Algorithms To Live By Summary

Book Summary – Algorithms To Live By :The Computer Science of Human Decisions

A fascinating exploration of how insights from computer algorithms can be applied to our everyday lives, helping to solve common decision-making problems and illuminate the workings of the human mind

About The Authors

Brian Christian is the author of The Most Human Human, a Wall Street Journal bestseller,

Tom Griffiths is a professor of psychology and cognitive science at UC Berkeley,

Introduction Algorithms to Live By

If you want the best odds of getting the best apartment, spend 37% of your

apartment hunt (eleven days, if you’ve given yourself a month for the search)

noncommittally exploring options. Leave the checkbook at home; you’re just

calibrating. But after that point, be prepared to immediately commit—deposit

and all—to the very first place you see that beats whatever you’ve already seen.

This is not merely an intuitively satisfying compromise between looking and leaping.

It is the provably optimal solution

1 Optimal Stopping When to Stop Looking

the explicit premise of the

optimal stopping problem is the implicit premise of what it is to be alive. It’s this

that forces us to decide based on possibilities we’ve not yet seen, this that forces

us to embrace high rates of failure even when acting optimally. No choice recurs.

We may get similar choices again, but never that exact one. Hesitation—inaction

—is just as irrevocable as action.

Intuitively, we think that rational decision-making means exhaustively

enumerating our options, weighing each one carefully, and then selecting the

best. But in practice, when the clock—or the ticker—is ticking, few aspects of

decision-making (or of thinking more generally) are as important as this one:

when to stop.

2 Explore/Exploit

The Latest vs. the Greatest

life should get better

over time. What an explorer trades off for knowledge is pleasure. The Gittins

index and the Upper Confidence Bound, as we’ve seen, inflate the appeal of

lesser-known options beyond what we actually expect, since pleasant surprises

can pay off many times over. But at the same time, this means that exploration

necessarily leads to being let down on most occasions. Shifting the bulk of one’s

attention to one’s favorite things should increase quality of life. And it seems

like it does: Carstensen has found that older people are generally more satisfied

with their social networks, and often report levels of emotional well-being that

are higher than those of younger adults.

3 Sorting Making Order

When we think about the factors that make large-scale human societies

possible, it’s easy to focus on technologies: agriculture, metals, machinery. But

the cultural practice of measuring status with quantifiable metrics might be just

as important. Money, of course, need not be the criterion; a rule like “respect

your elders,” for instance, likewise settles questions of people’s status by

reference to a common quantity. And the same principle is at work between nations.

Linearithmic numbers of fights might work fine for small-scale groups; they

do in nature. But in a world where status is established through pairwise

comparisons—whether they involve exchanging rhetoric or gunfire—the amount

of confrontation quickly spirals out of control as society grows. Operating at

industrial scale, with many thousands or millions of individuals sharing the same

space, requires a leap beyond. A leap from ordinal to cardinal.

Much as we bemoan the daily rat race, the fact that it’s a race rather than a

fight is a key part of what sets us apart from the monkeys, the chickens—and, for

that matter, the rats.

4 Caching Forget About It

An understanding of the unavoidable computational demands of memory,

Ramscar says, should help people come to terms with the effects of aging on

cognition. “I think the most important tangible thing seniors can do is to try to

get a handle on the idea that their minds are natural information processing

devices,” he writes. “Some things that might seem frustrating as we grow older

(like remembering names!) are a function of the amount of stuff we have to sift

through … and are not necessarily a sign of a failing mind.” As he puts it, “A lot

of what is currently called decline is simply learning.”

Caching gives us the language to understand what’s happening. We say

“brain fart” when we should really say “cache miss.” The disproportionate

occasional lags in information retrieval are a reminder of just how much we

benefit the rest of the time by having what we need at the front of our minds.

So as you age, and begin to experience these sporadic latencies, take heart:

the length of a delay is partly an indicator of the extent of your experience. The

effort of retrieval is a testament to how much you know. And the rarity of those

lags is a testament to how well you’ve arranged it: keeping the most important

things closest to hand.

5 Scheduling First Things First

you should try to stay on a single task as long as possible without

decreasing your responsiveness below the minimum acceptable limit. Decide

how responsive you need to be—and then, if you want to get things done, be no

more responsive than that.

If you find yourself doing a lot of context switching because you’re tackling a

heterogeneous collection of short tasks, you can also employ another idea from

computer science: “interrupt coalescing.” If you have five credit card bills, for

instance, don’t pay them as they arrive; take care of them all in one go when the

fifth bill comes.

6 Bayes’s Rule Predicting the Future

Consider how many times you’ve seen either a crashed plane or a crashed

car. It’s entirely possible you’ve seen roughly as many of each—yet many of

those cars were on the road next to you, whereas the planes were probably on

another continent, transmitted to you via the Internet or television. In the United

States, for instance, the total number of people who have lost their lives in

commercial plane crashes since the year 2000 would not be enough to fill

Carnegie Hall even half full. In contrast, the number of people in the United

States killed in car accidents over that same time is greater than the entire

population of Wyoming.

Simply put, the representation of events in the media does not track their

frequency in the world. As sociologist Barry Glassner notes, the murder rate in

the United States declined by 20% over the course of the 1990s, yet during that

time period the presence of gun violence on American news increased by 600%.

If you want to be a good intuitive Bayesian—if you want to naturally make

good predictions, without having to think about what kind of prediction rule is

appropriate—you need to protect your priors. Counterintuitively, that might

mean turning off the news.

7 Overfitting When to Think Less

how early to stop depends on the gap

between what you can measure and what really matters. If you have all the facts,

they’re free of all error and uncertainty, and you can directly assess whatever is

important to you, then don’t stop early. Think long and hard: the complexity and effort are appropriate.

But that’s almost never the case. If you have high uncertainty and limited data, then do stop early by all means.

When you’re truly in the dark, the best-laid plans will be the simplest. When

our expectations are uncertain and the data are noisy, the best bet is to paint with

a broad brush, to think in broad strokes. Sometimes literally. As entrepreneurs

Jason Fried and David Heinemeier Hansson explain, the further ahead they need

to brainstorm, the thicker the pen they use—a clever form of simplification by stroke size:

8 Relaxation Let It Slide

Unless we’re willing to spend eons striving for perfection every time we

encounter a hitch, hard problems demand that instead of spinning our tires we

imagine easier versions and tackle those first. When applied correctly, this is not

just wishful thinking, not fantasy or idle daydreaming. It’s one of our best ways

of making progress.

9 Randomness

When to Leave It to Chance

if you’re in the habit of

sometimes acting on bad ideas, you should always act on good ones. Second,

from the Metropolis Algorithm: your likelihood of following a bad idea should

be inversely proportional to how bad an idea it is. Third, from Simulated

Annealing: you should front-load randomness, rapidly cooling out of a totally

random state, using ever less and less randomness as time goes on, lingering

longest as you approach freezing. Temper yourself—literally.

10 Networking

How We Connect

The most prevalent critique of modern communications is that we are

“always connected.” But the problem isn’t that we’re always connected; we’re

not. The problem is that we’re always buffered. The difference is enormous.

The feeling that one needs to look at everything on the Internet, or read all

possible books, or see all possible shows, is bufferbloat. You miss an episode of

your favorite series and watch it an hour, a day, a decade later

11 Game Theory

The Minds of Others

If changing strategies doesn’t help, you can try to change the game. And if

that’s not possible, you can at least exercise some control about which games

you choose to play. The road to hell is paved with intractable recursions, bad

equilibria, and information cascades. Seek out games where honesty is the dominant strategy.

Then just be yourself.

Conclusion Computational Kindness

In almost every domain we’ve considered, we have seen how the more realworld factors we include—whether it’s having incomplete information when

interviewing job applicants, dealing with a changing world when trying to

resolve the explore/exploit dilemma, or having certain tasks depend on others

when we’re trying to get things done—the more likely we are to end up in a

situation where finding the perfect solution takes unreasonably long. And

indeed, people are almost always confronting what computer science regards as

the hard cases. Up against such hard cases, effective algorithms make

assumptions, show a bias toward simpler solutions, trade off the costs of error

against the costs of delay, and take chances

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