Constraint Relaxation provides a powerful framework for seemingly impossible problems with competing demands:<ul><li>Distinguish between hard constraints (must be satisfied) and soft constraints (preferences)</li><li>Assign weights to different preferences based on importance</li><li>Optimize for maximum overall satisfaction rather than perfect satisfaction of all constraints</li><li>Accept that some constraints must be violated to find any solution at all</li></ul>This approach transforms unsolvable problems into manageable ones. In life, as in computing, the art is knowing which constraints to prioritize and which to relax—focusing resources on what truly matters.

Constraint Relaxation

To try and fail is at least to learn; to fail to try is to suffer the loss of what might have been.

Sometimes the best algorithm is the one that's easiest to implement and explains to others.

The optimal algorithm for many problems is just to solve a different problem.

Game Theory reveals profound insights about strategic interactions:<ul><li>In zero-sum games, maximally exploitative strategies are optimal</li><li>In repeated interactions, cooperative strategies often outperform aggressive ones</li><li>The Tit for Tat strategy (cooperate first, then mirror opponent's last move) proves remarkably effective</li><li>Nash Equilibrium reveals why individually rational choices can lead to collectively poor outcomes</li></ul>These principles explain business behavior, international relations, and everyday interactions. The key insight: cooperation emerges naturally from repeated interactions, even among self-interested parties—explaining how trust develops in human relationships.

Game Theory

The Optimal Stopping Problem provides a mathematical solution to when to stop looking and decide. The 37% Rule works like this:
<ul>
<li>Determine how many options you'll likely encounter (n)
</li>
<li>Look at the first 37% of options (n/e, where e is Euler's number)
</li>
<li>Remember the best option seen so far, but don't choose any
</li>
<li>After the 37% mark, select the first option better than all previous ones
</li>
</ul>
This approach guarantees finding the best option 37% of the time—mathematically proven to be the best possible success rate. Applications include hiring, dating, apartment hunting, and any sequence of irreversible decisions.

Optimal Stopping

Randomized Algorithms use controlled randomness to achieve better results than deterministic approaches. This counterintuitive concept reveals:
<ul>
<li>Injecting randomness can break deadlocks and avoid predictable traps</li>
<li>Monte Carlo methods solve problems through random sampling</li>
<li>Randomized approaches often find solutions faster than exhaustive ones</li>
<li>Unpredictability itself can be a powerful strategic tool</li>
</ul>
This explains why random drug testing is more effective than testing everyone (or testing on a fixed schedule), and why mixed strategies in games like poker outperform predictable play. Sometimes the optimal approach isn't systematic but deliberately random.

Randomized Algorithms

Sorting algorithms reveal counterintuitive truths about organization:<ul><li>Perfect sorting (alphabetical, chronological) is often unnecessarily expensive</li><li>The LRU (Least Recently Used) algorithm proves mathematically optimal for many scenarios</li><li>It works by keeping recently accessed items most accessible</li><li>Libraries using this principle outperform traditional arrangements</li><li>Our brains naturally implement approximations of optimal sorting algorithms</li></ul>The implications are profound: the messy desk approach, keeping recently used items on top, is actually mathematically optimal. This explains why perfectly organized systems often feel less efficient than those that evolve naturally.

Sorting

Scheduling algorithms provide mathematically optimal solutions to time management:<ul><li>Shortest Processing Time First minimizes average completion time</li><li>Earliest Deadline First minimizes lateness for time-sensitive tasks</li><li>Processor Sharing (dividing attention between tasks) proves optimal when tasks benefit from partial completion</li></ul>The implications are counterintuitive but powerful:<ul><li>Tackling quick tasks first is not procrastination—it's mathematically optimal</li><li>Interruptions are far more costly than we intuit</li><li>Context switching imposes a heavy cognitive tax</li></ul>These algorithms provide guidelines for managing email, to-do lists, and project prioritization.

Scheduling

Overfitting occurs when a model is too precisely tailored to limited data, capturing noise rather than signal. This concept reveals:<ul><li>Complex models perform well on existing data but fail on new situations</li><li>Simpler models often make better predictions in uncertain environments</li><li>Regularization (penalizing complexity) improves real-world performance</li><li>Cross-validation (testing on unseen data) is essential</li></ul>Human cognition battles the same problem—we build overly complex mental models from limited experience. The antidote is embracing simplicity: broad principles instead of excessively detailed rules.

Overfitting

The Explore/Exploit Tradeoff represents a fundamental tension in decision-making: trying new things or sticking with known rewards. This framework reveals:
<ul>
<li>Early in any timeline, exploration delivers more long-term value
</li>
<li>As time horizon shortens, exploitation becomes optimal
</li>
<li>The mathematical solution is the Gittins index, which assigns values to each option
</li>
<li>Our intuitions often align with this model—we explore more when young and exploit more as we age
</li>
</ul>
This explains why children explore constantly while elderly people stick with favorites. The optimal strategy depends on how much time remains for using the information gained.

Explore vs. Exploit

Relaxation offers an elegant approach to seemingly impossible problems:<ul><li>Some problems (like the Traveling Salesman) have no efficient optimal solution</li><li>Relaxation algorithms start with any answer and make incremental improvements</li><li>They accept good enough rather than pursuing unattainable perfection</li><li>These approaches often achieve 90-95% optimal results with minimal effort</li></ul>The lesson is profound: in many domains, the cost of finding the perfect solution exceeds the benefit of having it. Relaxation is not laziness—it's an optimal strategy for allocating limited computational resources, whether in computers or human brains.

Relaxation

Caching is a profound concept with applications from computer architecture to everyday life. The key insights:
<ul>
<li>Limited space requires prioritization of what to keep immediately accessible</li>
<li>Optimal caching requires prediction about future needs</li>
<li>The LRU (Least Recently Used) principle works remarkably well</li>
<li>The working set (items needed for current tasks) should stay in cache</li>
<li>Eviction policies (what to remove when space runs out) matter enormously</li>
</ul>
This explains why we instinctively organize our physical spaces with frequently used items in accessible locations. Our brains implement approximations of optimal caching algorithms naturally.

Caching

Ever feel swamped with too many decisions? This mind-blowing book shows how computer algorithms secretly solve the same problems we face daily. From apartment hunting to managing email, the math that powers computers can optimize your life too! It's not about coding—it's about finding elegant solutions to everyday chaos. Better decisions aren't about having more brainpower—they're about having better strategies.

An exploration of how computer algorithms can be applied to our everyday lives to solve common decision-making problems and illuminate the workings of the human mind. What should we do, or leave undone, in a day or a lifetime? How much messiness should we accept? What balance of the new and familiar is the most fulfilling? These may seem like uniquely human quandaries, but they are not. Computers, like us, confront limited space and time, so computer scientists have been grappling with similar problems for decades. And the solutions they’ve found have much to teach us. In a dazzlingly interdisciplinary work, Brian Christian and Tom Griffiths show how algorithms developed for computers also untangle very human questions. They explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others. From finding a spouse to finding a parking spot, from organizing one’s inbox to peering into the future, Algorithms to Live By transforms the wisdom of computer science into strategies for human living.

Brian Christian is an accomplished writer whose work has resonated with readers around the world. Brian Christian has published several books that explore a range of topics, from fiction to nonfiction, and has received critical acclaim for the ability to weave compelling narratives. Brian Christian's writing style is known for its depth and authenticity, and has earned quite a loyal following of readers.

Brian Christian

Tom Griffiths is an accomplished writer whose work has resonated with readers around the world. Tom Griffiths has published several books that explore a range of topics, from fiction to nonfiction, and has received critical acclaim for the ability to weave compelling narratives. Tom Griffiths's writing style is known for its depth and authenticity, and has earned quite a loyal following of readers.

BRIAN CHRISTIAN AND TOM GRIFFITHS

Similar ideas to Brian Christian and Tom Griffiths

Mental Models: Out of Box Thinking

Heuristic Computing

2. Don't be afraid of Problems