3 ways to make better decisions -- by thinking like a computer | Tom Griffiths

譯者: Lilian Chiu 審譯者: Helen Chang

If there's one city in the world

如果世界上有一個城市

where it's hard to find a place to buy or rent,

很難找到出售或是出租的地方，

it's Sydney.

那就是雪梨。

And if you've tried to find a home here recently,

如果你最近試著在這裡找個家，

you're familiar with the problem.

你對這個問題就會很熟悉。

Every time you walk into an open house,

每當你走進開放看屋的地點，

you get some information about what's out there

你就可以得到些資訊， 知道那裡有什麼，

and what's on the market,

以及市場上有什麼；

but every time you walk out,

但每當你走出來時，

you're running the risk of the very best place passing you by.

你就冒著錯過最佳選擇的風險。

So how do you know when to switch from looking

所以，你怎麼知道 何時要從「看看」切換成

to being ready to make an offer?

準備好提出交易條件？

This is such a cruel and familiar problem

這是個殘酷又熟悉的問題，

that it might come as a surprise that it has a simple solution.

讓人意外的是， 它的解決方案很簡單。

37 percent.

37%。

(Laughter)

（笑聲）

If you want to maximize the probability that you find the very best place,

如果你想要把找到 最佳選擇的機率提升到最高，

you should look at 37 percent of what's on the market,

你得要看過市場上 37% 的所有選擇的，

and then make an offer on the next place you see,

接著到下一個地方時， 就提出交易條件，

which is better than anything that you've seen so far.

它會比你目前看過的 所有選擇都更好。

Or if you're looking for a month, take 37 percent of that time --

或者，如果你要花一個月來尋找， 就取那段時間的 37% ——

11 days, to set a standard --

即 11 天，來設定標準——

and then you're ready to act.

接著你就可以準備行動了。

We know this because trying to find a place to live

我們知道要這麼做， 是因為試圖找住房

is an example of an optimal stopping problem.

就是「最佳停止問題」的例子。

A class of problems that has been studied extensively

這類問題一直被數學家

by mathematicians and computer scientists.

和電腦科學家廣為研究。

I'm a computational cognitive scientist.

我是一位計算認知科學家。

I spend my time trying to understand

我把時間花在了解

how it is that human minds work,

人類大腦如何運作，

from our amazing successes to our dismal failures.

從達成了不起的成功 到遭遇令人沮喪的失敗。

To do that, I think about the computational structure

要做到這一點，我得要思考

日常問題的計算結構，

of the problems that arise in everyday life,

and compare the ideal solutions to those problems

並將那些問題的理想解決方案

與我們的真實行為做比較。

to the way that we actually behave.

As a side effect,

它有一個副作用，

I get to see how applying a little bit of computer science

我可以看到應用一點點電腦科學

can make human decision-making easier.

如何能讓人類決策變得更容易。

I have a personal motivation for this.

我這麼做，背後有個私人的動機。

Growing up in Perth as an overly cerebral kid ...

我在伯斯長大，以前 是個過度理智的小孩……

(Laughter)

（笑聲）

I would always try and act in the way that I thought was rational,

我總是試著用我認為 合理的方式來做事，

reasoning through every decision,

做每個決策都要依理推論，

trying to figure out the very best action to take.

試圖找出採取哪種做法最理想。

But this is an approach that doesn't scale up

但這種方法無法做更廣的應用，

when you start to run into the sorts of problems

當你開始遇到成人 生活中的那些問題時，

that arise in adult life.

就派不上用場了。

At one point, I even tried to break up with my girlfriend

我有一度甚至打算要和女友分手，

because trying to take into account her preferences as well as my own

原因是我試著考量 她的偏好和我的偏好，

and then find perfect solutions --

以找出最完美的解決方案——

(Laughter)

（笑聲）

was just leaving me exhausted.

我真的被搞得疲憊不堪。

(Laughter)

（笑聲）

She pointed out that I was taking the wrong approach

她指出我在解決這個問題時

to solving this problem --

用錯了方法——

and she later became my wife.

後來她成了我的太太。

(Laughter)

（笑聲）

(Applause)

（掌聲）

Whether it's as basic as trying to decide what restaurant to go to

不論是很基本的問題， 比如決定要去哪家餐廳吃飯，

or as important as trying to decide who to spend the rest of your life with,

或是很重要的問題， 比如決定要和誰共渡餘生，

human lives are filled with computational problems

人生其實都充滿了計算問題，

that are just too hard to solve by applying sheer effort.

光靠努力是很難解決的。

For those problems,

那些問題

it's worth consulting the experts:

值得去諮詢專家：

computer scientists.

電腦科學家。

(Laughter)

（笑聲）

When you're looking for life advice,

當你要尋求人生忠告時，

computer scientists probably aren't the first people you think to talk to.

你最先想要問的人大概 不會是電腦科學家。

Living life like a computer --

把人生過得像電腦一樣——

stereotypically deterministic, exhaustive and exact --

刻板的決定論、 詳盡無遺，且精確——

doesn't sound like a lot of fun.

聽起來實在不好玩。

But thinking about the computer science of human decisions

但思考一下人類決策的電腦科學，

reveals that in fact, we've got this backwards.

會發現，事實上， 我們把方向弄反了。

When applied to the sorts of difficult problems

當應用在人生中的

that arise in human lives,

那些困難問題上時，

the way that computers actually solve those problems

電腦實際上用來解決 那些問題的方式

looks a lot more like the way that people really act.

看起來很像是人們真正使用的方式。

Take the example of trying to decide what restaurant to go to.

就用決定要去哪間餐廳 吃飯當作例子吧。

This is a problem that has a particular computational structure.

這個問題有特定的計算結構。

You've got a set of options,

你有一組選項，

you're going to choose one of those options,

你得要從那些選項中擇一，

and you're going to face exactly the same decision tomorrow.

且你明天還會面對 完全一樣的決策。

In that situation,

在那樣的情況下，

you run up against what computer scientists call

你碰到的就是電腦科學家所謂的

the "explore-exploit trade-off."

「探索／利用的權衡」。

You have to make a decision

你得要做一個決策，

about whether you're going to try something new --

決定你是否要嘗試新選項——

exploring, gathering some information

去「探索」，收集一些未來

that you might be able to use in the future --

可能會用到的資訊——

or whether you're going to go to a place that you already know is pretty good --

或者你是否要選擇去 你已經知道不錯的地方——

exploiting the information that you've already gathered so far.

「利用」你目前已經 收集到的資訊。

The explore/exploit trade-off shows up any time you have to choose

探索／利用的權衡會出現在每次

between trying something new

你必須要從新選項和已經知道 不錯的選項中擇一的情況下，

and going with something that you already know is pretty good,

whether it's listening to music

也許是聽音樂，

or trying to decide who you're going to spend time with.

或者是試著決定 你要跟誰一起殺時間。

It's also the problem that technology companies face

這也是科技公司會面臨的問題，

when they're trying to do something like decide what ad to show on a web page.

比如決定要在網頁上放什麼 廣告時，遇到的就是這種問題。

它們應該要刊登新廣告， 從中得到一些資訊嗎？

Should they show a new ad and learn something about it,

或是它們應該要給你看

or should they show you an ad

that they already know there's a good chance you're going to click on?

一則它們已經知道你很有可能 會點選的廣告？

Over the last 60 years,

在過去六十年，

computer scientists have made a lot of progress understanding

電腦科學家在了解 探索／利用的權衡上，

the explore/exploit trade-off,

有相當多進展，

and their results offer some surprising insights.

他們的結果帶來了 一些讓人吃驚的洞見。

When you're trying to decide what restaurant to go to,

當你要試著決定該去哪一間餐廳時，

the first question you should ask yourself

你應該先問你自己一個問題：

is how much longer you're going to be in town.

你還會待在鎮上多久？

If you're just going to be there for a short time,

如果你只是短暫停留，

then you should exploit.

那麼你應該要「利用」。

There's no point gathering information.

收集資訊是沒有意義的。

Just go to a place you already know is good.

直接去一個你已經 知道不錯的地方吧。

But if you're going to be there for a longer time, explore.

但如果你會待久一點， 就「探索」吧。

Try something new, because the information you get

試試新選項，因為 你從中得到的資訊

is something that can improve your choices in the future.

可能協助你在未來做更好的選擇。

The value of information increases

你越有可能用到一項資訊，

the more opportunities you're going to have to use it.

該資訊的價值就會增加。

This principle can give us insight

這條原則也能協助我們

into the structure of a human life as well.

洞察人類的人生。

Babies don't have a reputation for being particularly rational.

寶寶通常不會特別理性。

They're always trying new things,

他們總是在嘗試新東西，

and you know, trying to stick them in their mouths.

你們知道的，總把 新東西放到嘴巴裡。

But in fact, this is exactly what they should be doing.

但，事實上，他們 的確應該要這麼做。

They're in the explore phase of their lives,

他們正處在人生的探索階段，

and some of those things could turn out to be delicious.

他們嘗試的東西當中， 有些可能真的會很美味。

At the other end of the spectrum,

在光譜的另一端，

the old guy who always goes to the same restaurant

是老人，他們總是去同樣的餐廳，

and always eats the same thing

總是點同樣的食物，

isn't boring --

並不是無趣，

he's optimal.

而是最佳化的選擇。

(Laughter)

（笑聲）

He's exploiting the knowledge that he's earned

他在利用他從一生的經驗中

through a lifetime's experience.

已經得到的知識。

More generally,

更普遍來說，知道有 「探索／利用的權衡」，

knowing about the explore/exploit trade-off

can make it a little easier for you to sort of relax and go easier on yourself

就能讓你在做決策時能更輕鬆些，

不要對自己太嚴厲。

when you're trying to make a decision.

你不需要每晚都去最好的餐廳。

You don't have to go to the best restaurant every night.

Take a chance, try something new, explore.

冒個險，嘗試新餐廳，去探索。

You might learn something.

你可能會學到些什麼。

And the information that you gain

而你所得到的資訊

is going to be worth more than one pretty good dinner.

價值絕對勝過一頓好吃的晚餐。

Computer science can also help to make it easier on us

在家中或在辦公室裡的其他地方，

in other places at home and in the office.

電腦科學也能夠讓我們更輕鬆些。

If you've ever had to tidy up your wardrobe,

如果你得要整理你的衣櫥，

you've run into a particularly agonizing decision:

你會碰到一個特別煩惱的決定：

you have to decide what things you're going to keep

你得要決定哪些東西該留下，

and what things you're going to give away.

哪些東西該送人。

Martha Stewart turns out to have thought very hard about this --

結果發現瑪莎史都華花了 很多功夫在想這件事——

(Laughter)

（笑聲）

and she has some good advice.

她有些不錯的忠告。

She says, "Ask yourself four questions:

她說：「問你自己四個問題：

How long have I had it?

我已經持有它多久了？

Does it still function?

它還有功能嗎？

Is it a duplicate of something that I already own?

它是不是跟某樣 我已經擁有的東西一樣？

And when was the last time I wore it or used it?"

我上次穿它或用它是什麼時候？」

But there's another group of experts

但還有另一群專家

who perhaps thought even harder about this problem,

花了更多功夫在想這個問題，

and they would say one of these questions is more important than the others.

他們會說，這些問題當中 有一個比其他的都還重要。

Those experts?

那些專家是誰？

The people who design the memory systems of computers.

設計出電腦記憶體系統的人。

Most computers have two kinds of memory systems:

大部分的電腦有兩種記憶體系統：

a fast memory system,

快速記憶體系統，

like a set of memory chips that has limited capacity,

就像是一組記憶體晶片，容量有限，

because those chips are expensive,

因為那些晶片很貴，

and a slow memory system, which is much larger.

還有慢速記憶體系統， 它的容量大很多。

In order for the computer to operate as efficiently as possible,

為了要讓電腦的 運作效能盡可能提高，

you want to make sure

你會希望能確保你要存取的資訊

that the pieces of information you want to access

are in the fast memory system,

位在快速記憶體系統中， 這樣你就能快速取得它。

so that you can get to them quickly.

每當你存取一項資訊時，

Each time you access a piece of information,

它就會被載入快速記憶體中，

it's loaded into the fast memory

and the computer has to decide which item it has to remove from that memory,

電腦得要決定要從 快速記憶體中移除哪個項目，

because it has limited capacity.

因為它的容量有限。

Over the years,

數年來，電腦科學家 試過幾種不同的策略

computer scientists have tried a few different strategies

來判定該從快速記憶體中移除什麼。

for deciding what to remove from the fast memory.

他們有試過隨機選擇的方法，

They've tried things like choosing something at random

or applying what's called the "first-in, first-out principle,"

也試過採用「先進先出」的原則，

which means removing the item

也就是說把在記憶體當中 最久的項目給移除。

which has been in the memory for the longest.

But the strategy that's most effective

不過，最有效的策略，

focuses on the items which have been least recently used.

是把目標放在近期最少使用的項目。

This says if you're going to decide to remove something from memory,

這種策略就是，如果你得 從記憶體中移除某樣東西，

you should take out the thing which was last accessed the furthest in the past.

你應該選擇最後一次使用時間 是最久遠的那樣東西。

And there's a certain kind of logic to this.

這背後是有某種邏輯的。

If it's been a long time since you last accessed that piece of information,

如果你上次存取那項資訊 已經是很久以前的事了，

it's probably going to be a long time

你下次需要存取它的時間

before you're going to need to access it again.

應該也會是很久以後。

Your wardrobe is just like the computer's memory.

你的衣櫥就像是電腦的記憶體。

You have limited capacity,

你的容量有限，

and you need to try and get in there the things that you're most likely to need

你得要把你最有可能 用到的東西放進去，

so that you can get to them as quickly as possible.

這樣你才能夠盡快取得它們。

認知到這一點後，

Recognizing that,

maybe it's worth applying the least recently used principle

也許也值得嘗試應用 「近期最少使用」原則

to organizing your wardrobe as well.

來整理你的衣櫥。

So if we go back to Martha's four questions,

如果我們回到瑪莎的四個問題，

the computer scientists would say that of these,

電腦科學家會說，在這些問題中，

the last one is the most important.

最後一個問題是最重要。

This idea of organizing things

在整理東西時，要讓你最可能

so that the things you are most likely to need are most accessible

需要的東西最容易存取的這個想法，

can also be applied in your office.

也可以應用到你的辦公室中。

The Japanese economist Yukio Noguchi

日本經濟學家野口悠紀雄

actually invented a filing system that has exactly this property.

真的發明了一個具有 這種特性的建檔系統。

He started with a cardboard box,

他從一個紙箱子開始，

and he put his documents into the box from the left-hand side.

他把他的文件 從左到右放進箱子中。

Each time he'd add a document,

每當他放入一份文件時， 他就得要移動箱中的文件，

he'd move what was in there along

才能把新放入的文件 放入箱子的左邊。

and he'd add that document to the left-hand side of the box.

每當他需要使用一份文件時， 他會把該文件取出，

And each time he accessed a document, he'd take it out,

consult it and put it back in on the left-hand side.

使用完之後放回到最左邊。

As a result, the documents would be ordered from left to right

這樣的結果是， 文件會從左到右排好，

by how recently they had been used.

最左邊的是最近期使用過的。

And he found he could quickly find what he was looking for

他發現這樣排之後， 他只要從箱子的左邊開始

by starting at the left-hand side of the box

一直向右找，就能快速 找到他想找的文件。

and working his way to the right.

Before you dash home and implement this filing system --

在你們衝回家導入 這個建檔系統之前——

(Laughter)

（笑聲）

it's worth recognizing that you probably already have.

值得先想想，你可能 已經有這個系統了。

(Laughter)

（笑聲）

That pile of papers on your desk ...

你書桌上的那疊紙……

typically maligned as messy and disorganized,

通常都被別人誹謗說是亂七八糟，

a pile of papers is, in fact, perfectly organized --

其實是有著完美 組織系統的一疊紙——

(Laughter)

（笑聲）

as long as you, when you take a paper out,

只要你每次把一張紙拿出來，

put it back on the top of the pile,

用完之後會放回那疊紙的最上方，

then those papers are going to be ordered from top to bottom

那麼那疊紙從上到下 就排好了順序，

by how recently they were used,

最上面的是最近期使用的，

and you can probably quickly find what you're looking for

你從那疊紙的最上面開始找，

by starting at the top of the pile.

可能就能快速找到你要的。

Organizing your wardrobe or your desk

整理你的衣櫥或你的書桌

are probably not the most pressing problems in your life.

可能不是你人生中最緊迫的問題。

Sometimes the problems we have to solve are simply very, very hard.

有時，我們需要解決的問題 就是非常非常難搞。

But even in those cases,

但即使在那些情況下，

computer science can offer some strategies

電腦科學也能夠提供一些策略，

and perhaps some solace.

也許還能提供一些安慰。

The best algorithms are about doing what makes the most sense

最好的演算法， 就是要在最短的時間內

in the least amount of time.

做出最合理的舉動。

When computers face hard problems,

當電腦面臨困難的問題時，

they deal with them by making them into simpler problems --

它們的處理方式是把那些問題 變成更簡單的問題——

by making use of randomness,

做法包括使用隨機性、

by removing constraints or by allowing approximations.

移除限制式，或是允許近似值。

Solving those simpler problems

解決那些較簡單的問題，

can give you insight into the harder problems,

就能提供你關於 原本困難問題的洞見，

and sometimes produces pretty good solutions in their own right.

有時，還能自己產生出 很好的解決方案。

Knowing all of this has helped me to relax when I have to make decisions.

知道這一切，讓我在 必須要做決策時能夠放輕鬆。

You could take the 37 percent rule for finding a home as an example.

可以用找房子時的 37% 規則來當例子。

There's no way that you can consider all of the options,

你不可能把所有的 選項都納入考量，

so you have to take a chance.

所以你得要冒險。

And even if you follow the optimal strategy,

即使你遵循最佳化策略，

you're not guaranteed a perfect outcome.

也不能保證你會得到最完美的結果。

If you follow the 37 percent rule,

如果你遵循 37% 規則，

the probability that you find the very best place is --

你能找到最棒的地方的機率是——

funnily enough ...

很有趣……

(Laughter)

（笑聲）

37 percent.

是 37%。

You fail most of the time.

大部分的時候，你會失敗。

But that's the best that you can do.

但你能做到最好的就是這樣了。

Ultimately, computer science can help to make us more forgiving

最終，電腦科學會協助讓我們

of our own limitations.

更能原諒自己的限制。

You can't control outcomes, just processes.

你不能控制結果，只能控制過程。

And as long as you've used the best process,

只要你已經用了最好的過程，

you've done the best that you can.

你就已經盡了全力。

Sometimes those best processes involve taking a chance --

有時，最好的過程會需要冒點險——

not considering all of your options,

比如不去考量所有的選項，

or being willing to settle for a pretty good solution.

或是願意妥協，接受 算是不錯的解決方案。

These aren't the concessions that we make when we can't be rational --

這些並不是我們在無法 理性時所做的讓步——

they're what being rational means.

它們就是理性的真締。

Thank you.

謝謝大家。

(Applause)

（掌聲）