I love video games. I'm also slightly in awe of them. I'm in awe of their power in terms of imagination, in terms of technology, in terms of concept. But I think, above all, I'm in awe at their power to motivate, to compel us, to transfix us, like really nothing else we've ever invented has quite done before. And I think that we can learn some pretty amazing things by looking at how we do this. And in particular, I think we can learn things about learning. Now the video games industry is far and away the fastest growing of all modern media. From about 10 billion in 1990, it's worth 50 billion dollars globally today, and it shows no sign of slowing down. In four years' time, it's estimated it'll be worth over 80 billion dollars. That's about three times the recorded music industry. This is pretty stunning, but I don't think it's the most telling statistic of all. The thing that really amazes me is that, today, people spend about eight billion real dollars a year buying virtual items that only exist inside video games. This is a screenshot from the virtual game world, Entropia Universe. Earlier this year, a virtual asteroid in it sold for 330,000 real dollars. And this is a Titan class ship in the space game, EVE Online. And this virtual object takes 200 real people about 56 days of real time to build, plus countless thousands of hours of effort before that. And yet, many of these get built. At the other end of the scale, the game Farmville that you may well have heard of, has 70 million players around the world and most of these players are playing it almost every day.
我熱愛電子遊戲。 我還有點小小地敬畏它們。 我敬畏它們在 想像力,技術 和概念方面的力量。 但是,最重要的, 我敬畏它們能夠 促使我們,強迫我們, 讓我們目瞪口呆, 這是人類其它發明 所不能企及的。 而且我認為我們能從中瞭解到很多驚人的事實, 就是看看我們是如何玩電子遊戲的。 特別是可以瞭解到 關於人的認知。 目前電子遊戲產業 發展之快遠遠超越了 其他現代媒體。 從1990年的一百億 到今天的全球產值五百億。 而且完全沒有放緩的跡象。 預計在未來的四年, 將超過八百億美圓。 這是唱片業的三倍。 相當驚人的數字, 但我認為這還不是最說明問題的數據。 真正讓我驚訝的是 現在 人們可以 一年花實實在在的八百億 購買虛擬的iTunes 只存在於 電子遊戲裡。 這是一個虛擬的遊戲世界《Entropia Universe》的遊戲截屏。 就在前不久, 這個遊戲中的一個虛擬的小行星 竟以三十三萬美圓的價格售出。 而這個 是一艘泰坦級的宇宙飛船 來自EVE Online 這個太空遊戲。 而這艘虛擬的飛船 需要200個真人 花費56天建造出來, 還要加上不知幾千小時的 前期工作。 類似這樣被造出的還有很多。 而另一方面, Farmville這個遊戲,可能你們已經聽說了, 有七千萬個玩家 遍佈全世界, 而且這些玩家中的大多數 幾乎每天都在玩。
This may all sound really quite alarming to some people, an index of something worrying or wrong in society. But we're here for the good news, and the good news is that I think we can explore why this very real human effort, this very intense generation of value, is occurring. And by answering that question, I think we can take something extremely powerful away. And I think the most interesting way to think about how all this is going on is in terms of rewards. And specifically, it's in terms of the very intense emotional rewards that playing games offers to people both individually and collectively. Now if we look at what's going on in someone's head when they are being engaged, two quite different processes are occurring. On the one hand, there's the wanting processes. This is a bit like ambition and drive -- I'm going to do that. I'm going to work hard. On the other hand, there's the liking processes, fun and affection and delight and an enormous flying beast with an orc on the back. It's a really great image. It's pretty cool. It's from the game World of Warcraft with more than 10 million players globally, one of whom is me, another of whom is my wife. And this kind of a world, this vast flying beast you can ride around, shows why games are so very good at doing both the wanting and the liking. Because it's very powerful. It's pretty awesome. It gives you great powers. Your ambition is satisfied, but it's very beautiful. It's a very great pleasure to fly around. And so these combine to form a very intense emotional engagement.
可能這聽上去 會令一些人相當警惕, 覺得是社會上那些令人焦慮 或不正確的現象。 但是我們來這是聽好消息的, 好消息就是 我認為我們能夠研究一下 爲什麽這種真實的人類勞動, 這麼巨大的價值的創造會得以出現。 通過回答這個問題, 我覺得我們可以從中得到 極其強大的信息。 我認為最有趣的 思考這些問題的角度 就是獎賞。 更具體來說, 就是非常密集的情感獎賞, 通過玩遊戲提供給人們, 既是個人的, 也有集體的。 如果我們觀察一下某人的大腦, 當他們忙碌時是怎樣運作的, 兩個相當不同的進程同時發生著。 一方面是想要的進程。 有些類似進取心和動機——我要做那件事。我要努力工作。 而另一方面是喜歡的進程。 樂趣和喜愛 以及快樂—— 這是一個巨型飛行獸,上頭騎著一個獸人。 這幅圖很棒,很酷。 它來自魔獸世界,全球的玩家超過一千萬, 其中一個就是我,另外一個就是我老婆。 在這種世界裡 你可以騎著這種巨型的飛行獸到處閒逛, 而這正顯示出爲什麽遊戲是多麼善於 讓人同時做要做和喜歡做的事。 因為這很強大,相當厲害。 它給予你強大的力量。 你的野心得到滿足,但又非常美麗。 飛來飛去帶來絕大的快感。 所有這些組合起來形成 非常巨大的情感投入。
But this isn't the really interesting stuff. The really interesting stuff about virtuality is what you can measure with it. Because what you can measure in virtuality is everything. Every single thing that every single person who's ever played in a game has ever done can be measured. The biggest games in the world today are measuring more than one billion points of data about their players, about what everybody does -- far more detail than you'd ever get from any website. And this allows something very special to happen in games. It's something called the reward schedule. And by this, I mean looking at what millions upon millions of people have done and carefully calibrating the rate, the nature, the type, the intensity of rewards in games to keep them engaged over staggering amounts of time and effort. Now, to try and explain this in sort of real terms, I want to talk about a kind of task that might fall to you in so many games. Go and get a certain amount of a certain little game-y item. Let's say, for the sake of argument, my mission is to get 15 pies and I can get 15 pies by killing these cute, little monsters. Simple game quest. Now you can think about this, if you like, as a problem about boxes. I've got to keep opening boxes. I don't know what's inside them until I open them. And I go around opening box after box until I've got 15 pies. Now, if you take a game like Warcraft, you can think about it, if you like, as a great box-opening effort. The game's just trying to get people to open about a million boxes, getting better and better stuff in them.
但這還不是真正有趣的部份。 虛擬世界真正有趣的地方在於 你從中可以量度的東西。 因為你在虛擬世界中能度量的東西 就是最重要的東西。 每一個人在遊戲中做的每一件事 都可被度量。 今天世界上最大型的遊戲 正在量度玩家的上十億的數據 具體到每個人做的事—— 其細緻程度超過任何其他網站。 而這就使得一些非常特別的東西可以 存在於遊戲中。 這就是獎賞機制。 通過這個機制, 觀察成百萬上千萬的人是怎麼玩的, 然後仔細校準比率, 屬性,類型,以及遊戲中獎賞的強度 令人持續投入 數量驚人的時間和努力。 現在為了試圖用一些實際的概念 來闡釋這個機制, 我要討論一種任務 就是你在很多遊戲中會遇到的那種任務。 去找到一定數量的某種遊戲小道具。 比如說, 我的任務是得到15個餡餅, 然後為了這15個餡餅 我要殺死這些可愛的小怪物。 很簡單的遊戲任務。 現在如果你喜歡可以把這個想像為 一個關於盒子的問題。 我需要不斷打開盒子。 我不知道裡頭有什麽,直到我打開它們。 然後我四處去打開一個又一個盒子,直到得到15個餡餅。 現在如果你在玩的是魔獸世界這樣的遊戲, 如果你願意可以把它想像為 一個繁重的開盒子的勞動。 遊戲想讓人去打開大約一百萬個盒子, 從裡頭找到越來越好的東西。
This sounds immensely boring but games are able to make this process incredibly compelling. And the way they do this is through a combination of probability and data. Let's think about probability. If we want to engage someone in the process of opening boxes to try and find pies, we want to make sure it's neither too easy, nor too difficult, to find a pie. So what do you do? Well, you look at a million people -- no, 100 million people, 100 million box openers -- and you work out, if you make the pie rate about 25 percent -- that's neither too frustrating, nor too easy. It keeps people engaged. But of course, that's not all you do -- there's 15 pies. Now, I could make a game called Piecraft, where all you had to do was get a million pies or a thousand pies. That would be very boring. Fifteen is a pretty optimal number. You find that -- you know, between five and 20 is about the right number for keeping people going. But we don't just have pies in the boxes. There's 100 percent up here. And what we do is make sure that every time a box is opened, there's something in it, some little reward that keeps people progressing and engaged. In most adventure games, it's a little bit in-game currency, a little bit experience. But we don't just do that either.
聽上去是極度枯燥, 但遊戲卻能夠 使得這個過程 極其吸引人。 而它們所使用的方法 就是把概率和數據結合起來。 讓我們來想想概率問題。 如果我們想讓人去 打開盒子尋找餡餅, 我們想確保它不要太容易, 也不能太困難。 那該怎麼辦?那麼你觀察一百萬個人—— 不,一億個人,一億個開盒子的人—— 然後來計算一下,如果你設定餡餅出現的比率 大約為25%—— 這樣不會太令人挫敗,也不會太容易; 這樣就能讓人投入進去—— 當然,這還不是全部——這只是15個餡餅。 現在,我可以做一個遊戲叫做餡餅世界, 你在這裡要做的就是找到一百萬個餡餅, 或一千個。 這個遊戲會很無聊。 15是一個最優化的數字。 你要尋找的,——你知道,在5到20之間, 這是讓人願意玩下去的一個恰到好處的數量。 但我們在盒子里找到的不只是餡餅。 這點我敢百分百肯定。 我們所做的就是要確保每次盒子一打開, 裡頭總有點什麽,一些小小的獎勵, 就是這些東西令人投入地玩下去。 在大部份的冒險遊戲裡, 這獎賞會是一點遊戲幣,一點經驗值, 但我們也不是僅僅為了這個才玩。
We also say there's going to be loads of other items of varying qualities and levels of excitement. There's going to be a 10 percent chance you get a pretty good item. There's going to be a 0.1 percent chance you get an absolutely awesome item. And each of these rewards is carefully calibrated to the item. And also, we say, "Well, how many monsters? Should I have the entire world full of a billion monsters?" No, we want one or two monsters on the screen at any one time. So I'm drawn on. It's not too easy, not too difficult. So all this is very powerful. But we're in virtuality. These aren't real boxes. So we can do some rather amazing things. We notice, looking at all these people opening boxes, that when people get to about 13 out of 15 pies, their perception shifts, they start to get a bit bored, a bit testy. They're not rational about probability. They think this game is unfair. It's not giving me my last two pies. I'm going to give up. If they're real boxes, there's not much we can do, but in a game we can just say, "Right, well. When you get to 13 pies, you've got 75 percent chance of getting a pie now." Keep you engaged. Look at what people do -- adjust the world to match their expectation. Our games don't always do this. And one thing they certainly do at the moment is if you got a 0.1 percent awesome item, they make very sure another one doesn't appear for a certain length of time to keep the value, to keep it special.
可以說裡頭還有一些其他道具 帶著不同的內容和不同級別的興奮感。 大約有十分之一的機會你可能得到一個相當好的道具。 而有大概千分之一的機會 會得到一件絕對厲害的道具。 而所有這些獎賞都小心地與道具調整在一起。 而且,我們還會說, “好,放多少鬼怪呢?我是不是應該讓整個世界充滿十億個鬼怪?” 不,我們只想讓一到兩隻鬼怪同時出現在屏幕上。 於是我就被吸引住了。這不太容易,也不太難。 加在一起就很強大了。 但是我們是在虛擬世界;這些都不是真的盒子。 所以我們還可以做一些 更加令人驚奇的事。 在觀察所有這些人打開盒子時,我們注意到, 當人們拿到15個餡餅中的13個時, 他們的注意力發生轉移,他們開始覺得有點無聊,開始急躁。 他們并沒有理性理解概率。 他們認為這個遊戲不公平。 它沒給我最後兩個餡餅。我快要放棄了。 如果要找的是真正的盒子,那到這裡我們就無能為力了, 但是在遊戲裡,我們只需說,“好吧,這樣。” 當你拿到13個餡餅時,現在你拿到餡餅的機會提高到75%。 這樣就會令你繼續玩下去。觀察人們如何玩遊戲—— 調整這個世界符合他們的期待。 而我們的遊戲并不總是如此。 目前有一件事它們肯定會做的就是 如果你拿到那個千分之一機會才能得到的道具, 它們會確保另一個這樣的道具在相當長一段時間內不會出現 以此令其保值,讓它特殊。
And the point is really that we evolved to be satisfied by the world in particular ways. Over tens and hundreds of thousands of years, we evolved to find certain things stimulating, and as very intelligent, civilized beings, we're enormously stimulated by problem solving and learning. But now, we can reverse engineer that and build worlds that expressly tick our evolutionary boxes. So what does all this mean in practice? Well, I've come up with seven things that, I think, show how you can take these lessons from games and use them outside of games. The first one is very simple: experience bars measuring progress -- something that's been talked about brilliantly by people like Jesse Schell earlier this year. It's already been done at the University of Indiana in the States, among other places. It's the simple idea that instead of grading people incrementally in little bits and pieces, you give them one profile character avatar which is constantly progressing in tiny, tiny, tiny little increments which they feel are their own. And everything comes towards that, and they watch it creeping up, and they own that as it goes along.
而關鍵就在於 我們適應了以某種特定的方式 從周圍的世界獲得滿足感。 通過幾百萬年, 我們演化成尋找某種刺激性的事物, 並且作為非常智能和文明化的生物, 我們通過解決問題和學習知識獲得巨大的刺激。 但是現在,我們能反向設計這一行為 構造出遊戲世界 很明顯地突出我們的演化特徵。 那麼所有這些在實踐中有什麽意義? 我總結出 七個要點 我認為表明了 你如何從遊戲中有所學習 并將它們應用到遊戲以外。 第一點很簡單: 用經驗值條量度進程—— 有人已經很出色地討論過這個問題 如今年年初時的Jesse Schell 。 在美國的印第安那大學和其他一些地方已經這樣去做了。 很簡單的道理就是,不用增量的方式給人打分, 不要去算計那些點點滴滴, 你給他們一個角色化身 這個化身會持續地發展 一點一點地,以非常微弱的量發展,他們會感同身受。 然後一切都朝向那個目標前進, 他們會看著它不斷增長,然後隨著它的發展他們對之認同。
Second, multiple long and short-term aims -- 5,000 pies, boring, 15 pies, interesting. So, you give people lots and lots of different tasks. You say, it's about doing 10 of these questions, but another task is turning up to 20 classes on time, but another task is collaborating with other people, another task is showing you're working five times, another task is hitting this particular target. You break things down into these calibrated slices that people can choose and do in parallel to keep them engaged and that you can use to point them towards individually beneficial activities.
第二,多進程的長短期目標—— 五千個餡餅,太煩了, 十五個,有意思。 因此你要給人們 很多很多不同的任務。 你要說,這是 解決10個這樣的問題, 而另一個任務 是在規定時間內升20級, 但再另外一個任務是和別人合作, 再另一個任務是展示你的工作五次, 再一個任務是擊中這個特定的標靶。 你把任務拆分成這些經過調校的小塊, 人們可以挑選,以及並行處理 以令他們保持投入 并將它們和 個人的獲利行為掛鉤。
Third, you reward effort. It's your 100 percent factor. Games are brilliant at this. Every time you do something, you get credit; you get a credit for trying. You don't punish failure. You reward every little bit of effort -- a little bit of gold, a little bit of credit. You've done 20 questions -- tick. It all feeds in as minute reinforcement.
第三,獎賞努力工作。 這是你的萬靈丹。遊戲在這點上極其擅長。 每次你做點什麽事時,你都得到分數,從嘗試中得分。 你不會懲罰失敗;你會獎勵每一點微小的努力—— 一小塊金子,一小點分數——你已經做完了20個問題了——完成。 這些都是通過小小的鼓勵實現的。
Fourth, feedback. This is absolutely crucial, and virtuality is dazzling at delivering this. If you look at some of the most intractable problems in the world today that we've been hearing amazing things about, it's very, very hard for people to learn if they cannot link consequences to actions. Pollution, global warming, these things -- the consequences are distant in time and space. It's very hard to learn, to feel a lesson. But if you can model things for people, if you can give things to people that they can manipulate and play with and where the feedback comes, then they can learn a lesson, they can see, they can move on, they can understand.
第四,反饋。 這絕對是個關鍵, 而虛擬世界為實現這一點做的讓人眼花繚亂。 如果你看那些當今世界上最難解決的一些問題, 關於這些問題我們已經聽到很多驚人的東西, 人們很難有所長進 如果他們無法將結果與行為聯繫起來。 污染,全球暖化,這些問題, 其後果從時間空間上看都還很遙遠。 結果就很難學到,感受到其中的教訓。 但如果你可以給人們一些這類事情的模型, 如果你可以給一些東西他們可以操控 玩耍并從中獲得反饋, 那麼他們就能從中有所學習,他們就能看到, 他們就能進步,能理解。
And fifth, the element of uncertainty. Now this is the neurological goldmine, if you like, because a known reward excites people, but what really gets them going is the uncertain reward, the reward pitched at the right level of uncertainty, that they didn't quite know whether they were going to get it or not. The 25 percent. This lights the brain up. And if you think about using this in testing, in just introducing control elements of randomness in all forms of testing and training, you can transform the levels of people's engagement by tapping into this very powerful evolutionary mechanism. When we don't quite predict something perfectly, we get really excited about it. We just want to go back and find out more.
第五, 不確定性因素。 目前這是神經科學的寶庫, 你可以這麼說, 因為一個已知的獎勵 會讓人們興奮, 但真正驅動他們的 是不確定的獎勵, 帶著適當程度的不確定性的獎勵, 也就是說人們不太知道是否能得到。 四分之一的概率。這就能使大腦興奮。 如果你想 把這點用於測試, 就只需引入隨機性的控制因素 放在各種形式的測試和訓練中, 你能夠改變人們的投入程度 通過引入這種非常強大的 演化機制。 當我們無法相當完美地預測某事時, 對它就會特別興奮。 我們就想回去發現更多。
As you probably know, the neurotransmitter associated with learning is called dopamine. It's associated with reward-seeking behavior. And something very exciting is just beginning to happen in places like the University of Bristol in the U.K., where we are beginning to be able to model mathematically dopamine levels in the brain. And what this means is we can predict learning, we can predict enhanced engagement, these windows, these windows of time, in which the learning is taking place at an enhanced level. And two things really flow from this. The first has to do with memory, that we can find these moments. When someone is more likely to remember, we can give them a nugget in a window. And the second thing is confidence, that we can see how game-playing and reward structures make people braver, make them more willing to take risks, more willing to take on difficulty, harder to discourage. This can all seem very sinister. But you know, sort of "our brains have been manipulated; we're all addicts." The word "addiction" is thrown around. There are real concerns there. But the biggest neurological turn-on for people is other people. This is what really excites us. In reward terms, it's not money; it's not being given cash -- that's nice -- it's doing stuff with our peers, watching us, collaborating with us.
你可能知道,神經遞質 伴隨學習產生的神經遞質叫做多巴胺。 它出現在尋找獎勵的行為中。 一些激動人心的工作正在 展開,如英國的布裡斯托爾大學, 在那裡我們開始能夠用數學的方式 建構大腦中多巴胺水平的模型。 這意味著我們可以預測學習, 我們可以預測加強的行為, 這些機會期,這些時間的機會期, 其中所發生的學習行為處在一個加強的水平。 從中產生兩個結果。 第一與記憶有關, 就是我們可以找到這些瞬間。 當某人想記住什麽時, 我們可以給他們提供機會期這一寶貴資源。 第二就是信心, 我們能看到遊戲的操作和獎賞結構是如何 令人更勇敢,令人更樂於冒險, 更願意面對困難 更不容易灰心。 這些可以是些不好的跡象。 但是你知道,有人會說“我們的大腦都被控制了,我們都是癮君子。” “上癮”這個詞到處可見。 這的確是個問題。 但是對人來說,最大的神經刺激 來自他人。 這才是真正令我們興奮的。 就獎賞來說,并不是金錢, 並不是得到現金——當然那也不錯—— 而是和同伴一起做事, 注視我們,和我們合作。
And I want to tell you a quick story about 1999 -- a video game called EverQuest. And in this video game, there were two really big dragons, and you had to team up to kill them -- 42 people, up to 42 to kill these big dragons. That's a problem because they dropped two or three decent items. So players addressed this problem by spontaneously coming up with a system to motivate each other, fairly and transparently. What happened was, they paid each other a virtual currency they called "dragon kill points." And every time you turned up to go on a mission, you got paid in dragon kill points. They tracked these on a separate website. So they tracked their own private currency, and then players could bid afterwards for cool items they wanted -- all organized by the players themselves. Now the staggering system, not just that this worked in EverQuest, but that today, a decade on, every single video game in the world with this kind of task uses a version of this system -- tens of millions of people. And the success rate is at close to 100 percent. This is a player-developed, self-enforcing, voluntary currency, and it's incredibly sophisticated player behavior.
我想很快地講一個小故事,1999年 有個電子遊戲叫做《無盡任務》。 在這個遊戲裡, 有兩頭巨大的龍,你必須組隊才能殺掉它們—— 42個人——必須要42個人才能殺掉巨龍。 這是個問題, 因為這些龍會丟出兩三個重要的道具。 於是玩家處理這個問題的方法是 自發地建立起一套體系 來激勵每個玩家, 公平地,透明地。 結果,他們付給每個玩家虛擬貨幣 他們稱之為殺龍點數。 每次出發去完成一個任務 都會得到一些殺龍點數。 他們用另一個獨立的網站記錄這些點數。 這樣就可以記錄自己的貨幣, 之後玩家就可以用來競拍 他們想要的厲害道具—— 這些都是玩家自己組織起來的。 目前這個令人難以置信的系統不僅出現在《無限任務》 而是今天,十年以後, 世界上的每一款有這類任務的電子遊戲 都在使用某個版本的這個系統—— 上千萬的人。 而成功率 接近百分之百。 這是一個玩家開發的, 自動實施的,自願的貨幣, 這就是玩家複雜到令人無法相信的 玩家行為。
And I just want to end by suggesting a few ways in which these principles could fan out into the world. Let's start with business. I mean, we're beginning to see some of the big problems around something like business are recycling and energy conservation. We're beginning to see the emergence of wonderful technologies like real-time energy meters. And I just look at this, and I think, yes, we could take that so much further by allowing people to set targets by setting calibrated targets, by using elements of uncertainty, by using these multiple targets, by using a grand, underlying reward and incentive system, by setting people up to collaborate in terms of groups, in terms of streets to collaborate and compete, to use these very sophisticated group and motivational mechanics we see. In terms of education, perhaps most obviously of all, we can transform how we engage people. We can offer people the grand continuity of experience and personal investment. We can break things down into highly calibrated small tasks. We can use calculated randomness. We can reward effort consistently as everything fields together. And we can use the kind of group behaviors that we see evolving when people are at play together, these really quite unprecedentedly complex cooperative mechanisms. Government, well, one thing that comes to mind is the U.S. government, among others, is literally starting to pay people to lose weight. So we're seeing financial reward being used to tackle the great issue of obesity. But again, those rewards could be calibrated so precisely if we were able to use the vast expertise of gaming systems to just jack up that appeal, to take the data, to take the observations, of millions of human hours and plow that feedback into increasing engagement.
最後我想建議 一些方法使這些原則 可以擴散到全世界。 首先是商業。 我認為我們將會看到一些非常巨大的問題 出現在諸如商業裏面, 循環利用和節約能源。 我們將會看到一些很奇妙的技術出現 如實時的能量計。 看著這些,我會想,對啊, 我們可以更充分地使用這些技術 讓人們設定目標 通過設定標準化的目標, 通過使用不確定性因素, 通過多任務進程, 通過使用一個巨大的,潛在的獎賞和激勵機制, 來激發人們 以團體和街區的形式合作, 既合作又競爭, 利用這些非常複雜的 組織和激勵機制。 在教育方面, 可能是最顯著的, 我們能改變吸引人注意的方式。 我們可以提供給人們愉快的連續的 經驗和個人的發展。 我們可以把事務拆分為 高度調整過的小任務。 我們可以利用計算過的隨機性。 我們可以持續地獎勵努力 調動所有方面。 我們還能利用這種團隊行為 也就是當人們一起玩遊戲時看到的演化, 這些真是前所未有的複雜的 協作機制。 我想到的另一個就是政府, 尤其是美國政府 已經真的開始付錢給民眾 去減肥。 所以我們所說的就是利用經濟獎賞 去解決肥胖這個大問題。 但是同樣,這些獎勵 可以被精確地分配 如果我們能夠使用遊戲系統的大量專業技術 去提升吸引力, 去採集數據,觀察, 上百萬的人小時 并將這些反饋用回到 提升人的參與度。
And in the end, it's this word, "engagement," that I want to leave you with. It's about how individual engagement can be transformed by the psychological and the neurological lessons we can learn from watching people that are playing games. But it's also about collective engagement and about the unprecedented laboratory for observing what makes people tick and work and play and engage on a grand scale in games. And if we can look at these things and learn from them and see how to turn them outwards, then I really think we have something quite revolutionary on our hands.
最後,就是這個詞,參與度, 我想留給大家。 就是如何使個人的參與 可以發生轉化, 通過心理學和神經學方面的經驗 就是我們從觀察人玩遊戲獲得的經驗。 但是還有集體的參與度 以及前所未有的實驗 觀察是什麽使人行動 工作,遊戲和投入 大量精力到遊戲中。 如果我們觀察這些并從中有所學習 并看到如何將它們應用到遊戲以外, 那麼我真的認為我們正在做的是具有革新意義的事情。
Thank you very much.
非常感謝。
(Applause)
(觀眾掌聲)