition when it’s merit-based and not in zero-sum games (one person’s gain is another’s equivalent cost). I think we can do better. Here's the vision I keep coming back to: a world where these tools help us connect instead of compete, where the systems we build make openness and honesty the natural choice. A world where, when I don’t see the outcome I wanted, I know that I did all the right things.
Thanks for walking this path with me and keep Aiming Up!
