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If there's one city in the world where it's hard to find a place to buy or rent, it's Sydney.
如果世界上有一个城市很难找到出售或是出租的地方，那就是悉尼。
And if you've tried to find a home here recently, you're familiar with the problem.
如果你最近试着在这里找个家，你对这个问题就会很熟悉。
Every time you walk into an open house, you get some information about what's out there and what's on the market,
每当你走进开放看屋的地点，你就可以得到些信息，知道那里有什么以及市场上有什么，
but every time you walk out, you're running the risk of the very best place passing you by.
但每当你走出来时，你就冒着错过最佳选择的风险。
So how do you know when to switch from looking to being ready to make an offer?
所以，你怎么知道何时要从“看看”切换成准备好提出交易条件？
This is such a cruel and familiar problem that it might come as a surprise that it has a simple solution.
这是个残酷又熟悉的问题，让人意外的是，它的解决方案很简单。
37 percent. If you want to maximize the probability that you find the very best place,
37%。如果你想要把找到最佳选择的机率提升到最高，
you should look at 37 percent of what's on the market, and then make an offer on the next place you see,
你得要看过市场上37%的所有选择的，接着到下一个地方时，就提出交易条件，
which is better than anything that you've seen so far.
它会比你目前看过的所有选择都更好。
Or if you're looking for a month, take 37 percent of that time -- 11 days, to set a standard -- and then you're ready to act.
或者，如果你要花一个月来寻找，就取那段时间的37%-- 即11天，来设定标准--接着你就可以准备行动了。
We know this because trying to find a place to live is an example of an optimal stopping problem.
我们知道要这么做，是因为试图找住房就是“最佳停止问题”的例子。
A class of problems that has been studied extensively by mathematicians and computer scientists.
这类问题一直被数学家和计算机科学家广为研究。
I'm a computational cognitive scientist.
我是一位计算认知科学家。
I spend my time trying to understand how it is that human minds work, from our amazing successes to our dismal failures.
我把时间花在了解人类大脑如何运作，从达成了不起的成功到遭遇令人沮丧的失败。
To do that, I think about the computational structure of the problems that arise in everyday life,
要做到这一点，我得要思考日常问题的计算结构，
and compare the ideal solutions to those problems to the way that we actually behave.
并将那些问题的理想解决方案与我们的真实行为做比较。
As a side effect, I get to see how applying a little bit of computer science can make human decision-making easier.
它有一个副作用，我可以看到应用一点点计算机科学如何能让人类决策变得更容易。
I have a personal motivation for this. Growing up in Perth as an overly cerebral kid ...
我这么做，背后有个私人的动机。我在伯斯长大，以前是个过度理智的小孩...
I would always try and act in the way that I thought was rational,
我总是试着用我认为合理的方式来做事，
reasoning through every decision, trying to figure out the very best action to take.
做每个决策都要依理推论，试图找出采取哪种做法最理想。
But this is an approach that doesn't scale up when you start to run into the sorts of problems that arise in adult life.
但这种方法无法做更广的应用，当你开始遇到成人生活中的那些问题时，就派不上用场了。
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
原因是我试着考虑她的偏好和我的偏好，
and then find perfect solutions -- was just leaving me exhausted.
以找出最完美的解决方案--我真的被搞得疲惫不堪。
She pointed out that I was taking the wrong approach to solving this problem -- and she later became my wife.
她指出我在解决这个问题时用错了方法--后来她成了我的太太。
Whether it's as basic as trying to decide what restaurant to go to
不论是很基本的问题，比如决定要去哪家餐厅吃饭，
or as important as trying to decide who to spend the rest of your life with,
或是很重要的问题，比如决定要和谁共渡余生，
human lives are filled with computational problems that are just too hard to solve by applying sheer effort.
人生其实都充满了计算问题，光靠努力是很难解决的。
For those problems, it's worth consulting the experts: computer scientists.
那些问题值得去咨询专家：计算机科学家。
When you're looking for life advice, computer scientists probably aren't the first people you think to talk to.
当你要寻求人生忠告时，你最先想要问的人大概不会是计算机科学家。
Living life like a computer -- stereotypically deterministic, exhaustive and exact -- doesn't sound like a lot of fun.
把人生过得像计算机一样--刻板的决定论、详尽无遗且精确--听起来实在不好玩。
But thinking about the computer science of human decisions reveals that in fact, we've got this backwards.
但思考一下人类决策的计算机科学，会发现事实上我们把方向弄反了。
When applied to the sorts of difficult problems that arise in human lives,
当应用在人生中的那些困难问题上时，
the way that computers actually solve those problems looks a lot more like the way that people really act.
计算机实际上用来解决那些问题的方式看起来很像是人们真正使用的方式。
Take the example of trying to decide what restaurant to go to.
就用决定要去哪间餐厅吃饭当作例子吧。
This is a problem that has a particular computational structure.
这个问题有特定的计算结构。
You've got a set of options, you're going to choose one of those options,
你有一组选项，你得要从那些选项中择一，
and you're going to face exactly the same decision tomorrow.
并且你明天还会面对完全一样的决策。
In that situation, you run up against what computer scientists call the "explore-exploit trade-off."
在那样的情况下，你碰到的就是计算机科学家所谓的“探索/利用的权衡”。
You have to make a decision about whether you're going to try something new
你得要做一个决策，决定你是否要尝试新选项，
exploring, gathering some information that you might be able to use in the future
去探索、收集一些未来可能会用到的信息，
or whether you're going to go to a place that you already know is pretty good
或者你是否要选择去你已经知道不错的地方，
exploiting the information that you've already gathered so far.
利用你目前已经收集到的信息。
The explore/exploit trade-off shows up any time
探索/利用的权衡会出现在每次
you have to choose between trying something new and going with something that you already know is pretty good,
你必须要从新选项和已经知道不错的选项中择一的情况下，
whether it's listening to music or trying to decide who you're going to spend time with.
也许是听音乐，或者是试着决定你要跟谁一起杀时间。
It's also the problem that technology companies face when they're trying to do something like decide what ad to show on a web page.
这也是科技公司会面临的问题，比如决定要在网页上放什么广告时，遇到的就是这种问题。
Should they show a new ad and learn something about it,
它们应该要刊登新广告，从中得到一些信息吗？
or should they show you an ad that they already know there's a good chance you're going to click on?
或是它们应该要给你看一则它们已经知道你很有可能会点选的广告？
Over the last 60 years, computer scientists have made a lot of progress understanding the explore/exploit trade-off,
在过去六十年，计算机科学家在了解探索/利用的权衡上有相当多进展，
and their results offer some surprising insights.
他们的结果带来了一些让人吃惊的洞见。
When you're trying to decide what restaurant to go to,
当你要试着决定该去哪一间餐厅时，
the first question you should ask yourself is how much longer you're going to be in town.
你应该先问你自己一个问题：你还会待在镇上多久？
If you're just going to be there for a short time, then you should exploit.
如果你只是短暂停留，那么你应该要“利用”。
There's no point gathering information. Just go to a place you already know is good.
收集信息是没有意义的。直接去一个你已经知道不错的地方吧。
But if you're going to be there for a longer time, explore.
但如果你会待久一点，就“探索”吧。
Try something new, because the information you get is something that can improve your choices in the future.
试试新选项，因为你从中得到的信息可能协助你在未来做更好的选择。
The value of information increases the more opportunities you're going to have to use it.
你越有可能用到一项信息，该信息的价值就会增加。
This principle can give us insight into the structure of a human life as well.
这条原则也能协助我们洞察人类的人生。
Babies don't have a reputation for being particularly rational.
宝宝通常不会特别理性。
They're always trying new things, and you know, trying to stick them in their mouths.
他们总是在尝试新东西，你们知道的，总把新东西放到嘴巴里。
But in fact, this is exactly what they should be doing.
但事实上，他们的确应该要这么做。
They're in the explore phase of their lives, and some of those things could turn out to be delicious.
他们正处在人生的探索阶段，他们尝试的东西当中，有些可能真的会很美味。
At the other end of the spectrum,
在光谱的另一端，
the old guy who always goes to the same restaurant and always eats the same thing isn't boring -- he's optimal.
是老人，他们总是去同样的餐厅，总是点同样的食物，并不是无趣，而是优化的选择。
He's exploiting the knowledge that he's earned through a lifetime's experience.
他在利用他从一生的经验中已经得到的知识。
More generally, knowing about the explore/exploit trade-off
更普遍来说，知道有“探索/利用的权衡”，
can make it a little easier for you to sort of relax and go easier on yourself when you're trying to make a decision.
就能让你在做决策时能更轻松些，不要对自己太严厉。
You don't have to go to the best restaurant every night.
你不需要每晚都去最好的餐厅。
Take a chance, try something new, explore. You might learn something.
冒个险，尝试新餐厅，去探索。你可能会学到些什么。
And the information that you gain is going to be worth more than one pretty good dinner.
而你所得到的信息价值绝对胜过一顿好吃的晚餐。
Computer science can also help to make it easier on us in other places at home and in the office.
在家中或在办公室里的其他地方，计算机科学也能够让我们更轻松些。
If you've ever had to tidy up your wardrobe, you've run into a particularly agonizing decision:
如果你得要整理你的衣橱，你会碰到一个特别烦恼的决定：
you have to decide what things you're going to keep and what things you're going to give away.
你得要决定哪些东西该留下，哪些东西该送人。
Martha Stewart turns out to have thought very hard about this -- and she has some good advice.
结果发现玛莎·斯图尔特花了很多功夫在想这件事--她有些不错的忠告。
She says, "Ask yourself four questions: How long have I had it? Does it still function?
她说：“问你自己四个问题：我已经持有它多久了？它还有功能吗？
Is it a duplicate of something that I already own?
它是不是跟某样我已经拥有的东西一样？
And when was the last time I wore it or used it?"
我上次穿它或用它是什么时候？”
But there's another group of experts who perhaps thought even harder about this problem,
但还有另一群专家花了更多功夫在想这个问题，
and they would say one of these questions is more important than the others.
他们会说，这些问题当中有一个比其他的都还重要。
Those experts? The people who design the memory systems of computers.
那些专家是谁？设计出计算机内存系统的人。
Most computers have two kinds of memory systems:
大部分的计算机有两种内存系统：
a fast memory system, like a set of memory chips that has limited capacity,
快速内存系统，就像是一组内存芯片，容量有限，
because those chips are expensive, and a slow memory system, which is much larger.
因为那些芯片很贵，还有慢速内存系统，它的容量大很多。
In order for the computer to operate as efficiently as possible,
为了要让计算机的运作效能尽可能提高，
you want to make sure that the pieces of information you want to access are in the fast memory system,
你会希望能确保你要存取的信息位于快速内存系统中，
so that you can get to them quickly.
这样你就能快速取得它。
Each time you access a piece of information,
每当你存取一项信息时，
it's loaded into the fast memory and the computer has to decide which item it has to remove from that memory,
它就会被加载快速内存中，计算机得要决定要从快速内存中移除哪个项目，
because it has limited capacity.
因为它的容量有限。
Over the years, computer scientists have tried a few different strategies for deciding what to remove from the fast memory.
数年来，计算机科学家试过几种不同的策略来判定该从快速内存中移除什么。
They've tried things like choosing something at random or applying what's called the "first-in, first-out principle,"
他们有试过随机选择的方法，也试过采用“先进先出”的原则，
which means removing the item which has been in the memory for the longest.
也就是说把在内存当中最久的项目给移除。
But the strategy that's most effective focuses on the items which have been least recently used.
不过，最有效的策略，是把目标放在近期最少使用的项目。
This says if you're going to decide to remove something from memory,
这种策略就是，如果你得从内存中移除某样东西，
you should take out the thing which was last accessed the furthest in the past.
你应该选择最后一次使用时间是最久远的那样东西。
And there's a certain kind of logic to this.
这背后是有某种逻辑的。
If it's been a long time since you last accessed that piece of information,
如果你上次存取那项信息已经是很久以前的事了，
it's probably going to be a long time before you're going to need to access it again.
你下次需要存取它的时间应该也会是很久以后。
Your wardrobe is just like the computer's memory.
你的衣橱就像是计算机的内存。
You have limited capacity, and you need to try and get in there the things that you're most likely to need
你的容量有限，你得要把你最有可能用到的东西放进去，
so that you can get to them as quickly as possible.
这样你才能够尽快取得它们。
Recognizing that, maybe it's worth applying the least recently used principle to organizing your wardrobe as well.
认知到这一点后，也许也值得尝试应用“近期最少使用”原则来整理你的衣橱。
So if we go back to Martha's four questions, the computer scientists would say that of these, the last one is the most important.
如果我们回到玛莎的四个问题，计算机科学家会说，在这些问题中，最后一个问题是最重要。
This idea of organizing things so that the things you are most likely to need are most accessible can also be applied in your office.
在整理东西时，要让你最可能需要的东西最容易存取的这个想法，也可以应用到你的办公室中。
The Japanese economist Yukio Noguchi actually invented a filing system that has exactly this property.
日本经济学家野口悠纪雄真的发明了一个具有这种特性的建文件系统。
He started with a cardboard box, and he put his documents into the box from the left-hand side.
他从一个纸箱子开始，他把他的文件从左到右放进箱子中。
Each time he'd add a document, he'd move what was in there along and he'd add that document to the left-hand side of the box.
每当他放入一份文件时，他就得要移动箱中的文件，才能把新放入的文件放入箱子的左边。
And each time he accessed a document, he'd take it out, consult it and put it back in on the left-hand side.
每当他需要使用一份文件时，他会把该文件取出，使用完之后放回到最左边。
As a result, the documents would be ordered from left to right by how recently they had been used.
这样的结果是，文件会从左到右排好，最左边的是最近期使用过的。
And he found he could quickly find what he was looking for by starting at the left-hand side of the box and working his way to the right.
他发现这样排之后，他只要从箱子的左边开始一直向右找，就能快速找到他想找的文件。
Before you dash home and implement this filing system -- it's worth recognizing that you probably already have.
在你们冲回家导入这个建文件系统之前--值得先想想，你可能已经有这个系统了。
That pile of papers on your desk ... typically maligned as messy and disorganized,
你书桌上的那叠纸...通常都被别人诽谤说是乱七八糟，
a pile of papers is, in fact, perfectly organized...
其实是有着完美组织系统的一叠纸...
as long as you, when you take a paper out, put it back on the top of the pile,
只要你每次把一张纸拿出来，用完之后会放回那叠纸的最上方，
then those papers are going to be ordered from top to bottom by how recently they were used,
那么那叠纸从上到下就排好了顺序，最上面的是最近期使用的，
and you can probably quickly find what you're looking for by starting at the top of the pile.
你从那叠纸的最上面开始找，可能就能快速找到你要的。
Organizing your wardrobe or your desk are probably not the most pressing problems in your life.
整理你的衣橱或你的书桌可能不是你人生中最紧迫的问题。
Sometimes the problems we have to solve are simply very, very hard.
有时，我们需要解决的问题就是非常非常难搞。
But even in those cases, computer science can offer some strategies and perhaps some solace.
但即使在那些情况下，计算机科学也能够提供一些策略，也许还能提供一些安慰。
The best algorithms are about doing what makes the most sense in the least amount of time.
最好的算法，就是要在最短的时间内做出最合理的举动。
When computers face hard problems, they deal with them by making them into simpler problems
当计算机面临困难的问题时，它们的处理方式是把那些问题变成更简单的问题，
by making use of randomness, by removing constraints or by allowing approximations.
做法包括使用随机性、移除限制，或是允许近似值。
Solving those simpler problems can give you insight into the harder problems,
解决那些较简单的问题，就能提供你关于原本困难问题的洞见，
and sometimes produces pretty good solutions in their own right.
有时还能自己产生出很好的解决方案。
Knowing all of this has helped me to relax when I have to make decisions.
知道这一切，让我在必须要做决策时能够放轻松。
You could take the 37 percent rule for finding a home as an example.
可以用找房子时的37%规则来当例子。
There's no way that you can consider all of the options, so you have to take a chance.
你不可能把所有的选项都纳入考虑，所以你得要冒险。
And even if you follow the optimal strategy, you're not guaranteed a perfect outcome.
即使你遵循优化策略，也不能保证你会得到最完美的结果。
If you follow the 37 percent rule, the probability that you find the very best place is -- funnily enough ... 37 percent.
如果你遵循37%规则，你能找到最棒的地方的机率是--很有趣...是37%。
You fail most of the time. But that's the best that you can do.
大部分的时候，你会失败。但你能做到最好的就是这样了。
Ultimately, computer science can help to make us more forgiving of our own limitations.
最终，计算机科学会协助让我们更能原谅自己的限制。
You can't control outcomes, just processes.
你不能控制结果，只能控制过程。
And as long as you've used the best process, you've done the best that you can.
只要你已经用了最好的过程，你就已经尽了全力。
Sometimes those best processes involve taking a chance
有时，最好的过程会需要冒点险，
not considering all of your options, or being willing to settle for a pretty good solution.
比如不去考虑所有的选项，或是愿意妥协，接受算是不错的解决方案。
These aren't the concessions that we make when we can't be rational -- they're what being rational means. Thank you.
这些并不是我们在无法理性时所做的让步--它们就是理性的真缔。谢谢大家。
