Algorithms to Live By: The Computer Science of Human Decisions

by Brian Christian

the tactical dropping of balls is a critical part of getting things done under overload.

The feeling that one needs to look at everything on the Internet, or read all possible books, or see all possible shows, is bufferbloat.

The much-lamented “lack of idleness” one reads about is, perversely, the primary feature of buffers: to bring average throughput up to peak throughput. Preventing idleness is what they do.

I’m an optimist in the sense that I believe humans are noble and honorable, and some of them are really smart.… I have a somewhat more pessimistic view of people in groups. —STEVE JOBS

Optimal stopping problems spring from the irreversibility and irrevocability of time; the explore/exploit dilemma, from time’s limited supply. Relaxation and randomization emerge as vital and necessary strategies for dealing with the ineluctable complexity of challenges like trip planning and vaccinations.

In the past couple of decades, cross-pollination between game theory and computer science has produced the field of algorithmic game theory—

most influential economist of the twentieth century, John Maynard Keynes, once said that “successful investing is anticipating the anticipations of others.”

the value of a stock isn’t what people think it’s worth but what people think people think it’s worth.

the “halting problem.”

any time a system—be it a machine or a mind—simulates the workings of something as complex as itself, it finds its resources totally maxed out, more or less by definition.

Computer scientists have a term for this potentially endless journey into the hall of mirrors, minds simulating minds simulating minds: “recursion.”

Sometimes poker pros will deliberately bait their opponent into a convoluted recursion, meanwhile playing completely by-the-book, unpsychological poker themselves. This is known as luring them into “a leveling war against themselves.”

In one of the seminal results in game theory, the mathematician John Nash proved in 1951 that every two-player game has at least one equilibrium.

the Nash equilibrium offers a prediction of the stable long-term outcome of any set of rules or incentives. As such, it provides an invaluable tool for both predicting and shaping economic policy, as well as social policy in general.

the object of study in mathematics is truth; the object of study in computer science is complexity.

original field of game theory begat algorithmic game theory—that is, the study of theoretically ideal strategies for games became the study of how machines (and people) come up with strategies for games.

Nash equilibria have held a hallowed place within economic theory as a way to model and predict market behavior, but that place might not be deserved. As Papadimitriou explains, “If an equilibrium concept is not efficiently computable, much of its credibility as a prediction of the behavior of rational agents is lost.”

A dominant strategy avoids recursion altogether, by being the best response to all of your opponent’s possible strategies—so you don’t even need to trouble yourself getting inside their head at all.

one of the major insights of traditional game theory: the equilibrium for a set of players, all acting rationally in their own interest, may not be the outcome that is actually best for those players.

The price of anarchy measures the gap between cooperation (a centrally designed or coordinated solution) and competition (where each participant is independently trying to maximize the outcome for themselves).

A low price of anarchy means the system is, for better or worse, about as good on its own as it would be if it were carefully managed. A high price of anarchy, on the other hand, means that things have the potential to turn out fine if they’re carefully coordinated—but that without some form of intervention, we are courting disaster.

“tragedy of the commons,” and it has become one of the primary lenses through which economists, political scientists, and the environmental movement view large-scale ecological crises like pollution and climate change.

if the rules of the game force a bad strategy, maybe we shouldn’t try to change strategies. Maybe we should try to change the game.

While game theory asks what behavior will emerge given a set of rules, mechanism design (sometimes called “reverse game theory”) works in the other direction, asking: what rules will give us the behavior we want to see?

Mechanism design makes a powerful argument for the need for a designer—

by reducing the number of options that people have, behavioral constraints of the kind imposed by religion don’t just make certain kinds of decisions less computationally challenging—they can also yield better outcomes.

The heart has its reasons which reason knows nothing of. —BLAISE PASCAL

Perhaps each of us, individually, would be better off being the kind of person who can always make a detached, calculated decision in their own best interest, not willing to lose time fuming over a sunk cost, let alone lose a tooth over $40. But all of us are better off living in a society in which such defiant stands are common.

“Morality is herd instinct in the individual,” wrote Nietzsche.

emotion is mechanism design in the species.

As Cornell economist Robert Frank puts it, “If people expect us to respond irrationally to the theft of our property, we will seldom need to, because it will not be in their interests to steal it. Being predisposed to respond irrationally serves much better here than being guided only by material self-interest.”

As Robert Frank puts it, “The worry that people will leave relationships because it may later become rational for them to do so is largely erased if it is not rational assessment that binds them in the first place.”

the rational argument for love is twofold: the emotions of attachment not only spare you from recursively overthinking your partner’s intentions, but by changing the payoffs actually enable a better outcome altogether. What’s more, being able to fall involuntarily in love makes you, in turn, a more attractive partner to have. Your capacity for heartbreak, for sleeping with the emotional fishes, is the very quality that makes you such a trusty accomplice.

game theory offers a sobering perspective: catastrophes like this can happen even when no one’s at fault.

under the right circumstances, a group of agents who are all behaving perfectly rationally and perfectly appropriately can nonetheless fall prey to what is effectively infinite misinformation. This has come to be known as an “information cascade.”

As Hirshleifer puts it, “Something very important happens once somebody decides to follow blindly his predecessors independently of his own information signal, and that is that his action becomes uninformative to all later decision makers. Now the public pool of information is no longer growing. That welfare benefit of having public information … has ceased.”

Information cascades offer a rational theory not only of bubbles, but also of fads and herd behavior more generally.

be wary of cases where public information seems to exceed private information, where you know more about what people are doing than why they’re doing it, where you’re more concerned with your judgments fitting the consensus than fitting the facts. When you’re mostly looking to others to set a course, they may well be looking right back at you to do the same.

remember that actions are not beliefs; cascades get caused in part when we misinterpret what others think based on what they do. We should be especially hesitant to overrule our own doubts—and if we do, we might want to find some way to broadcast those doubts even as we move forward, lest others fail to distinguish the reluctance in our minds from the implied enthusiasm in our actions.

remember from the prisoner’s dilemma that sometimes a game can have irredeemably lousy rules. There may be not...

the Vickrey auction, just like the first-price auction, is a “sealed bid” auction process. That is, every participant simply writes down a single number in secret, and the highest bidder wins. However, in a Vickrey auction, the winner ends up paying not the amount of their own bid, but that of the second-place bidder.

This makes the Vickrey auction what mechanism designers call “strategy-proof,” or just “truthful.”

a game-theoretic principle called “revenue equivalence” establishes that over time, the average expected sale price in a first-price auction will converge to precisely the same as in a Vickrey auction. Thus the Vickrey equilibrium involves the same bidder winning the item for the same price—without any strategizing by any of the bidders whatsoever.

the “revelation principle,” Nobel laureate Roger Myerson proved that any game that requires strategically masking the truth can be transformed into a game that requires nothing but simple honesty.

When we think about ourselves, when we try to know ourselves … we use the knowledge of us which other people already have. We judge ourselves with the means other people have and have given us for judging ourselves. Into whatever I say about myself someone else’s judgment always enters. Into whatever I feel within myself someone else’s judgment enters.… But that does not at all mean that one cannot have relations with other people. It simply brings out the capital importance of all other people for each one of us.

Sartre, expanding upon “hell is other people”

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.

there are cases where computer scientists and mathematicians have identified good algorithmic approaches that can simply be transferred over to human problems. The 37% Rule, the Least Recently Used criterion for handling overflowing caches, and the Upper Confidence Bound as a guide to exploration are all examples of this. Second, knowing that you are using an optimal algorithm should be a relief even if you don’t get the results you were looking for.

Even the best strategy sometimes yields bad results—which is why computer scientists take care to distinguish between “process” and “outcome.” If you followed the best possible process, then you’ve done all you can, and you shouldn’t blame yourself if things didn’t go your way.

If you wind up stuck in an intractable scenario, remember that heuristics, approximations, and strategic use of randomness can help you find workable solutions.

we don’t only pick the problems that we pose to ourselves. We also pick the problems we pose each other, whether it’s the way we design a city or the way we ask a question. This creates a surprising bridge from computer science to ethics—in the form of a principle that we call computational kindness.