If there's one city in the world where it's hard to find a place to buy or rent, it's Sydney. And if you've tried to find a home here recently, you're familiar with the problem. Every time you walk into an open house, you get some information about what's out there and what's on the market, but every time you walk out, you're running the risk of the very best place passing you by. So how do you know when to switch from looking to being ready to make an offer?
如果世界上有一個城市 很難找到出售或是出租的地方, 那就是雪梨。 如果你最近試著在這裡找個家, 你對這個問題就會很熟悉。 每當你走進開放看屋的地點, 你就可以得到些資訊, 知道那裡有什麼, 以及市場上有什麼; 但每當你走出來時, 你就冒著錯過最佳選擇的風險。 所以,你怎麼知道 何時要從「看看」切換成 準備好提出交易條件?
This is such a cruel and familiar problem that it might come as a surprise that it has a simple solution. 37 percent.
這是個殘酷又熟悉的問題, 讓人意外的是, 它的解決方案很簡單。 37%。
(Laughter)
(笑聲)
If you want to maximize the probability that you find the very best place, you should look at 37 percent of what's on the market, and then make an offer on the next place you see, which is better than anything that you've seen so far. 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.
如果你想要把找到 最佳選擇的機率提升到最高, 你得要看過市場上 37% 的所有選擇的, 接著到下一個地方時, 就提出交易條件, 它會比你目前看過的 所有選擇都更好。 或者,如果你要花一個月來尋找, 就取那段時間的 37% —— 即 11 天,來設定標準—— 接著你就可以準備行動了。
We know this because trying to find a place to live is an example of an optimal stopping problem. A class of problems that has been studied extensively by mathematicians and computer scientists.
我們知道要這麼做, 是因為試圖找住房 就是「最佳停止問題」的例子。 這類問題一直被數學家 和電腦科學家廣為研究。
I'm a computational cognitive scientist. I spend my time trying to understand how it is that human minds work, from our amazing successes to our dismal failures. To do that, I think about the computational structure of the problems that arise in everyday life, and compare the ideal solutions to those problems to the way that we actually behave. As a side effect, I get to see how applying a little bit of computer science can make human decision-making easier.
我是一位計算認知科學家。 我把時間花在了解 人類大腦如何運作, 從達成了不起的成功 到遭遇令人沮喪的失敗。 要做到這一點,我得要思考 日常問題的計算結構, 並將那些問題的理想解決方案 與我們的真實行為做比較。 它有一個副作用, 我可以看到應用一點點電腦科學 如何能讓人類決策變得更容易。
I have a personal motivation for this. Growing up in Perth as an overly cerebral kid ...
我這麼做,背後有個私人的動機。 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, reasoning through every decision, trying to figure out the very best action to take. 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. At one point, I even tried to break up with my girlfriend because trying to take into account her preferences as well as my own and then find perfect solutions --
我總是試著用我認為 合理的方式來做事, 做每個決策都要依理推論, 試圖找出採取哪種做法最理想。 但這種方法無法做更廣的應用, 當你開始遇到成人 生活中的那些問題時, 就派不上用場了。 我有一度甚至打算要和女友分手, 原因是我試著考量 她的偏好和我的偏好, 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 to solving this problem -- and she later became my wife.
她指出我在解決這個問題時 用錯了方法—— 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 or as important as trying to decide who to spend the rest of your life with, human lives are filled with computational problems that are just too hard to solve by applying sheer effort. For those problems, it's worth consulting the experts: computer scientists.
不論是很基本的問題, 比如決定要去哪家餐廳吃飯, 或是很重要的問題, 比如決定要和誰共渡餘生, 人生其實都充滿了計算問題, 光靠努力是很難解決的。 那些問題 值得去諮詢專家: 電腦科學家。
(Laughter)
(笑聲)
When you're looking for life advice, computer scientists probably aren't the first people you think to talk to. Living life like a computer -- stereotypically deterministic, exhaustive and exact -- doesn't sound like a lot of fun. But thinking about the computer science of human decisions reveals that in fact, we've got this backwards. When applied to the sorts of difficult problems that arise in human lives, the way that computers actually solve those problems looks a lot more like the way that people really act.
當你要尋求人生忠告時, 你最先想要問的人大概 不會是電腦科學家。 把人生過得像電腦一樣—— 刻板的決定論、 詳盡無遺,且精確—— 聽起來實在不好玩。 但思考一下人類決策的電腦科學, 會發現,事實上, 我們把方向弄反了。 當應用在人生中的 那些困難問題上時, 電腦實際上用來解決 那些問題的方式 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. This is a problem that has a particular computational structure. You've got a set of options, you're going to choose one of those options, and you're going to face exactly the same decision tomorrow. In that situation, you run up against what computer scientists call the "explore-exploit trade-off." You have to make a decision about whether you're going to try something new -- exploring, gathering some information that you might be able to use in the future -- or whether you're going to go to a place that you already know is pretty good -- exploiting the information that you've already gathered so far. The explore/exploit trade-off shows up any time you have to choose between trying something new and going with something that you already know is pretty good, whether it's listening to music or trying to decide who you're going to spend time with. 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. Should they show a new ad and learn something about it, or should they show you an ad that they already know there's a good chance you're going to click on?
就用決定要去哪間餐廳 吃飯當作例子吧。 這個問題有特定的計算結構。 你有一組選項, 你得要從那些選項中擇一, 且你明天還會面對 完全一樣的決策。 在那樣的情況下, 你碰到的就是電腦科學家所謂的 「探索/利用的權衡」。 你得要做一個決策, 決定你是否要嘗試新選項—— 去「探索」,收集一些未來 可能會用到的資訊—— 或者你是否要選擇去 你已經知道不錯的地方—— 「利用」你目前已經 收集到的資訊。 探索/利用的權衡會出現在每次 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, 也許是聽音樂, 或者是試著決定 你要跟誰一起殺時間。 這也是科技公司會面臨的問題, 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? 或是它們應該要給你看 一則它們已經知道你很有可能 會點選的廣告?
Over the last 60 years, computer scientists have made a lot of progress understanding the explore/exploit trade-off, and their results offer some surprising insights. When you're trying to decide what restaurant to go to, the first question you should ask yourself is how much longer you're going to be in town. If you're just going to be there for a short time, then you should exploit. There's no point gathering information. Just go to a place you already know is good. But if you're going to be there for a longer time, explore. Try something new, because the information you get is something that can improve your choices in the future. The value of information increases the more opportunities you're going to have to use it.
在過去六十年, 電腦科學家在了解 探索/利用的權衡上, 有相當多進展, 他們的結果帶來了 一些讓人吃驚的洞見。 當你要試著決定該去哪一間餐廳時, 你應該先問你自己一個問題: 你還會待在鎮上多久? 如果你只是短暫停留, 那麼你應該要「利用」。 收集資訊是沒有意義的。 直接去一個你已經 知道不錯的地方吧。 但如果你會待久一點, 就「探索」吧。 試試新選項,因為 你從中得到的資訊 可能協助你在未來做更好的選擇。 你越有可能用到一項資訊, 該資訊的價值就會增加。
This principle can give us insight into the structure of a human life as well. Babies don't have a reputation for being particularly rational. They're always trying new things, and you know, trying to stick them in their mouths. But in fact, this is exactly what they should be doing. They're in the explore phase of their lives, and some of those things could turn out to be delicious. At the other end of the spectrum, the old guy who always goes to the same restaurant and always eats the same thing isn't boring -- he's optimal.
這條原則也能協助我們 洞察人類的人生。 寶寶通常不會特別理性。 他們總是在嘗試新東西, 你們知道的,總把 新東西放到嘴巴裡。 但,事實上,他們 的確應該要這麼做。 他們正處在人生的探索階段, 他們嘗試的東西當中, 有些可能真的會很美味。 在光譜的另一端, 是老人,他們總是去同樣的餐廳, 總是點同樣的食物, 並不是無趣, 而是最佳化的選擇。
(Laughter)
(笑聲)
He's exploiting the knowledge that he's earned through a lifetime's experience. More generally, knowing about the explore/exploit trade-off 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. You don't have to go to the best restaurant every night. Take a chance, try something new, explore. You might learn something. And the information that you gain is going to be worth more than one pretty good dinner.
他在利用他從一生的經驗中 已經得到的知識。 更普遍來說,知道有 「探索/利用的權衡」, 就能讓你在做決策時能更輕鬆些, 不要對自己太嚴厲。 你不需要每晚都去最好的餐廳。 冒個險,嘗試新餐廳,去探索。 你可能會學到些什麼。 而你所得到的資訊 價值絕對勝過一頓好吃的晚餐。
Computer science can also help to make it easier on us in other places at home and in the office. If you've ever had to tidy up your wardrobe, you've run into a particularly agonizing decision: you have to decide what things you're going to keep and what things you're going to give away. Martha Stewart turns out to have thought very hard about this --
在家中或在辦公室裡的其他地方, 電腦科學也能夠讓我們更輕鬆些。 如果你得要整理你的衣櫥, 你會碰到一個特別煩惱的決定: 你得要決定哪些東西該留下, 哪些東西該送人。 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. She says, "Ask yourself four questions: How long have I had it? Does it still function? Is it a duplicate of something that I already own? And when was the last time I wore it or used it?" But there's another group of experts who perhaps thought even harder about this problem, and they would say one of these questions is more important than the others. Those experts? The people who design the memory systems of computers. Most computers have two kinds of memory systems: a fast memory system, like a set of memory chips that has limited capacity, because those chips are expensive, and a slow memory system, which is much larger. In order for the computer to operate as efficiently as possible, you want to make sure that the pieces of information you want to access are in the fast memory system, so that you can get to them quickly. Each time you access a piece of information, it's loaded into the fast memory and the computer has to decide which item it has to remove from that memory, because it has limited capacity.
她有些不錯的忠告。 她說:「問你自己四個問題: 我已經持有它多久了? 它還有功能嗎? 它是不是跟某樣 我已經擁有的東西一樣? 我上次穿它或用它是什麼時候?」 但還有另一群專家 花了更多功夫在想這個問題, 他們會說,這些問題當中 有一個比其他的都還重要。 那些專家是誰? 設計出電腦記憶體系統的人。 大部分的電腦有兩種記憶體系統: 快速記憶體系統, 就像是一組記憶體晶片,容量有限, 因為那些晶片很貴, 還有慢速記憶體系統, 它的容量大很多。 為了要讓電腦的 運作效能盡可能提高, 你會希望能確保你要存取的資訊 位在快速記憶體系統中, 這樣你就能快速取得它。 每當你存取一項資訊時, 它就會被載入快速記憶體中, 電腦得要決定要從 快速記憶體中移除哪個項目, 因為它的容量有限。
Over the years, computer scientists have tried a few different strategies for deciding what to remove from the fast memory. They've tried things like choosing something at random or applying what's called the "first-in, first-out principle," which means removing the item which has been in the memory for the longest. But the strategy that's most effective focuses on the items which have been least recently used. This says if you're going to decide to remove something from memory, you should take out the thing which was last accessed the furthest in the past. And there's a certain kind of logic to this. If it's been a long time since you last accessed that piece of information, it's probably going to be a long time before you're going to need to access it again. Your wardrobe is just like the computer's memory. You have limited capacity, and you need to try and get in there the things that you're most likely to need so that you can get to them as quickly as possible. Recognizing that, maybe it's worth applying the least recently used principle to organizing your wardrobe as well. 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.
數年來,電腦科學家 試過幾種不同的策略 來判定該從快速記憶體中移除什麼。 他們有試過隨機選擇的方法, 也試過採用「先進先出」的原則, 也就是說把在記憶體當中 最久的項目給移除。 不過,最有效的策略, 是把目標放在近期最少使用的項目。 這種策略就是,如果你得 從記憶體中移除某樣東西, 你應該選擇最後一次使用時間 是最久遠的那樣東西。 這背後是有某種邏輯的。 如果你上次存取那項資訊 已經是很久以前的事了, 你下次需要存取它的時間 應該也會是很久以後。 你的衣櫥就像是電腦的記憶體。 你的容量有限, 你得要把你最有可能 用到的東西放進去, 這樣你才能夠盡快取得它們。 認知到這一點後, 也許也值得嘗試應用 「近期最少使用」原則 來整理你的衣櫥。 如果我們回到瑪莎的四個問題, 電腦科學家會說,在這些問題中, 最後一個問題是最重要。
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. The Japanese economist Yukio Noguchi actually invented a filing system that has exactly this property. He started with a cardboard box, and he put his documents into the box from the left-hand side. 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. And each time he accessed a document, he'd take it out, consult it and put it back in on the left-hand side. As a result, the documents would be ordered from left to right by how recently they had been used. 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.
在整理東西時,要讓你最可能 需要的東西最容易存取的這個想法, 也可以應用到你的辦公室中。 日本經濟學家野口悠紀雄 真的發明了一個具有 這種特性的建檔系統。 他從一個紙箱子開始, 他把他的文件 從左到右放進箱子中。 每當他放入一份文件時, 他就得要移動箱中的文件, 才能把新放入的文件 放入箱子的左邊。 每當他需要使用一份文件時, 他會把該文件取出, 使用完之後放回到最左邊。 這樣的結果是, 文件會從左到右排好, 最左邊的是最近期使用過的。 他發現這樣排之後, 他只要從箱子的左邊開始 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 --
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 ... typically maligned as messy and disorganized, a pile of papers is, in fact, perfectly organized --
你書桌上的那疊紙…… 通常都被別人誹謗說是亂七八糟, 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, put it back on the top of the pile, then those papers are going to be ordered from top to bottom by how recently they were used, and you can probably quickly find what you're looking for by starting at the top of the pile.
只要你每次把一張紙拿出來, 用完之後會放回那疊紙的最上方, 那麼那疊紙從上到下 就排好了順序, 最上面的是最近期使用的, 你從那疊紙的最上面開始找, 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. Sometimes the problems we have to solve are simply very, very hard. But even in those cases, computer science can offer some strategies and perhaps some solace. The best algorithms are about doing what makes the most sense in the least amount of time. When computers face hard problems, they deal with them by making them into simpler problems -- by making use of randomness, by removing constraints or by allowing approximations. Solving those simpler problems can give you insight into the harder problems, and sometimes produces pretty good solutions in their own right.
整理你的衣櫥或你的書桌 可能不是你人生中最緊迫的問題。 有時,我們需要解決的問題 就是非常非常難搞。 但即使在那些情況下, 電腦科學也能夠提供一些策略, 也許還能提供一些安慰。 最好的演算法, 就是要在最短的時間內 做出最合理的舉動。 當電腦面臨困難的問題時, 它們的處理方式是把那些問題 變成更簡單的問題—— 做法包括使用隨機性、 移除限制式,或是允許近似值。 解決那些較簡單的問題, 就能提供你關於 原本困難問題的洞見, 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. You could take the 37 percent rule for finding a home as an example. There's no way that you can consider all of the options, so you have to take a chance. And even if you follow the optimal strategy, you're not guaranteed a perfect outcome. If you follow the 37 percent rule, the probability that you find the very best place is -- funnily enough ...
知道這一切,讓我在 必須要做決策時能夠放輕鬆。 可以用找房子時的 37% 規則來當例子。 你不可能把所有的 選項都納入考量, 所以你得要冒險。 即使你遵循最佳化策略, 也不能保證你會得到最完美的結果。 如果你遵循 37% 規則, 你能找到最棒的地方的機率是—— 很有趣……
(Laughter)
(笑聲)
37 percent. You fail most of the time. But that's the best that you can do.
是 37%。 大部分的時候,你會失敗。 但你能做到最好的就是這樣了。
Ultimately, computer science can help to make us more forgiving of our own limitations. You can't control outcomes, just processes. And as long as you've used the best process, you've done the best that you can. Sometimes those best processes involve taking a chance -- not considering all of your options, or being willing to settle for a pretty good solution. These aren't the concessions that we make when we can't be rational -- they're what being rational means.
最終,電腦科學會協助讓我們 更能原諒自己的限制。 你不能控制結果,只能控制過程。 只要你已經用了最好的過程, 你就已經盡了全力。 有時,最好的過程會需要冒點險—— 比如不去考量所有的選項, 或是願意妥協,接受 算是不錯的解決方案。 這些並不是我們在無法 理性時所做的讓步—— 它們就是理性的真締。
Thank you.
謝謝大家。
(Applause)
(掌聲)